"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
}
- ],
- "metadata": {
- "accelerator": "GPU",
- "colab": {
- "gpuType": "L4",
- "provenance": []
- },
- "kernelspec": {
- "display_name": "qmcpy",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.13"
- }
+ ],
+ "source": [
+ "nrows = len(ds)\n",
+ "ncols = len(kinds_func)*len(kinds_coeff)\n",
+ "print('logging')\n",
+ "fig,ax = pyplot.subplots(nrows=nrows,ncols=ncols,figsize=(ncols*5,nrows*5),sharey=True,sharex=True)\n",
+ "ax = ax.reshape(nrows,ncols)\n",
+ "colors = pyplot.rcParams['axes.prop_cycle'].by_key()['color'] + [\"indigo\"]\n",
+ "for v,(name,x_full) in enumerate(pts.items()):\n",
+ " print('%20s d: '%name,end='',flush=True)\n",
+ " for j,d in enumerate(ds):\n",
+ " print('%d, '%d,end='',flush=True)\n",
+ " for i1,kind_func in enumerate(kinds_func):\n",
+ " for i2,kind_coeff in enumerate(kinds_coeff):\n",
+ " i = len(kinds_coeff)*i1+i2\n",
+ " tag = '%s.%d'%(kind_func,kind_coeff)\n",
+ " genz = Genz(IIDStdUniform(d),kind_func=kind_func,kind_coeff=kind_coeff)\n",
+ " y_full = genz.f(x_full[:,:d])\n",
+ " mu_hats = array([y_full[:n].mean() for n in ns],dtype=float)\n",
+ " error = abs(mu_hats-ref_sols.loc[d,tag])\n",
+ " ax[j,i].plot(ns,error,label=name, color=colors[v])\n",
+ " if v==(len(pts)-1): ax[j,i].legend(loc='lower left')\n",
+ " if v>0: continue\n",
+ " ax[j,i].set_xscale('log',base=2)\n",
+ " ax[j,i].set_yscale('log',base=10)\n",
+ " if i==0: ax[j,i].set_ylabel(r'$d=%d$\\\\$\\varepsilon = \\lvert \\mu - \\hat{\\mu} \\rvert$'%d)\n",
+ " if j==0: ax[j,i].set_title(tag)\n",
+ " if j==(len(ds)-1):\n",
+ " ax[j,i].set_xlabel(r'$n$')\n",
+ " ax[j,i].set_xticks(ns)\n",
+ " ax[j,i].set_xlim([ns.min(),ns.max()])\n",
+ " print()"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "L4",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "qmcpy",
+ "language": "python",
+ "name": "python3"
},
- "nbformat": 4,
- "nbformat_minor": 5
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/demos/GBM/gbm_demo.ipynb b/demos/GBM/gbm_demo.ipynb
index bdbd527fe..3ba95fe63 100644
--- a/demos/GBM/gbm_demo.ipynb
+++ b/demos/GBM/gbm_demo.ipynb
@@ -7,13 +7,6 @@
"# Geometric Brownian Motion Demo"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/gbm_demo.ipynb)"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -32,6 +25,40 @@
"- Random seeds: 42 (QMCPy), 7 (QuantLib)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/GBM/gbm_demo.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " import sys\n",
+ " import os\n",
+ " repo_root = \"/content/QMCSoftware\"\n",
+ " notebook_dir = f\"{repo_root}/demos/GBM\"\n",
+ " if not os.path.isdir(repo_root):\n",
+ " !git clone -q --depth 1 https://github.com/QMCSoftware/QMCSoftware {repo_root}\n",
+ " !pip install -q qmcpy\n",
+ " !pip install -q ipywidgets QuantLib\n",
+ " os.chdir(notebook_dir)\n",
+ " if notebook_dir not in sys.path:\n",
+ " sys.path.insert(0, notebook_dir)\n",
+ " extra_path = f\"{repo_root}/demos/GBM/gbm_code\"\n",
+ " if extra_path not in sys.path:\n",
+ " sys.path.insert(0, extra_path)\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
@@ -409,7 +436,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now, using `plot_gbm_paths`, we generate 32 GBM paths to model stock price, $S(t)$, with initial value $S_0$ = 50, drift coeffient, $\\mu = 0.1$, diffusion coefficient $\\sigma = 0.2$ using IID points."
+ "Now, using `plot_gbm_paths`, we generate 32 GBM paths to model stock price, $S(t)$, with initial value $S_0$ = 50, drift coefficient, $\\mu = 0.1$, diffusion coefficient $\\sigma = 0.2$ using IID points."
]
},
{
@@ -436,7 +463,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## GBM Using Low-Discrepancy Lattice Sequence Distrubtion "
+ "## GBM Using Low-Discrepancy Lattice Sequence Distribution "
]
},
{
@@ -1203,7 +1230,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "To compare how mean absolute error (MAE) differs between QMCPy and QuantLib samplers, we compute MAEs averaged across several independant replications. In QMCPy, samplers have a `replications` parameter, which specifies the number of independent randomizations of the underlying point set. This allows us to generate multiple independent sets of paths in a single call.\n",
+ "To compare how mean absolute error (MAE) differs between QMCPy and QuantLib samplers, we compute MAEs averaged across several independent replications. In QMCPy, samplers have a `replications` parameter, which specifies the number of independent randomizations of the underlying point set. This allows us to generate multiple independent sets of paths in a single call.\n",
"QuantLib does not have a built-in `replications` parameter so we run path generation function multiple times with different seeds. \n",
"\n",
"For each replication, we compute the absolute error between the theoretical mean and the empirical mean of the simulated paths at the final time (maturity), and then average these errors to obtain the MAE values shown in the *Mean Absolute Error Comparison* subplot below.\n",
diff --git a/demos/GBM/gbm_examples.ipynb b/demos/GBM/gbm_examples.ipynb
index 20b156f75..f8f2b5bd4 100644
--- a/demos/GBM/gbm_examples.ipynb
+++ b/demos/GBM/gbm_examples.ipynb
@@ -8,6 +8,39 @@
"This notebook demonstrates MAE plots and comparisons for GBM samplers. "
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/GBM/gbm_examples.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " import sys\n",
+ " import os\n",
+ " repo_root = \"/content/QMCSoftware\"\n",
+ " notebook_dir = f\"{repo_root}/demos/GBM\"\n",
+ " if not os.path.isdir(repo_root):\n",
+ " !git clone -q --depth 1 https://github.com/QMCSoftware/QMCSoftware {repo_root}\n",
+ " !pip install -q qmcpy\n",
+ " os.chdir(notebook_dir)\n",
+ " if notebook_dir not in sys.path:\n",
+ " sys.path.insert(0, notebook_dir)\n",
+ " extra_path = f\"{repo_root}/demos/GBM/gbm_code\"\n",
+ " if extra_path not in sys.path:\n",
+ " sys.path.insert(0, extra_path)\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/demos/acceptance_rejection.ipynb b/demos/acceptance_rejection.ipynb
index e603aacdf..c9b69f5da 100644
--- a/demos/acceptance_rejection.ipynb
+++ b/demos/acceptance_rejection.ipynb
@@ -13,21 +13,35 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/acceptance_rejection.ipynb)"
+ "**Reference:** Zhu, H. & Dick, J. (2014). *Discrepancy bounds for deterministic acceptance-rejection samplers.* Electronic Journal of Statistics, 8(1), 678–707. [DOI: 10.1214/14-EJS898](https://doi.org/10.1214/14-EJS898)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "**Reference:** Zhu, H. & Dick, J. (2014). *Discrepancy bounds for deterministic acceptance-rejection samplers.* Electronic Journal of Statistics, 8(1), 678–707. [DOI: 10.1214/14-EJS898](https://doi.org/10.1214/14-EJS898)"
+ "## Setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Setup"
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/acceptance_rejection.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
]
},
{
diff --git a/demos/asian-option-mlqmc.ipynb b/demos/asian-option-mlqmc.ipynb
index 2b91aeaf2..638280662 100644
--- a/demos/asian-option-mlqmc.ipynb
+++ b/demos/asian-option-mlqmc.ipynb
@@ -1,5 +1,26 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/asian-option-mlqmc.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
@@ -27,13 +48,6 @@
"# Comparison of multilevel (Quasi-)Monte Carlo for an Asian option problem"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/asian-option-mlqmc.ipynb)"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/demos/control_variates.ipynb b/demos/control_variates.ipynb
index 3d94086cd..d92336e62 100644
--- a/demos/control_variates.ipynb
+++ b/demos/control_variates.ipynb
@@ -11,6 +11,15 @@
"This notebook demonstrates QMCPy's current support for control variates. "
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "v_CDThSJUpUz"
+ },
+ "source": [
+ "## Setup"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -19,12 +28,17 @@
]
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "v_CDThSJUpUz"
- },
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
- "## Setup"
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
]
},
{
diff --git a/demos/demo_resume_data/Iteration_Log_Tolerance_Demo.ipynb b/demos/demo_resume_data/Iteration_Log_Tolerance_Demo.ipynb
index ed0adbaea..5c9b4fa36 100644
--- a/demos/demo_resume_data/Iteration_Log_Tolerance_Demo.ipynb
+++ b/demos/demo_resume_data/Iteration_Log_Tolerance_Demo.ipynb
@@ -21,6 +21,27 @@
"This naive approach requires re-running the solver for every target tolerance. This method suffers from poor scaling, making it impractical for large-scale parameter sweeps.\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/demo_resume_data/Iteration_Log_Tolerance_Demo.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/demos/demo_resume_data/accuracy_and_resume.ipynb b/demos/demo_resume_data/accuracy_and_resume.ipynb
index 54522254f..57035d06b 100644
--- a/demos/demo_resume_data/accuracy_and_resume.ipynb
+++ b/demos/demo_resume_data/accuracy_and_resume.ipynb
@@ -84,6 +84,36 @@
"Let's see how this works in code. We will use a Genz oscillatory integrand and QMCPy's `CubQMCLatticeG` routine."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/demo_resume_data/accuracy_and_resume.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " import sys\n",
+ " import os\n",
+ " repo_root = \"/content/QMCSoftware\"\n",
+ " notebook_dir = f\"{repo_root}/demos/demo_resume_data\"\n",
+ " if not os.path.isdir(repo_root):\n",
+ " !git clone -q --depth 1 https://github.com/QMCSoftware/QMCSoftware {repo_root}\n",
+ " !pip install -q qmcpy\n",
+ " os.chdir(notebook_dir)\n",
+ " if notebook_dir not in sys.path:\n",
+ " sys.path.insert(0, notebook_dir)\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
diff --git a/demos/demo_resume_data/resume_examples.ipynb b/demos/demo_resume_data/resume_examples.ipynb
index 84e42c45e..2e5746330 100644
--- a/demos/demo_resume_data/resume_examples.ipynb
+++ b/demos/demo_resume_data/resume_examples.ipynb
@@ -23,6 +23,36 @@
"For small examples, wall-clock timing differences can be negligible; performance gains become clearer when the initial run already used substantial work."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/demo_resume_data/resume_examples.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " import sys\n",
+ " import os\n",
+ " repo_root = \"/content/QMCSoftware\"\n",
+ " notebook_dir = f\"{repo_root}/demos/demo_resume_data\"\n",
+ " if not os.path.isdir(repo_root):\n",
+ " !git clone -q --depth 1 https://github.com/QMCSoftware/QMCSoftware {repo_root}\n",
+ " !pip install -q qmcpy\n",
+ " os.chdir(notebook_dir)\n",
+ " if notebook_dir not in sys.path:\n",
+ " sys.path.insert(0, notebook_dir)\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/demos/digital_net_b2.ipynb b/demos/digital_net_b2.ipynb
index 3e26202f2..619d4dbf6 100644
--- a/demos/digital_net_b2.ipynb
+++ b/demos/digital_net_b2.ipynb
@@ -7,6 +7,27 @@
"# Digital Net Base 2 Generator"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/digital_net_b2.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/demos/elliptic-pde.ipynb b/demos/elliptic-pde.ipynb
index c49fb2cbf..9d8a9f1ae 100644
--- a/demos/elliptic-pde.ipynb
+++ b/demos/elliptic-pde.ipynb
@@ -4,11 +4,24 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Elliptic PDE\n",
- "\n",
"[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/elliptic-pde.ipynb)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ " !tmp=$(mktemp) && if { apt-get update -qq && DEBIAN_FRONTEND=noninteractive apt-get install -y -qq --no-install-recommends texlive-latex-base texlive-fonts-recommended texlive-latex-extra cm-super dvipng; } >\"$tmp\" 2>&1; then rm -f \"$tmp\"; else status=$?; cat \"$tmp\"; rm -f \"$tmp\"; exit $status; fi\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
@@ -1193,7 +1206,7 @@
"kernelspec": {
"display_name": "qmcpy",
"language": "python",
- "name": "qmcpy"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -1205,7 +1218,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.12"
+ "version": "3.13.13"
}
},
"nbformat": 4,
diff --git a/demos/gaussian_diagnostics/gaussian_diagnostics_demo.ipynb b/demos/gaussian_diagnostics/gaussian_diagnostics_demo.ipynb
index d92611d7a..999a16b4b 100644
--- a/demos/gaussian_diagnostics/gaussian_diagnostics_demo.ipynb
+++ b/demos/gaussian_diagnostics/gaussian_diagnostics_demo.ipynb
@@ -13,7 +13,21 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/gaussian_diagnostics_demo.ipynb)"
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/gaussian_diagnostics/gaussian_diagnostics_demo.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
]
},
{
diff --git a/demos/iris.ipynb b/demos/iris.ipynb
index 3cb13df76..b2d7914f3 100644
--- a/demos/iris.ipynb
+++ b/demos/iris.ipynb
@@ -6,11 +6,32 @@
"source": [
"# ML Sensitivity Indices\n",
"\n",
- "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/iris.ipynb)\n",
"\n",
"This notebook demonstrates QMCPy's support for vectorized sensitivity index computation. We preview this functionality by performing classification of Iris species using a decision tree. The computed sensitivity indices provide insight into input subset importance for a classic machine learning problem."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/iris.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ " !pip install -q scikit-learn scikit-optimize\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 2,
diff --git a/demos/lattice_random_generator.ipynb b/demos/lattice_random_generator.ipynb
index 50c518314..76fb2ca43 100644
--- a/demos/lattice_random_generator.ipynb
+++ b/demos/lattice_random_generator.ipynb
@@ -7,6 +7,27 @@
"# Random Lattice Generators Are Not Bad\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/lattice_random_generator.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 16,
diff --git a/demos/lebesgue_integration.ipynb b/demos/lebesgue_integration.ipynb
index 88d9dd390..cdc6521c3 100644
--- a/demos/lebesgue_integration.ipynb
+++ b/demos/lebesgue_integration.ipynb
@@ -15,6 +15,20 @@
"[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/lebesgue_integration.ipynb)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 26,
diff --git a/demos/linear-scrambled-halton.ipynb b/demos/linear-scrambled-halton.ipynb
index b1da04703..47b64a9f6 100644
--- a/demos/linear-scrambled-halton.ipynb
+++ b/demos/linear-scrambled-halton.ipynb
@@ -63,6 +63,27 @@
"### Here we set up the QMCPY environment:"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/linear-scrambled-halton.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/demos/nei_demo.ipynb b/demos/nei_demo.ipynb
index 2558e459c..c2671e88d 100644
--- a/demos/nei_demo.ipynb
+++ b/demos/nei_demo.ipynb
@@ -9,6 +9,27 @@
"You can also look at the Botorch implementation, but that requires a lot more understanding of code which involves Pytorch. So we tried to put a simple example together here."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/nei_demo.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/demos/plot_proj_function.ipynb b/demos/plot_proj_function.ipynb
index 58239d547..445f66726 100644
--- a/demos/plot_proj_function.ipynb
+++ b/demos/plot_proj_function.ipynb
@@ -28,6 +28,27 @@
"### Here we set up the QMCPY environment enabling us to utilize this function:"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/plot_proj_function.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/demos/pricing_options.ipynb b/demos/pricing_options.ipynb
index 791580669..7e0759ae5 100644
--- a/demos/pricing_options.ipynb
+++ b/demos/pricing_options.ipynb
@@ -11,6 +11,27 @@
"- The option is only exercised at expiry, unlike American options, which can be exercised at any time before expiry.\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/pricing_options.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 36,
diff --git a/demos/qei-demo-for-blog.ipynb b/demos/qei-demo-for-blog.ipynb
index a7dc6ad62..b95c7f8ee 100644
--- a/demos/qei-demo-for-blog.ipynb
+++ b/demos/qei-demo-for-blog.ipynb
@@ -7,6 +7,27 @@
"# QEI (Q-Noisy Expected Improvement) Demo for Blog"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/qei-demo-for-blog.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/demos/qmcpy_intro.ipynb b/demos/qmcpy_intro.ipynb
index b3c3a618b..aa4bb2d20 100644
--- a/demos/qmcpy_intro.ipynb
+++ b/demos/qmcpy_intro.ipynb
@@ -12,14 +12,28 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/qmcpy_intro.ipynb)"
+ "Here we show three different ways to import QMCPy in a Python environment. First, we can import the package `qmcpy` under the alias `qp`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Here we show three different ways to import QMCPy in a Python environment. First, we can import the package `qmcpy` under the alias `qp`."
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/qmcpy_intro.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
]
},
{
diff --git a/demos/quickstart.ipynb b/demos/quickstart.ipynb
index 6e86acf05..d020ed7dc 100644
--- a/demos/quickstart.ipynb
+++ b/demos/quickstart.ipynb
@@ -12,13 +12,6 @@
"In this tutorial, we introduce QMCPy [1] by an example. QMCPy can be installed with **pip install qmcpy** or cloned from the [QMCSoftware GitHub repository](https://github.com/QMCSoftware/QMCSoftware)."
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/quickstart.ipynb)"
- ]
- },
{
"cell_type": "markdown",
"metadata": {
@@ -36,6 +29,27 @@
"The Keister function is implemented below with help from NumPy [3] in the following code snippet:"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/quickstart.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 4,
diff --git a/demos/ray_tracing.ipynb b/demos/ray_tracing.ipynb
index e9f9bf0d4..613528ae0 100644
--- a/demos/ray_tracing.ipynb
+++ b/demos/ray_tracing.ipynb
@@ -12,6 +12,27 @@
"by Paul Rademacher](http://wwwx.cs.unc.edu/~rademach/xroads-RT/RTarticle.html)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/ray_tracing.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 9,
diff --git a/demos/sample_scatter_plots.ipynb b/demos/sample_scatter_plots.ipynb
index 33d434234..186ac9177 100644
--- a/demos/sample_scatter_plots.ipynb
+++ b/demos/sample_scatter_plots.ipynb
@@ -7,6 +7,27 @@
"# Scatter Plots of Samples"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/sample_scatter_plots.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 14,
diff --git a/demos/scipywrapper_dependence_custom/scipywrapper_demo.ipynb b/demos/scipywrapper_dependence_custom/scipywrapper_demo.ipynb
index 22e42362e..9af9f2677 100644
--- a/demos/scipywrapper_dependence_custom/scipywrapper_demo.ipynb
+++ b/demos/scipywrapper_dependence_custom/scipywrapper_demo.ipynb
@@ -1,5 +1,26 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/scipywrapper_dependence_custom/scipywrapper_demo.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/demos/some_true_measures.ipynb b/demos/some_true_measures.ipynb
index cea9c1366..d0ccd5709 100644
--- a/demos/some_true_measures.ipynb
+++ b/demos/some_true_measures.ipynb
@@ -57,6 +57,27 @@
"## Imports"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/some_true_measures.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/demos/talk_paper_demos/ACMTOMS_Sorokin_2025/acm_toms_sorokin_2025.ipynb b/demos/talk_paper_demos/ACMTOMS_Sorokin_2025/acm_toms_sorokin_2025.ipynb
index 39e714c79..0444781d2 100644
--- a/demos/talk_paper_demos/ACMTOMS_Sorokin_2025/acm_toms_sorokin_2025.ipynb
+++ b/demos/talk_paper_demos/ACMTOMS_Sorokin_2025/acm_toms_sorokin_2025.ipynb
@@ -14,6 +14,28 @@
"## Setup"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/talk_paper_demos/ACMTOMS_Sorokin_2025/acm_toms_sorokin_2025.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ " !pip install -q sympy torch tueplots\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/demos/talk_paper_demos/JOSS2026/joss2026.ipynb b/demos/talk_paper_demos/JOSS2026/joss2026.ipynb
index df5e6e86a..e8f491a3f 100644
--- a/demos/talk_paper_demos/JOSS2026/joss2026.ipynb
+++ b/demos/talk_paper_demos/JOSS2026/joss2026.ipynb
@@ -10,18 +10,32 @@
},
{
"cell_type": "markdown",
- "id": "a32d4c28",
+ "id": "e862347b",
"metadata": {},
"source": [
- "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/joss/demos/talk_paper_demos/JOSS2026/joss2026.ipynb)"
+ "## Setup"
]
},
{
"cell_type": "markdown",
- "id": "e862347b",
"metadata": {},
"source": [
- "## Setup"
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/talk_paper_demos/JOSS2026/joss2026.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ " !pip install -q seaborn tueplots\n",
+ "except:\n",
+ " pass"
]
},
{
diff --git a/demos/talk_paper_demos/MCQMC_Tutorial_2020/MCQMC_2020_QMC_Software_Tutorial.ipynb b/demos/talk_paper_demos/MCQMC_Tutorial_2020/MCQMC_2020_QMC_Software_Tutorial.ipynb
index 2e54f18f4..2306384de 100644
--- a/demos/talk_paper_demos/MCQMC_Tutorial_2020/MCQMC_2020_QMC_Software_Tutorial.ipynb
+++ b/demos/talk_paper_demos/MCQMC_Tutorial_2020/MCQMC_2020_QMC_Software_Tutorial.ipynb
@@ -1,3514 +1,3536 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "0xwyIP8iF1oa"
- },
- "source": [
- "[QMCPy]: https://qmcsoftware.github.io/QMCSoftware/ \"Choi, S.-C. T., Hickernell, F. J., McCourt, M. & Sorokin, A. A quasi-Monte Carlo Python Library. https://qmcsoftware.github.io/QMCSoftware/. 2020.\"\n",
- "\n",
- "[GAIL]: http://gailgithub.github.io/GAIL_Dev/ \"Sou-Cheng T. Choi, Yuhan Ding, Fred J. Hickernell, Lan Jiang, Lluis Antoni Jimenez Rugama, Da Li, Jagadeeswaran Rathinavel, Xin Tong, Kan Zhang, Yizhi Zhang, and Xuan Zhou, GAIL: Guaranteed Automatic Integration Library (Version 2.3.1) [MATLAB Software], 2020. Available from `http://gailgithub.github.io/GAIL_Dev/`\"\n",
- "\n",
- "[NumPy]: https://pypi.org/project/numpy/ \"Oliphant, T. Guide to NumPy https://ecs.wgtn.ac.nz/foswiki/pub/Support/ManualPagesAndDocumentation/numpybook.pdf (TrelgolPublishing USA, 2006).\"\n",
- "\n",
- "[Scipy]: https://pypi.org/project/scipy/ \"Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E.A. Quintero, Charles R Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. (2020) SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, in press.\"\n",
- "\n",
- "[QRNG]: https://CRAN.R-project.org/package=qrng \"Marius Hofert and Christiane Lemieux (2019). qrng: (Randomized) Quasi-Random Number Generators. R package version 0.0-7. `https://CRAN.R-project.org/package=qrng`.\"\n",
- "\n",
- "[OwenHalton]: https://arxiv.org/abs/1706.02808 \"Owen, A. B. 'A randomized Halton algorithm in R,' 2017. arXiv:1706.02808 [stat.CO]\"\n",
- "\n",
- "[MPS]: https://people.cs.kuleuven.be/~dirk.nuyens/qmc-generators/ \"F. Y. Kuo and D. Nuyens. 'Application of quasi-Monte Carlo methods to elliptic PDEs with random diffusion coefficients - a survey of analysis and implementation,' Foundations of Computational Mathematics, 16(6):1631-1696, 2016. ([springer link](https://link.springer.com/article/10.1007/s10208-016-9329-5), [arxiv link](https://arxiv.org/abs/1606.06613))\"\n",
- "\n",
- "[CubMCML]: https://pubsonline.informs.org/doi/abs/10.1287/opre.1070.0496 \"Giles, M. Multilevel Monte Carlo Path Simulation. Operations Research 56,607–617 (June 2008).\"\n",
- "\n",
- "[CubQMCML]: https://www.semanticscholar.org/paper/Multilevel-quasi-Monte-Carlo-path-simulation-Giles-Waterhouse/25a9ca3aa216aa371d1be06fa9b93927187ee4ca \"Giles, M. B. & Waterhouse, B. J. Multilevel quasi-Monte Carlo path simulation. Advanced Financial Modelling, Radon Series on Computational and Applied Mathematics 8,165–181 (2009).\"\n",
- "\n",
- "[GilesSoftware]: http://people.maths.ox.ac.uk/~gilesm/mlmc/\n",
- "\n",
- "[PyTorch]: https://pytorch.org\n",
- "\n",
- "[LatticeBuilder]: http://simul.iro.umontreal.ca/latbuilder/ \"L’Ecuyer, Pierre & Munger, David. (2015). LatticeBuilder: A General Software Tool for Constructing Rank-1 Lattice Rules. ACM Transactions on Mathematical Software. 42. 10.1145/2754929.\"\n",
- "\n",
- "# Quasi-Monte Carlo (QMC) Software in QMCPy\n",
- "\n",
- "A tutorial based on this notebook is at https://media.ed.ac.uk/playlist/dedicated/51612401/1_0z0wec2z/1_2k12mwiw.\n",
- "\n",
- "As an example of available QMC software, we introduce the Python library [QMCPy][QMCPy]. This Jupyter notebook saves your typing. \n",
- "\n",
- "QMCPy is a community effort. This early release includes contributions from\n",
- "\n",
- "* Mike Giles [MLMC][CubMCML] and [MLQMC][CubQMCML] [software][GilesSoftware]\n",
- "* Marius Hofert and Christiane Lemieux's [QRNG][QRNG]\n",
- "* Pierre L'Ecuyer's [Lattice Builder][LatticeBuilder]\n",
- "* Dirk Nuyens's [Magic Point Shop (MPS)][MPS]\n",
- "* Art Owen's [Halton sequences][OwenHalton]\n",
- "* [PyTorch][PyTorch]\n",
- "* Guaranteed Automatic Integration Library [(GAIL)][GAIL]\n",
- "\n",
- "and depends on the [NumPy][NumPy] and [SciPy][SciPy] Python packages.\n",
- "\n",
- "View the companion pdf slides at https://speakerdeck.com/fjhickernell/quasi-monte-carlo-software and the introductory QMCPy blog at https://qmcpy.wordpress.com."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "EbOXiuCQVs46"
- },
- "source": [
- "\n",
- "\n",
- "## Installation\n",
- "QMCPy can be installed with ``pip install qmcpy`` or cloned from the [QMCSoftware GitHub repository](https://github.com/QMCSoftware/QMCSoftware). "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "6Z9pGNDeWIBw",
- "outputId": "481b6697-de4a-498b-ce14-18fe3926f839"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "QMCPy Version 1.6.3.2a\n"
- ]
- }
- ],
- "source": [
- "import qmcpy #we import the environment at the start to use it\n",
- "import numpy as np #basic numerical routines in Python\n",
- "import time #timing routines\n",
- "import warnings #to suppress warnings when needed\n",
- "import torch #only needed for PyTorch Sobol' backend\n",
- "from torch.quasirandom import SobolEngine\n",
- "from matplotlib import pyplot; #plotting\n",
- "\n",
- "pyplot.rc('font', size=16) #set defaults so that the plots are readable\n",
- "pyplot.rc('axes', titlesize=16)\n",
- "pyplot.rc('axes', labelsize=16)\n",
- "pyplot.rc('xtick', labelsize=16)\n",
- "pyplot.rc('ytick', labelsize=16)\n",
- "pyplot.rc('legend', fontsize=16)\n",
- "pyplot.rc('figure', titlesize=16)\n",
- "\n",
- "#a helpful plotting method to show increasing numbers of points\n",
- "def plot_successive_points(distrib,ld_name,first_n=64,n_cols=1,pt_clr='bgkcmy',\n",
- " xlim=[0,1],ylim=[0,1],coord1 = 0,coord2 = 1):\n",
- " fig,ax = pyplot.subplots(nrows=1,ncols=n_cols,figsize=(5*n_cols,5.5))\n",
- " if n_cols==1: ax = [ax]\n",
- " last_n = first_n*(2**n_cols)\n",
- " points = distrib.gen_samples(n=last_n)\n",
- " for i in range(n_cols):\n",
- " n = first_n\n",
- " nstart = 0\n",
- " for j in range(i+1):\n",
- " n = first_n*(2**j)\n",
- " ax[i].scatter(points[nstart:n,coord1],points[nstart:n,coord2],color=pt_clr[j])\n",
- " nstart = n\n",
- " ax[i].set_title('n = %d'%n)\n",
- " ax[i].set_xlim(xlim); ax[i].set_xticks(xlim); ax[i].set_xlabel('$x_{i,%d}$'%(coord1+1))\n",
- " ax[i].set_ylim(ylim); ax[i].set_yticks(ylim); ax[i].set_ylabel('$x_{i,%d}$'%(coord2+1))\n",
- " ax[i].set_aspect((xlim[1]-xlim[0])/(ylim[1]-ylim[0]))\n",
- " fig.suptitle('%s Points'%ld_name)\n",
- "print('QMCPy Version',qmcpy.__version__)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "VuiiIKLQZW0G"
- },
- "source": [
- "## Generating low discrepancy (LD) points via a ``DiscreteDistribution`` object\n",
- "\n",
- "We generate some points used for quasi-Monte Carlo methods. These points are called low discrepancy (LD for short) and are created as an instance of a ``DiscreteDistribution`` class."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "bWz-RtrMHll7"
- },
- "source": [
- "### Integration lattices\n",
- "\n",
- "Here are some (randomly shifted) integration lattice points. This is a two step procees: \n",
- "\n",
- "i) construct the ``DiscreteDistribution`` object ``lattice``, and then\n",
- "\n",
- "ii) construct a number of points from the sequence. \n",
- "\n",
- "The structure of these points favors ``n`` that is a power of 2."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 834
- },
- "id": "JhMZcYfOhchK",
- "outputId": "90ac5112-0e25-4c18-99f6-494d9164fa85"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Lattice (AbstractLDDiscreteDistribution)\n",
- " d 2^(1)\n",
- " replications 1\n",
- " randomize SHIFT\n",
- " gen_vec_source kuo.lattice-33002-1024-1048576.9125.txt\n",
- " order RADICAL INVERSE\n",
- " n_limit 2^(20)\n",
- " entropy 264030601762852985433978628854999910438\n",
- "\n",
- "LD Lattice Points with shape (16, 2)\n",
- "[[0.14448802 0.23129441]\n",
- " [0.64448802 0.73129441]\n",
- " [0.39448802 0.98129441]\n",
- " [0.89448802 0.48129441]\n",
- " [0.26948802 0.60629441]\n",
- " [0.76948802 0.10629441]\n",
- " [0.51948802 0.35629441]\n",
- " [0.01948802 0.85629441]\n",
- " [0.20698802 0.91879441]\n",
- " [0.70698802 0.41879441]\n",
- " [0.45698802 0.66879441]\n",
- " [0.95698802 0.16879441]\n",
- " [0.33198802 0.29379441]\n",
- " [0.83198802 0.79379441]\n",
- " [0.58198802 0.04379441]\n",
- " [0.08198802 0.54379441]]\n"
- ]
- },
- {
- "data": {
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pec9ABAAgGkglAAAsEKQAACTz0K4TdDp+8Sn5Z8+eNcc66fR7j8cT07YBAGJDF63o9pUXXXSR1W1DVwTphAkTZOzYsbFuBgAgDun5v40bNy7317siSPXA5GHDhhW9P3TokDnGSj88/7FVAAD3bTKSmpoacNxfebgiSPXAZH0E0xAlSOG0wkKR1av1WDAR3SmvRw+RihVj3SoApbG9xeeKIAWiJTdXJDNTt837X5mOGOmeIrfeGsuWAYgUZu0CDoZoRkZgiKqCAl+5XgeQfAhSwKHhXO2Jhtq52l+WleWrByC5EKSAA/SeaHBPNDhM8/J89QAkl0rJegYiEE06scjJegASR6VkPgMRiJZwf4fjdz0g+cT9eaSRCudatWqZ9aQsf4ET9N5ns2a+iUWh/ovS2fU6e3f3bpbCAMmWBdwjBRyg4eg/Njd4SZr/fXY2IQokI4IUcIiuE83JEUlJCSzXnqiWs44USE5xf48USCQalunp7GwEuAlBCjhMQzMtLdatABAtDO0CAGCBIAUAwAJBCgCABYIUAAALBCkAABYIUgAALBCkAABYIEgBALBAkAIAYIEgBQDAAkEKAIAFghQAAAtsWo8yH2DNySYA8D8EKcKWmyuSmSmSnx941qYeaM1ZmwDciqFdhB2iGRmBIaoKCnzleh0A3IggRVjDudoT9XpLXvOXZWX56gGA2xCkOCe9JxrcEw0O07w8Xz0AcBuCFOekE4ucrAcAyYQgxTnp7Fwn6wFAMiFIcU66xEVn53o8oa9reWqqrx4AuA1BinPSdaK6xEUFh6n/fXY260kBuBNBirDoOtGcHJGUlMBy7alqOetIAbgVGzIgbBqW6ensbAQAxRGkKBMNzbS0WLcCAOIHQ7sAAFggSAEAsECQAgBggSAFAMACQQoAgAWCFAAACwQpAAAWCFIAACwQpAAAWCBIAQCwQJACAGCBIAUAwAJBCgCABU5/KaawkCPCAABlQ5D+V26uSGamSH5+4KHVkydzaDUAoHQM7f43RDMyAkNUFRT4yvU6AAChuD5IdThXe6Jeb8lr/rKsLF89AACCuT5I9Z5ocE80OEzz8nz1AAAI5vog1YlFTtYDALiL64NUZ+c6WQ8A4C6uD1Jd4qKzcz2e0Ne1PDXVVw8AgGCuD1JdJ6pLXFRwmPrfZ2eznhQAEJrrg1TpOtGcHJGUlMBy7alqOetIAQClYUOG/9KwTE9nZyMAQNkQpMVoaKalxboVAIBEwtAuAAAWCFIAACwQpAAAWCBIAQCwQJACAGCBIAUAwAJBCgCABYIUAAALBCkAABYIUgAALBCkAABYIEgBALDApvUA4DKFhZx05SSCFABcJDdXJDNTJD8/8OzlyZM5e7m8GNoFABeFaEZGYIiqggJfuV5H2RGkAOCS4VztiXq9Ja/5y7KyfPVQNgQpALiA3hMN7okGh2lenq8eyoYgBQAX0IlFTtbD/xCkAOACOjvXyXr4H4IUAFxAl7jo7FyPJ/R1LU9N9dVD2RCkAOACuk5Ul7io4DD1v8/OZj1peRCkAOASuk40J0ckJSWwXHuqWs460vJhQwYAcBENy/R0djZyEkEKAC6joZmWFutWJA+GdgEAsECQAgBggSAFAMACQQoAgAWCFAAACwQpAAAWCFIAACwQpAAAWCBIAQCwQJACAGCBIAUAwAJBCgCABTatB5BUCgs52QTRRZACSBq5uSKZmSL5+YFnbeqB1py1iUhhaBdA0oRoRkZgiKqCAl+5XgcigSAFkBTDudoT9XpLXvOXZWX56gFOI0gBJDy9JxrcEw0O07w8Xz3AaQQpgISnE4ucrAeUBUEKIOHp7Fwn6wFlQZACSHi6xEVn53o8oa9reWqqrx7gNIIUQMLTdaK6xEUFh6n/fXY260kRGQQpgKSg60RzckRSUgLLtaeq5awjRaSwIQOApKFhmZ7OzkaILoIUQFLR0ExLi3Ur4CYM7QIAYIEgBQDAAkEKAIAFghQAAAsEKQAAFghSAAAsEKQAAFggSAEAsECQAgBggSAFAMACQQoAgAWCFAAAC2xaDySYwkJONwHiCUEKJJDcXJHMTJH8/MDzNvVQa87bBGKDoV0ggUI0IyMwRFVBga9crwOIPoIUSJDhXO2Jer0lr/nLsrJ89QBEF0EKJAC9JxrcEw0O07w8Xz0A0UWQAglAJxY5WQ+AcwhSIAHo7Fwn6wFwDkEKJABd4qKzcz2e0Ne1PDXVVw9AdBGkQALQdaK6xEUFh6n/fXY260mBWCBIgQSh60RzckRSUgLLtaeq5awjBWKDDRmABKJhmZ7OzkZAPCFIgQSjoZmWFutWAPBjaBcAAAsEKQAAFghSAAAsEKQAAFggSAEAsECQAgBggSAFAMACQQoAgAWCFAAACwQpAAAWCFIAACwQpAAAWCBIAQCwwOkviCuFhRwRBiCxEKSIG7m5IpmZIvn5gYdWT57ModUA4hdDu4ibEM3ICAxRVVDgK9frABCPCFLExXCu9kS93pLX/GVZWb56ABBvCFLEnN4TDe6JBodpXp6vHgDEG4IUMacTi5ysBwDRRJAi5nR2rpP1ACCaCFLEnC5x0dm5Hk/o61qemuqrBwDxhiBFzOk6UV3iooLD1P8+O5v1pADiE0GKuKDrRHNyRFJSAsu1p6rlrCMFEK/YkAFxQ8MyPZ2djQAkFoIUcUVDMy0t1q0AgPAxtAsAgAWCFAAACwQpAAAWCFIAACwQpAAAWCBIAQCwQJACAGCBIAUAwAJBCgCABYIUAAALBCkAABYIUgAALLBpfRQVFnKyCQAkG4I0SnJzRTIzRfLzA8/a1AOtOWsTABIXQ7tRCtGMjMAQVQUFvnK9DgBITARpFIZztSfq9Za85i/LyvLVAwAkHoI0wvSeaHBPNDhM8/J89QAAiYcgjTCdWORkPQBAEgXp999/L1u2bJGjR4+GvH7kyBFZtWqVuJnOznWyHgAgCYL0zJkzcvfdd0vDhg2lQ4cOcuGFF0pWVpYcP348oN62bdukZ8+e4ma6xEVn53o8oa9reWqqrx4AwCVBOmXKFHn99dflmWeekbffftuE6Msvvyxdu3aV/fv3O9/KBKbrRHWJiwoOU//77GzWkwKAq4L0r3/9q4wePVqefPJJ6d27t0yYMEE+/vhjOXbsmAnTL7/80vmWJjBdJ5qTI5KSEliuPVUtZx0pALgsSHfv3m0Cs7jLL79c1q1bJ3Xr1pVu3brJxo0bnWpjUtCw/PprkRUrRObO9T3v3k2IAoArdzaqX79+yCHcevXqyYoVK6Rfv37m3ujw4cOdaGPS0OHbtLRYtwIAEPMeaceOHWXhwoUhr1WvXl3eeecd6dWrlzz11FO27QMAIPmCtH///mZ498CBAyGvV6lSRebPny/333+/NGnSxLaNAADELY/XG2rzuuR2+PBhqVWrlhw6dEhq1qwZ6+YAABI4C9jZCAAACwQpAAAWCFIAACwQpAAAxGuQ6haCunXgiRMnIvljAABIzlm7FSr4clo3tR82bFjcbNDArF0AwGGHsqBcOxuFS9ea6v67a9eulTVr1kTyRwEAEBOsI6VHCgCudDiW60i/++67cv9AAACSSbmC9KKLLpJNmzYVTShatGiR7Nmzx+m2AQASUGGhyMqVIv/6l+9Z3yezct0jffPNN6VRo0bmtc7KLSgoEI/HY7rGV155pbRt27bo0bp1aznvvPOcbjcAIA7l5opkZork5weevTx5cvIeG+nIPdKDBw/Kli1bAh7btm2TU6dOScWKFeX06dMST7hHCgCRCdGMDJHgVPF4fM85OfEVpk5lQdhBqj9If2C4zpw5I59//rl8+umnctddd0k8IUgBwFmFhSLNmgX2RIPDVHumu3f7zmZ25WQjPai7tGPTQqlUqZK0adMm7kIUAOC81atLD1GlXba8PF+9ZBN2kG7evFmuvfZa+fbbb89ZN96GcgEAkbVvn7P1kjJIn3jiCTNU26NHD9m7d2+p9V5//XVp1aqVU+0DACSARo2crZeUQTpx4kR59tln5auvvjJhunPnzoDrH374oXTt2lX69+8vX3/9dSTaCgCIUz16+O6B+icWBdPy1FRfPVevIx05cqRMmzZN8vPzzTDv1q1bTWjecccd0q1bNxOmTZo0kVdffTVyLQYAxJ2KFX1LXFRwmPrfZ2fHz0SjmC9/mTNnjtx9991ywQUXyMmTJ82jbt26MmrUKBk6dKhUqVJF4hmzdgEgeutIU1N9IRpPS19iumn92bNnzUb0NWrUkB9//NFsxHDnnXfKjBkzyrQ8BgCQfG69VSQ93Tc7VycW6T1RHc5Nxp5ouYJ0wYIF8uSTT8qOHTtEO7J6T3TdunWybNkyc9JLu3btItdSAEBCqFhRJC1NXCPse6QamhkZGbJ9+3Zp3769rFixwhyNNmvWLNMz7dWrl3zwwQeRbS0AAIkapDqRKCUlxUwk2rBhg1x33XWmXO+Vzp071wz33nTTTbJ06dJIthcAgMQM0nHjxskXX3whAwcOLHHt9ttvN8O+ev+0X79+5jUAAG7g6MHe77//vvTt27doJm+8YtYuAOBwLA/2Lo0O9+rEI53RCwCAGzgapKpz586yUk9yBQDABRwPUqWHeQMA4AYRCVIAANyCIAUAwAJBCgCABYIUAAALBCkAABbKfPoLALhdYaG7TjfBzyNIAcDyvM3GjX2HWsfbeZuIDoZ2AaAMIZqRERiiqqDAV67X4T4EKQCEOZyrPdFQu5P7y7KyfPXgLgQpAIRB74kG90SDwzQvz1cP7kKQAkAYdGKRk/WQPAhSAAiDzs51sh6SB0EKAGHQJS46O9fjCX1dy1NTffXgLgQpAIRB14nqEhcVHKb+99nZrCd1I4IUAMKk60RzckRSUgLLtaeq5awjdSc2ZACAMtCwTE9nZyP8D0EKAGWkoZmWFutWIF4wtAsAgAWCFAAACwQpAAAWCFIAACwQpAAAWCBIAQCwQJACAGCBIAUAwAJBCgCABYIUAAALBCkAABYIUgAALLBpPYAAhYWcbAKUBUEKoEhurkhmpkh+fuBZm3qgNWdtAqExtAugKEQzMgJDVBUU+Mr1OoCSCFIAZjhXe6Jeb8lr/rKsLF89AIEIUgDmnmhwTzQ4TPPyfPUABCJIAZiJRU7WA9yEIAVgZuc6WQ9wE4IUgFniorNzPZ7Q17U8NdVXD0AgghSAWSeqS1xUcJj632dns54UCIUgBWDoOtGcHJGUlMBy7alqOetIgdDYkAFAEQ3L9HR2NgLKgiAFEEBDMy0t1q0AEgdDuwAAWCBIAQCwQJACAGCBIAUAwAJBCgCABYIUAAALBCkAABYIUgAALBCkAABYIEgBALBAkAIAYIEgBQDAAkEKAIAFTn+BqxQWckQYAGcRpHCN3FyRzEyR/PzAQ6snT+bQagDlx9AuXBOiGRmBIaoKCnzleh0AyoMghSuGc7Un6vWWvOYvy8ry1QOAsiJIkfT0nmhwTzQ4TPPyfPUAoKwIUiQ9nVjkZD0AKI4gRdLT2blO1gOA4ghSJD1d4qKzcz2e0Ne1PDXVVw8AyoogRdLTdaK6xEUFh6n/fXY260kBlA9BClfQdaI5OSIpKYHl2lPVctaRAigvNmSAa2hYpqezsxEAZxGkcBUNzbS0WLcCQDJhaBcAAAsEKQAAFghSAAAsEKQAAFggSAEAsECQAgBggSAFAMACQQoAgAWCFAAACwQpAAAWCFIAACwQpAAAWGDT+iRTWMjpJgAQTQRpEsnNFcnMFMnPDzxvUw+15rxNAIgMhnaTKEQzMgJDVBUU+Mr1OgDAeQRpkgznak/U6y15zV+WleWrBwBwFkGaBPSeaHBPNDhM8/J89QAAziJIk4BOLHKyHgAgfARpEtDZuU7WAwCEjyBNArrERWfnejyhr2t5aqqvHgDAWQRpEtB1orrERQWHqf99djbrSQEgEgjSJKHrRHNyRFJSAsu1p6rlrCMFgMhgQ4YkomGZns7ORgAQTQRpktHQTEuLdSsAwD0Y2gUAwAJBCgCABYIUAAALBCkAABYIUgAALBCkAABYIEgBALBAkAIAYIEgBQDAAkEKAIAFghQAAAsEKQAAFti0HgCQcAoL4+ekK4IUAJBQcnNFMjNF8vMDz16ePDk2Zy8ztAsASKgQzcgIDFFVUOAr1+vRRpACABJmODczU8TrLXnNX5aV5asXTQQpACAhrF5dsicaHKZ5eb560USQAgASwr59ztZzCkEKAEgIjRo5W88pBCkAICH06OGbnevxhL6u5ampvnrRRJACABJCxYq+JS4qOEz977Ozo7+elCAFACSMW28VyckRSUkJLNeeqpbHYh0pGzIAABLKrbeKpKezsxEAAOWmoZmWJnGBoV0AACwQpAAAWCBIAQCwQJACAGCBIAUAwAJBCgCABYIUAAALBCkAABYIUgAALBCkAABYIEgBALBAkAIAYIFN6wEgzhQWxs/JJjg3ghQA4khurkhmpkh+fuBZm3qgdSzO2sS5MbQLAHEUohkZgSGqCgp85Xod8YcgBYA4Gc7VnqjXW/Kavywry1cP8YUgBYA4oPdEg3uiwWGal+erh/hCkAJAHNCJRU7WQ/QQpAAQB3R2rpP1ED0EKQDEAV3iorNzPZ7Q17U8NdVXD/GFIAWAOKDrRHWJiwoOU//77GzWk8YjghQA4oSuE83JEUlJCSzXnqqWs440PrEhAwDEEQ3L9HR2NkokBCkAxBkNzbS0WLcC4WJoFwAACwQpAAAWCFIAACwQpAAAWCBIAQCwQJACAGCBIAUAwAJBCgCABYIUAAALBCkAABYIUgAALBCkAABYIEgBALDA6S8AoqqwkCPCkFwIUgBRk5srkpkpkp8feGj15MkcWo3ExdAugKiFaEZGYIiqggJfuV4HEhFBCiAqw7naE/V6S17zl2Vl+eoBiYYgBRBxek80uCcaHKZ5eb56QKIhSAFEnE4scrIeEE8IUgARp7NznawHxBOCFEDE6RIXnZ3r8YS+ruWpqb56QKIhSAFEnK4T1SUuKjhM/e+zs1lPisREkAKICl0nmpMjkpISWK49VS1nHSkSFRsyAIgaDcv0dHY2QnIhSAFElYZmWlqsWwE4h6FdAAAsEKQAALghSOfNmydpaWlSp04dqVatmrRt21YmTZokp0+fjnXTAAAulhBBmpWVJb/73e/kgw8+kM6dO0vv3r1l7969Mnz4cOnVq5ccP3481k0EALhU3AfpwoULZfLkyVK9enVZv369LFmyRObPny87d+6UNm3ayJo1a2T06NGxbiYAwKXiPkife+458zxixAjp0KFDUXn9+vVl+vTp5vVLL70khw4dilkbAQDuFddBWlBQIB9//LF53b9//xLXu3fvLqmpqXLy5ElZvHhxDFoIAHC7uA7STZs2mee6detK8+bNQ9bp1KlTQF0AAKIproN09+7d5rlJkyal1tEeafG6AABEU1zvbHTkyBHzrMtdSqOTkNThw4dLraNDv/rw899P/bmvAQAkt8P/zQCvniyfrEHqlAkTJsjYsWNL7c0CANzrwIEDUqtWreQM0ho1apjnY8eOlVrn6NGj5rlmzZql1hk5cqQMGzas6P1PP/0kTZs2NWtRbT48AEDi0tFJvXWo83BsxHWQNmvWzDzn5eWVWsd/zV83lKpVq5pHMA3RnwtgAEDyq1ChQvJONmrfvn1Rt7u0yUQbNmwwz8XXmAIAEC1xHaSNGzeWq666yryeO3duieu6q5H2SLW32adPnxi0EADgdnEdpGrUqFHmeeLEibJx48aicu2lPvTQQ+b10KFDy3SvU4N3zJgxIYd7AQDuUNWhLPB4bef9RkFmZqZMmTJFKleuLNdff71ZDrN8+XIzaahbt26ydOlSOf/882PdTACACyVEkKo33nhDpk2bJps3bzZHp7Vo0UIGDBggjzzyiFSpUiXWzQMAuFTCBCkAAPEo7u+RAgAQz1wVpPPmzZO0tDSpU6eOuc/atm1bmTRpkhkqBgAkvx07dsjUqVNl8ODB5kzrSpUqicfjkfHjxyfnhgxOysrKMgeE64fWq1cvs0fve++9J8OHD5dFixbJu+++y4QlAEhyM2bMMFngJFf0SBcuXGg+OA3P9evXy5IlS2T+/Pmyc+dO8xuJrkcdPXp0rJsJAIiw1q1by2OPPSZz5syRzz//XAYOHGj9PV3RI33uuefM84gRIwJ2QKpfv75Mnz5devToIS+99JIJU/beBYDkde+99zq6PaD5HpLkCgoK5OOPPzav+/fvX+J69+7dzSkwesza4sWLY9BCAEAiS/og3bRpk3nW3f2bN28esk6nTp0C6gIAEK6kD1L/Zvd6VE5p/OeSlrYxPgAArg3SI0eOmGdd7lIanYRU/LR0AADClfRBCgBAJCV9kNaoUcM8Hzt2rNQ6R48eNc8c8g0AKKukD9JmzZqZZz23tDT+a/66AACEK+mDtH379kXnl5Y2mWjDhg3mufgaUwAAwpH0Qdq4cWO56qqrzOu5c+eWuK67GmmPVA927dOnTwxaCABIZEkfpGrUqFHmeeLEibJx48aicu2lPvTQQ+b10KFD2dUIAFBmrjmPNDMzU6ZMmSKVK1eW66+/3iyHWb58ufz000/SrVs3Wbp0KZvWA0CS27hxY1EHSn311Vfyww8/mNHLlJSUovIFCxZIo0aNwvqerglS9cYbb8i0adNk8+bN5ui0Fi1ayIABA+SRRx6RKlWqxLp5AIAIW7lypfTs2fOc9XROTbgTUF0VpAAAOM0V90gBAIgUghQAAAsEKQAAFghSAAAsEKQAAFggSAEAsECQAgBggSAFAMACQQoAgAWCFAAACwQpkMDGjx8vHo9HunTpEvL6iBEjzPV27drJjz/+GPX2AW7AXrtAAjt+/Li0bNlS8vPzJScnR2677baiaxMmTDBHCF522WWyatUqadCgQUzbCiQreqRAAtOj/5599lnz+sknn5QzZ86Y1zNmzDAh2rx5c3NcICEKRA5BCiS4gQMHSocOHWTHjh3yyiuvyJw5c2TIkCHmbEUN0eJnLKq33npLKlSoIN9++21Y3//LL7+UBx980PwMPc833KOlALdgaBdIojMW69SpI0eOHDHPOpzbqlWrEnW/+eYb2bdvn3Ts2DGs7/3mm2+aYO7cubM5o1HvtX799dcR+FMAiYkgBZJEt27dZO3atVKjRg0TojrByAlnz541PVilPdN33nmHIAWKYWgXSAKzZ8+WdevWmdcnT56UmjVrllq3cePG8vTTT4f9vf0hCiA0/gsBEty8efPkvvvuk7p168odd9whp06dkuHDh4ese+DAASkoKHCstwqAIAUS2uLFi+Wuu+6SatWqyZIlS2TmzJkmUHUpjA7zBtuyZYt5btu2bQxaCyQnghRIUO+//75kZGRIpUqVZNGiRWbyUO3atc2yFzVs2LCQQarDvsy8BZxDkAIJ6KOPPpK+fftKYWGh5ObmyrXXXlt0bejQodK0aVNZv369vPbaayWC9MorrzS7HQFwBkEKJJitW7fKzTffLP/3f/9n1oz27t074HrVqlVl3Lhx5vXIkSPN5KPiQcqwLuAsghRIMG3atDGThnQXIx3aLW2TBl3Zpus+NVjV6dOnZdu2bQQp4DCCFHCJ7du3mxm9wUGqa0J1qLe0JTHa89XJS/rYtWtXwPs9e/ZEqfVA/KoU6wYAiA4d1tU1oa1btw4oP3bsmHlu2LBhyK/77rvv5Pbbbw8o87/X9auDBw+OWJuBRMDORoDLzZo1y9xL1d7lBRdcEOvmAAmHoV3A5VavXi2PPPIIIQqUEz1SAAAs0CMFAMACQQoAgAWCFAAACwQpAAAWCFIAACwQpAAAWCBIAQCwQJACAGCBIAUAwAJBCgCABYIUAAALBCkAAFJ+/w8xdIJVk4Q98AAAAABJRU5ErkJggg==",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "lattice = qmcpy.Lattice(dimension=2) #define a discrete LD distribution based on an integration lattice\n",
- "print(lattice) #print the properties of the lattice object\n",
- "n = 16 #number of points to generate\n",
- "points = lattice.gen_samples(n) #construct some points\n",
- "print(f'\\nLD Lattice Points with shape {points.shape}\\n'+str(points)) #these points have 15 significant digit precision but only three digits are shown\n",
- "plot_successive_points(lattice,'Lattice',n)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "WztUPDqptYOp"
- },
- "source": [
- "Rerunning the commands above yields a different sequence of points because these points are _randomly shifted_ modulo 1.\n",
- "\n",
- "We may also construct a subsequence of points in the middle of the sequence. Note that the points below match those above."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "wTM9Mi5otlZF",
- "outputId": "b1b5e967-e8cc-4f32-c3c3-307cacfb51fa"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "LD Lattice Points with shape (12, 2) \n",
- "[[0.26948802 0.60629441]\n",
- " [0.76948802 0.10629441]\n",
- " [0.51948802 0.35629441]\n",
- " [0.01948802 0.85629441]\n",
- " [0.20698802 0.91879441]\n",
- " [0.70698802 0.41879441]\n",
- " [0.45698802 0.66879441]\n",
- " [0.95698802 0.16879441]\n",
- " [0.33198802 0.29379441]\n",
- " [0.83198802 0.79379441]\n",
- " [0.58198802 0.04379441]\n",
- " [0.08198802 0.54379441]]\n"
- ]
- }
- ],
- "source": [
- "more_points = lattice.gen_samples(n_min=4,n_max=n) #get more points\n",
- "print('LD Lattice Points with shape',more_points.shape,'\\n'+str(more_points))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "aR5Z5rSMFBN9"
- },
- "source": [
- "Each $d$-dimensional point is _one row_ in the array.\n",
- "\n",
- "As we increase the number of points, they fill $[0,1]^d$ evenly. The next points are placed in between the existing points. Here we illustrate with $d=2$."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 321
- },
- "id": "sEF-IqqeckC-",
- "outputId": "991b6b14-442f-4b30-bb87-b8c4b6e3be5b"
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_successive_points(lattice,'Lattice',n_cols=5)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "14UexIYSJr2r"
- },
- "source": [
- "### IID uniform points do not fill space as well\n",
- "\n",
- "Contrast this with independent and identically distributed (IID) points. Although successive points fill the square, they do so without knowledge of the others and produce clusters and gaps. Think of it this way\n",
- "\n",
- "* LD = evenly spread\n",
- "* IID = points do not know about each other\n",
- "\n",
- "(Since the first parameter in the ``DiscreteDistribution`` object is the dimension, you need not identify it.)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "rWJEGbqPJ4Ap",
- "outputId": "84fc821b-f4a9-4b27-895c-b5ee710f05c5"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "IIDStdUniform (AbstractIIDDiscreteDistribution)\n",
- " d 2^(1)\n",
- " replications 1\n",
- " entropy 151929946340103232908357805579611369175\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "iid = qmcpy.IIDStdUniform(2) #standard uniform IID random vector generator from NumPy\n",
- "print(iid) #print the properties of iid\n",
- "plot_successive_points(iid,'IID Uniform',n_cols=5)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "57P6amgaMV36"
- },
- "source": [
- "### Rows and columns are _not_ interchangeable for LD\n",
- "\n",
- "For LD sequences we must differentiate between the cooordinates of the point (column) and which point (row). The transpose of an LD array is _not_ LD. This differs from IID multivariate points with IID marginals.\n",
- "\n",
- "In the example below we reverse the roles of the rows and columns. The transposed lattice points do not fill space at all, while the transposed IID points are as good as the originals."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 346
- },
- "id": "TluhjiIyM12p",
- "outputId": "56834502-f203-4630-8755-d7f278b3f47f"
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "d = 16 #dimension\n",
- "n = 2 #number of points\n",
- "lattice = qmcpy.Lattice(d) #define a discrete LD distribution based on an integration lattice\n",
- "lattice_pts = lattice.gen_samples(n) #the first parameter in the .gen_samples method is the number of points\n",
- "iid = qmcpy.IIDStdUniform(d)\n",
- "iid_pts = iid.gen_samples(n)\n",
- "fig,ax = pyplot.subplots(nrows=1,ncols=2,figsize=(10,5.5))\n",
- "ax[0].scatter(lattice_pts[0,0:d],lattice_pts[1,0:d],color='b')\n",
- "ax[0].set_title('Transposed Lattice Points')\n",
- "ax[1].scatter(iid_pts[0,0:d],iid_pts[1,0:d],color='b')\n",
- "ax[1].set_title('Transposed IID Points')\n",
- "for ii in range(2):\n",
- " ax[ii].set_xlim([0,1]); ax[ii].set_xticks([0,1]); ax[ii].set_xlabel('$x_{1,j}$')\n",
- " ax[ii].set_ylim([0,1]); ax[ii].set_yticks([0,1]); ax[ii].set_ylabel('$x_{2,j}$')\n",
- " ax[ii].set_aspect(1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "5TUH0dtfICP8"
- },
- "source": [
- "### Sobol' points\n",
- "\n",
- "Another LD sequence is the Sobol' points. Again, new points fill in the gaps between existing points. Since these are randomly digitally shifted Sobol' points, rerunning this command gives a different set of points. For Sobol' points as well, ``n`` should normally be a power of 2."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 753
- },
- "id": "iZN7mN98ktsR",
- "outputId": "b20e7b2e-229e-4d5d-e271-345553f2eedb"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "DigitalNetB2 (AbstractLDDiscreteDistribution)\n",
- " d 2^(1)\n",
- " replications 1\n",
- " randomize LMS DS\n",
- " gen_mats_source joe_kuo.6.21201.txt\n",
- " order RADICAL INVERSE\n",
- " t 63\n",
- " alpha 1\n",
- " n_limit 2^(32)\n",
- " entropy 73208827127383995610457310988530317873\n",
- "\n",
- "LD Sobol' Points with shape (16, 2)\n",
- "[[0.9431598 0.47365992]\n",
- " [0.22125498 0.72515188]\n",
- " [0.66129464 0.90348679]\n",
- " [0.37738574 0.15491697]\n",
- " [0.79964553 0.60309908]\n",
- " [0.02104669 0.35362196]\n",
- " [0.58028993 0.0173281 ]\n",
- " [0.36467173 0.76778926]\n",
- " [0.88650748 0.81477058]\n",
- " [0.16853181 0.06430855]\n",
- " [0.72964997 0.30712722]\n",
- " [0.44965495 0.55660491]\n",
- " [0.87190807 0.20193054]\n",
- " [0.0894106 0.95049973]\n",
- " [0.52754482 0.67862835]\n",
- " [0.30804326 0.4271372 ]]\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "ld = qmcpy.Lattice(2)\n",
- "gaussian_ld = qmcpy.Gaussian(ld, mean=[3,2], covariance=[[9,5], [5,4]]) #specify the desired mean and covariance of your multivariate Gaussian distribution\n",
- "points = gaussian_ld.gen_samples(2**8)\n",
- "print(gaussian_ld)\n",
- "print(f'\\nGaussian LD Points with shape {points.shape}\\n'+str(points)) #these points have 15 significant digit precision but only three digits are shown\n",
- "plot_successive_points(gaussian_ld,'Gaussian LD',first_n=2**8,xlim=[-6,12],ylim=[-2,6])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "lcgMPIqo4i0-"
- },
- "source": [
- "[TransformedLD]: https://arxiv.org/abs/2004.09887 \"Li, Y., Kang, L., and Hickernell, F. J. Is a Transformed Low Discrepancy Design Also Low Discrepancy? in Contemporary Experimental Design, Multivariate Analysis and Data Mining, Festschrift in Honour of Professor Kai-Tai Fang (J. Fan and J. Pan, eds.), p. 69–92, 2020.\"\n",
- "\n",
- "Transformations of low discrepancy sequences often have good properties, but may not be low discrepancy themselves, depending on your definition of discrepancy as shown by [Yiou Li and Lulu Kang][TransformedLD]."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "azx_-HNzb6m5"
- },
- "source": [
- "**_Pause for questions_**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "iaAiqS7ccQ2v"
- },
- "source": [
- "## Integration\n",
- "\n",
- "[Keister]: https://aip.scitation.org/doi/pdf/10.1063/1.168565 \"Keister, B. D. Multidimensional Quadrature Algorithms. Computers in Physics 10, 119–122 (1996).\"\n",
- "\n",
- "Cubature—the approximation of multivariate integrals—is an important application area for QMC. To solve this problem we need the ``Integrand`` and ``StoppingCriterion`` classes.\n",
- "\n",
- "Consider the followng $d$-variate integral due to [Keister][Keister]:\n",
- "\\begin{equation*}\n",
- "\\mu = \\int_{\\mathbb{R}^d} \\cos(\\lVert\\boldsymbol{t}\\rVert) \\exp( - \\lVert \\boldsymbol{t} \\rVert^2) \\, \\mathrm{d} \\boldsymbol{t},\n",
- "\\end{equation*}\n",
- "where $\\lVert \\cdot \\rVert$ is the Euclidean norm. To approximate this integral via (Q)MC methods, it needs to be written as the integral with respect to a probability measure, e.g.,\n",
- "\\begin{equation*}\n",
- "\\mu = \\int_{\\mathbb{R}^d} \\underbrace{\\pi^{d/2} \\cos(\\lVert\\boldsymbol{t}\\rVert)}_{g(\\boldsymbol{t})} \\; \\underbrace{\\pi^{-d/2} \\exp( - \\lVert \\boldsymbol{t} \\rVert^2) \\, \\mathrm{d} \\boldsymbol{t}}_{\\mathcal{N}(\\boldsymbol{0}_d,\\mathsf{I}_d/2) \\text{ measure}}.\n",
- "\\end{equation*}\n",
- "Using transformation techniques highlighted above, this integral can be further transformed to an integral over the unit cube, which is suitable for certain stopping criteria:\n",
- "\\begin{equation*}\n",
- "\\mu = \\int_{[0,1]^d} \\underbrace{\\pi^{d/2} \\cos\\left(\\sqrt{ \\frac 12 \\sum_{j=1}^d \\bigl[\\Phi^{-1}(x_j)\\bigr]^2}\\right)}_{f(\\boldsymbol{x})} \\, \\rm d \\boldsymbol{x}.\n",
- "\\end{equation*}\n",
- "\n",
- "Although it may seem counter-intuitive, we set up our numerical problem by\n",
- "\n",
- "* first choosing the ``DiscreteDistribution`` object,\n",
- "* next the ``TrueMeasure`` object,\n",
- "* thirdly choosing ``Integrand`` object, and\n",
- "* finally the ``StoppingCriterion`` object."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "id": "_w1FeeDRcbok"
- },
- "outputs": [],
- "source": [
- "d = 5 #coded as parameters so that\n",
- "tol = 1e-3 #you can change here and propagate them through this example\n",
- "lattice = qmcpy.Lattice(d)\n",
- "gaussian_lattice = qmcpy.Gaussian(lattice, mean = 0, covariance = 1/2) #mean and covariance of the distribution identified above"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "HXqFIrY-kKky"
- },
- "source": [
- "### ``Integrand`` objects\n",
- "\n",
- "The ``TrueMeasure`` object becomes input to an ``Integrand`` object, which transforms our original integrand, $g$, to our eventual integrand, $f$. This transformation relies on the ``TrueMeasure`` object along with its corresponding ``DiscreteDistribution`` object. The object ``qmcpy.Keister`` has already been coded as a use case in ``qmcpy``."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "id": "x4xC0suLkGCf"
- },
- "outputs": [],
- "source": [
- "keister = qmcpy.Keister(gaussian_lattice) #transform the original integrand to the eventual one"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "lerpTkUtkMNn"
- },
- "source": [
- "### ``StoppingCriterion`` objects and the ``integrate`` method\n",
- "\n",
- "[CubQMCLatticeG]: https://arxiv.org/abs/1411.1966 \"Jiménez Rugama, Ll. A. & Hickernell, F. J. Adaptive Multidimensional Integration Based on Rank-1 Lattices in Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014 (eds Cools, R. & Nuyens,D.) 163.arXiv:1411.1966 (Springer-Verlag, Berlin, 2016), 407–422.\"\n",
- "\n",
- "Determining the sample size needed requires a stopping criterion, which typically depends on the error tolerance, ``abs_tol``. The stopping criterion attempts to produce the answer satisfying the error tolerance with not much more work than is truly needed. There are several ``StoppingCriterion`` objects available, but they tend to work for specific LD sequences. This one comes from [Tony Jiménez Rugama][CubQMCLatticeG]. It takes as its input the ``Integrand`` object, which carries information about the ``TrueMeasure`` object and its ``DiscreteDistribution`` object."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "id": "xNaRz44KkGZt"
- },
- "outputs": [],
- "source": [
- "keister_lattice_gauss_g = qmcpy.CubQMCLatticeG(keister, abs_tol = tol) #using Tony's stopping criterion"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "h09IBoHscjaY"
- },
- "source": [
- "Invoking the ``integrate`` method returns the numerical solution and a data object. Printing the data object provides a neat summary of the integration problem. For details of the output fields, see the online, searchable QMCPy Documentation at [https://qmcpy.readthedocs.io/](https://qmcpy.readthedocs.io/en/latest/algorithms.html#module-qmcpy.integrand.keister)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "bvo8WUAociQk",
- "outputId": "e355bb54-2e9c-4721-91b0-1f32ab23a994"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Data (Data)\n",
- " solution 1.135\n",
- " comb_bound_low 1.135\n",
- " comb_bound_high 1.136\n",
- " comb_bound_diff 0.001\n",
- " comb_flags 1\n",
- " n_total 2^(17)\n",
- " n 2^(17)\n",
- " time_integrate 0.042\n",
- "CubQMCLatticeG (AbstractStoppingCriterion)\n",
- " abs_tol 0.001\n",
- " rel_tol 0\n",
- " n_init 2^(10)\n",
- " n_limit 2^(30)\n",
- "Keister (AbstractIntegrand)\n",
- "Gaussian (AbstractTrueMeasure)\n",
- " mean 0\n",
- " covariance 2^(-1)\n",
- " decomp_type PCA\n",
- " transform Gaussian (AbstractTrueMeasure)\n",
- " mean 0\n",
- " covariance 2^(-1)\n",
- " decomp_type PCA\n",
- "Lattice (AbstractLDDiscreteDistribution)\n",
- " d 5\n",
- " replications 1\n",
- " randomize SHIFT\n",
- " gen_vec_source kuo.lattice-33002-1024-1048576.9125.txt\n",
- " order RADICAL INVERSE\n",
- " n_limit 2^(20)\n",
- " entropy 38563684764170563855999902143753109741\n"
- ]
- }
- ],
- "source": [
- "solution, data = keister_lattice_gauss_g.integrate()\n",
- "print(data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Tp1gEJNvHrCH"
- },
- "source": [
- "#### Fixed sample budget computation\n",
- "\n",
- "If you are not concerned about meeting an error tolerance but can only afford ``n_max`` function values, then you can set an ``abs_tol`` small enough and set ``n_max`` to your desired sample size. You will get an error bound."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "qqQBnGnpdQGG",
- "outputId": "7d9a57a6-c4fb-40f8-f4cc-45ef1ed8940d"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Data (Data)\n",
- " solution 1.129\n",
- " comb_bound_low 1.116\n",
- " comb_bound_high 1.142\n",
- " comb_bound_diff 0.026\n",
- " comb_flags 0\n",
- " n_total 2^(12)\n",
- " n 2^(12)\n",
- " time_integrate 0.003\n",
- "CubQMCLatticeG (AbstractStoppingCriterion)\n",
- " abs_tol 1.00e-06\n",
- " rel_tol 0\n",
- " n_init 2^(10)\n",
- " n_limit 2^(12)\n",
- "Keister (AbstractIntegrand)\n",
- "Gaussian (AbstractTrueMeasure)\n",
- " mean 0\n",
- " covariance 2^(-1)\n",
- " decomp_type PCA\n",
- " transform Gaussian (AbstractTrueMeasure)\n",
- " mean 0\n",
- " covariance 2^(-1)\n",
- " decomp_type PCA\n",
- "Lattice (AbstractLDDiscreteDistribution)\n",
- " d 5\n",
- " replications 1\n",
- " randomize SHIFT\n",
- " gen_vec_source kuo.lattice-33002-1024-1048576.9125.txt\n",
- " order RADICAL INVERSE\n",
- " n_limit 2^(20)\n",
- " entropy 38563684764170563855999902143753109741\n"
- ]
- }
- ],
- "source": [
- "warnings.simplefilter(\"ignore\")\n",
- "keister_lattice_gauss_g_small_n = qmcpy.CubQMCLatticeG(keister, abs_tol = 1e-6, n_limit = 2**12) #the default n_max is 2**35\n",
- "solution, data = keister_lattice_gauss_g_small_n.integrate()\n",
- "print(data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "TfKJMMnwo_6k"
- },
- "source": [
- "#### QMC is much faster than MC. (Q)MC cost is mostly dimension dependent\n",
- "\n",
- "Run this next code block to see how the run time and the number of function evaluations increase as the tolerance decreases. QMC using LD sequences uses much less time and much fewer function values than MC using IID sequences.\n",
- "\n",
- "Tensor product rules have a time or function value cost that is $\\mathcal{O}(\\varepsilon^{-d/r})$, where $\\varepsilon$ is the error tolerance and $r$ is bounded above by both the smoothness of the integrand and the quality of the algorithm. Such rules have a _curse of dimensionality_ because their cost blows up exponentially with dimension.\n",
- "\n",
- "Unlike tensor product cubature rules, the cost of (Q)MC cubature is essentially dimension independent: $\\mathcal{O}(\\varepsilon^{-2})$ for IID MC and typically $\\mathcal{O}(\\varepsilon^{-1-\\delta})$ for QMC. Although Q(MC) is not particularly fast, its performance usually does not degrade as the number of variables of in the integrand increases."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 436
- },
- "id": "EzaSx-P5UWHY",
- "outputId": "2dc5b0eb-310c-4aa1-b185-f6024a1221ea"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Keister integral = 1.1356452963607024\n",
- "\n"
- ]
- },
- {
- "data": {
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",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "d = 5; tol = 2.5e-3 #re-construct the example\n",
- "#d = 7; tol = 3e-3 #if you change the dimension\n",
- "#d = 10; tol = 3e-2 #you may also wish to change the tolerance, since the value of the integral changes\n",
- "ld_keister = qmcpy.Keister(qmcpy.Gaussian(qmcpy.Lattice(d), mean = 0, covariance = 1/2)) #mean and covariance of the distribution identified above\n",
- "iid_keister = qmcpy.Keister(qmcpy.Gaussian(qmcpy.IIDStdUniform(d), mean = 0, covariance = 1/2))\n",
- "\n",
- "n_tol = 7\n",
- "ii_iid = 2 #make this larger to reduce the time required\n",
- "tol_vec = [tol*2**(ii) for ii in range(n_tol)] #initialize\n",
- "ld_time = [0]*n_tol; ld_n = [0]*n_tol #low discrepancy time and number of function values\n",
- "iid_time = [0]*n_tol; iid_n = [0]*n_tol #IID time and number of function values\n",
- "for ii in range(n_tol):\n",
- " solution, data = qmcpy.CubQMCLatticeG(ld_keister, abs_tol = tol_vec[ii]).integrate()\n",
- " if ii == 0:\n",
- " print(f'\\nKeister integral = {solution}\\n')\n",
- " ld_time[ii] = data.time_integrate\n",
- " ld_n[ii] = data.n_total\n",
- " if ii >= ii_iid:\n",
- " solution, data = qmcpy.CubMCG(iid_keister, abs_tol = tol_vec[ii]).integrate()\n",
- " iid_time[ii] = data.time_integrate\n",
- " iid_n[ii] = data.n_total\n",
- "fig,ax = pyplot.subplots(nrows=1,ncols=2,figsize=(13,5.5))\n",
- "ax[0].scatter(tol_vec[0:n_tol],ld_time[0:n_tol],color='b');\n",
- "ax[0].plot(tol_vec[0:n_tol],[(ld_time[0]*tol_vec[0])/tol_vec[jj] for jj in range(n_tol)],color='b')\n",
- "ax[0].scatter(tol_vec[ii_iid:n_tol],iid_time[ii_iid:n_tol],color='g');\n",
- "ax[0].plot(tol_vec[ii_iid:n_tol],[(iid_time[ii_iid]*(tol_vec[ii_iid]**2))/(tol_vec[jj]**2) for jj in range(ii_iid,n_tol)],color='g')\n",
- "ax[0].set_ylim([0.001,1000]); ax[0].set_ylabel('Time (s)')\n",
- "ax[1].scatter(tol_vec[0:n_tol],ld_n[0:n_tol],color='b');\n",
- "ax[1].plot(tol_vec[0:n_tol],[(ld_n[0]*tol_vec[0])/tol_vec[jj] for jj in range(n_tol)],color='b')\n",
- "ax[1].scatter(tol_vec[ii_iid:n_tol],iid_n[ii_iid:n_tol],color='g');\n",
- "ax[1].plot(tol_vec[ii_iid:n_tol],[(iid_n[ii_iid]*(tol_vec[ii_iid]**2))/(tol_vec[jj]**2) for jj in range(ii_iid,n_tol)],color='g')\n",
- "ax[1].set_ylim([1e2,1e8]); ax[1].set_ylabel('n')\n",
- "for ii in range(2):\n",
- " ax[ii].set_xlim([tol,100*tol]); ax[ii].set_xlabel('Tolerance, '+r'$\\varepsilon$')\n",
- " ax[ii].set_xscale('log'); ax[ii].set_yscale('log')\n",
- " ax[ii].legend([r'$\\mathcal{O}(\\varepsilon^{-1})$',r'$\\mathcal{O}(\\varepsilon^{-2})$','LD','IID'],frameon=False)\n",
- " ax[ii].set_aspect(0.35)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "xjYc-Lco8mfm"
- },
- "source": [
- "### Alternatives for ``StoppingCriterion``\n",
- "\n",
- "Other ``StoppingCriterion`` objects are available. Most are tied to particular ``DiscreteDistribution`` objects. For LD points one can use replications and the Central Limit Theorem (CLT)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "jlMvENFj83_Q",
- "outputId": "5a8ab21d-fbd1-4bf8-a2af-15f617c7b7da"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Data (Data)\n",
- " solution 1.135\n",
- " comb_bound_low 1.134\n",
- " comb_bound_high 1.136\n",
- " comb_bound_diff 0.002\n",
- " comb_flags 1\n",
- " n_total 122880\n",
- " n 122880\n",
- " n_rep 2^(13)\n",
- " time_integrate 0.029\n",
- "CubQMCRepStudentT (AbstractStoppingCriterion)\n",
- " inflate 1\n",
- " alpha 0.010\n",
- " abs_tol 0.003\n",
- " rel_tol 0\n",
- " n_init 2^(8)\n",
- " n_limit 2^(30)\n",
- "Keister (AbstractIntegrand)\n",
- "Gaussian (AbstractTrueMeasure)\n",
- " mean 0\n",
- " covariance 2^(-1)\n",
- " decomp_type PCA\n",
- " transform Gaussian (AbstractTrueMeasure)\n",
- " mean 0\n",
- " covariance 2^(-1)\n",
- " decomp_type PCA\n",
- "Lattice (AbstractLDDiscreteDistribution)\n",
- " d 5\n",
- " replications 15\n",
- " randomize SHIFT\n",
- " gen_vec_source kuo.lattice-33002-1024-1048576.9125.txt\n",
- " order RADICAL INVERSE\n",
- " n_limit 2^(20)\n",
- " entropy 275034244784380212109446453686580990022\n"
- ]
- }
- ],
- "source": [
- "lattice_rep = qmcpy.Lattice(d,replications=15)\n",
- "gaussian_lattice_rep = qmcpy.Gaussian(lattice_rep, mean = 0, covariance = 1/2)\n",
- "keister_rep = qmcpy.Keister(gaussian_lattice_rep)\n",
- "keister_lattice_gauss_CLT = qmcpy.CubQMCCLT(keister_rep, abs_tol = tol) #using a CLT stopping criterion with random replications\n",
- "solution, data = keister_lattice_gauss_CLT.integrate()\n",
- "print(data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "HCadgtCYb_AJ"
- },
- "source": [
- "This answer agrees with the one above."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "n1BT5mFBiwA0"
- },
- "source": [
- "### Alternatives for ``TrueMeasure``\n",
- "\n",
- "The Keister integrand may also be solved using a Lebesgue ``TrueMeasure`` object:\n",
- "$$\\mu = \\int_{\\mathbb{R}^d} \\underbrace{\\cos(\\lVert\\boldsymbol{t}\\rVert) \\exp( - \\lVert\\boldsymbol{t}\\rVert^2)}_{g(\\boldsymbol{t})} \\, \\underbrace{\\mathrm{d} \\boldsymbol{t}}_{\\text{Lebesgue measure}}$$\n",
- "The ``TrueMeasure`` object contains the appropriate information so that the ``Integrand`` object can obtain the correct eventual integrand, $f$, in terms of the original integrand, $g$. \n",
- "\n",
- "Since ``TrueMeasure`` is not a probability measure, so it cannot be mimicked, but it can be used to solve the integration problem. The ``Integrand`` object maps the problem using an affine transformation for finite boxes and an inverse normal distribution function transformation for $\\mathbb{R}^d$.\n",
- "\n",
- "The code below also shows how to take our own integrand defined with a simple input and output, and turn it into a ``qmcpy`` ready integrand using the ``qmcpy.CustomFun`` object."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "Y8FUgw9PVaMt",
- "outputId": "5518418c-da80-41b8-c0be-7f7bc51cec3f"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Data (Data)\n",
- " solution 1.136\n",
- " comb_bound_low 1.133\n",
- " comb_bound_high 1.138\n",
- " comb_bound_diff 0.004\n",
- " comb_flags 1\n",
- " n_total 2^(16)\n",
- " n 2^(16)\n",
- " time_integrate 0.030\n",
- "CubQMCNetG (AbstractStoppingCriterion)\n",
- " abs_tol 0.003\n",
- " rel_tol 0\n",
- " n_init 2^(10)\n",
- " n_limit 2^(35)\n",
- "CustomFun (AbstractIntegrand)\n",
- "Lebesgue (AbstractTrueMeasure)\n",
- " transform Gaussian (AbstractTrueMeasure)\n",
- " mean 0\n",
- " covariance 1\n",
- " decomp_type PCA\n",
- "DigitalNetB2 (AbstractLDDiscreteDistribution)\n",
- " d 5\n",
- " replications 1\n",
- " randomize LMS DS\n",
- " gen_mats_source joe_kuo.6.21201.txt\n",
- " order RADICAL INVERSE\n",
- " t 63\n",
- " alpha 1\n",
- " n_limit 2^(32)\n",
- " entropy 91580573475794728146679265749810645603\n"
- ]
- }
- ],
- "source": [
- "def my_Keister(x): #this could be a functional of a solution to a PDE with random coefficients\n",
- " #or anything that you would like\n",
- " \"\"\"\n",
- " x: nxd numpy ndarray\n",
- " n samples\n",
- " d dimensions\n",
- "\n",
- " returns n-vector of the Keister function\n",
- " evaluated at the n input samples\n",
- " \"\"\"\n",
- " d = x.shape[1]\n",
- " norm = np.sqrt((x**2).sum(1))\n",
- " k = np.cos(norm)*np.exp(-norm**2)\n",
- " return k #size n vector\n",
- "\n",
- "ld = qmcpy.Sobol(d) #choose the LD points\n",
- "lebesgue = qmcpy.Lebesgue(qmcpy.Gaussian(ld)) #now choose the Lebesgue distribution\n",
- "f = qmcpy.CustomFun(lebesgue, g=my_Keister)\n",
- "keister_lebesgue_ld_g = qmcpy.CubQMCSobolG(f, abs_tol = tol) #the stopping criterion does need to match the LD points\n",
- "solution, data = keister_lebesgue_ld_g.integrate()\n",
- "print(data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "_2SFs_6Ma-9q"
- },
- "source": [
- "This answer agrees with the answers above for the same integration problem.\n",
- "\n",
- "The initial ``DiscreteDistribution`` does not need to mimic the standard uniform distribution as this next example shows."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "8a0KABTT3Lai",
- "outputId": "85d5d973-00f1-4945-be11-83e84da6426a"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Data (Data)\n",
- " solution 1.127\n",
- " bound_low 1.099\n",
- " bound_high 1.155\n",
- " bound_diff 0.056\n",
- " n_total 1694184\n",
- " time_integrate 0.361\n",
- "CubMCCLT (AbstractStoppingCriterion)\n",
- " abs_tol 0.025\n",
- " rel_tol 0\n",
- " n_init 2^(10)\n",
- " n_limit 2^(30)\n",
- " inflate 1.200\n",
- " alpha 0.010\n",
- "CustomFun (AbstractIntegrand)\n",
- "Lebesgue (AbstractTrueMeasure)\n",
- " transform Gaussian (AbstractTrueMeasure)\n",
- " mean 0\n",
- " covariance 1\n",
- " decomp_type PCA\n",
- "IIDStdUniform (AbstractIIDDiscreteDistribution)\n",
- " d 5\n",
- " replications 1\n",
- " entropy 110067297374675390351789060258764096093\n"
- ]
- }
- ],
- "source": [
- "iid = qmcpy.IIDStdUniform(d) #choose the LD points\n",
- "lebesgue = qmcpy.Lebesgue(qmcpy.Gaussian(iid)) #now choose the Lebesgue distribution\n",
- "f = qmcpy.CustomFun(lebesgue, g=my_Keister)\n",
- "keister_lebesgue_ld_g = qmcpy.CubMCCLT(f, abs_tol = 10*tol) #the stopping criterion does need to match the points\n",
- "solution, data = keister_lebesgue_ld_g.integrate()\n",
- "print(data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "py0Q1ysPyhdn"
- },
- "source": [
- "### Multi-level (Q)MC\n",
- "\n",
- "When the dimesion of the multivariate integral is high, multi-level (Quasi-)Monte Carlo (ML(Q)MC) methods may save computation time. The cost of one integrand value depends on the number of input variables, $d$, which corresponds to the dimension of our integration problem. ML(Q)MC methods allow us attain our accuracy requirements by evaluating low dimensional integrands many times and high dimensional integrands much fewer times.\n",
- "\n",
- "High or infinte dimesional integration problems arise when computing the expectations of quantities coming stochastic differential equations (SDEs). These problems arise in finance applications. The dimension of the integrand typically refers to the number of time steps used to discretize the SDE.\n",
- "\n",
- "Here are some parameters for the Asian option examples below. Changing them here allows you to compare the run times of these examples in a fair way."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "id": "V_IXXRnNuBAa"
- },
- "outputs": [],
- "source": [
- "abs_tol = .05 #a nickel\n",
- "n_time_steps = 64 #being a power of 2 will help for multi-level\n",
- "\n",
- "options = { #there should be nothing magic about these choices\n",
- " 'interest_rate': .05,\n",
- " 'volatility': .5,\n",
- " 'start_price': 30,\n",
- " 'strike_price': 30\n",
- "}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "byuaaQO4rYpS"
- },
- "source": [
- "#### Single Level MC\n",
- "\n",
- "The vanilla way to solve this problem is IID Monte Carlo. The fair price of the option is an expectation or integral, and the dimension is the number of time steps of used to discretize the Brownian motion that drives the SDE describing the price of the underlying asset. The number of time steps should be fairly large.\n",
- "\n",
- "[CubMCG]: https://arxiv.org/abs/1208.4318 \"Fred J. Hickernell, Lan Jiang, Yuewei Liu, and Art B. Owen, 'Guaranteed conservative fixed width confidence intervals via Monte Carlo sampling,' Monte Carlo and Quasi-Monte Carlo Methods 2012 (J. Dick, F.Y. Kuo, G. W. Peters, and I. H. Sloan, eds.), pp. 105-128, Springer-Verlag, Berlin, 2014. DOI: 10.1007/978-3-642-41095-6_5\"\n",
- "\n",
- "[This paper by Lan Jiang and collaborators][CubMCG] describes the stopping criterion."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "DoxNSMfFl5C-",
- "outputId": "c093118a-cc82-4bf3-81ed-2f85a69ff446"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Data (Data)\n",
- " solution 3.686\n",
- " bound_low 3.636\n",
- " bound_high 3.736\n",
- " bound_diff 0.100\n",
- " n_total 225848\n",
- " time_integrate 0.834\n",
- "CubMCG (AbstractStoppingCriterion)\n",
- " abs_tol 0.050\n",
- " rel_tol 0\n",
- " n_init 2^(10)\n",
- " n_limit 2^(30)\n",
- " inflate 1.200\n",
- " alpha 0.010\n",
- " kurtmax 1.478\n",
- "AsianOption (AbstractIntegrand)\n",
- " option ASIAN\n",
- " call_put CALL\n",
- " volatility 2^(-1)\n",
- " start_price 30\n",
- " strike_price 30\n",
- " interest_rate 0.050\n",
- " t_final 1\n",
- " asian_mean ARITHMETIC\n",
- "BrownianMotion (AbstractTrueMeasure)\n",
- " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
- " drift 0\n",
- " mean [0. 0. 0. ... 0. 0. 0.]\n",
- " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
- " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
- " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
- " ...\n",
- " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
- " decomp_type PCA\n",
- " transform BrownianMotion (AbstractTrueMeasure)\n",
- " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
- " drift 0\n",
- " mean [0. 0. 0. ... 0. 0. 0.]\n",
- " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
- " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
- " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
- " ...\n",
- " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
- " decomp_type PCA\n",
- "IIDStdUniform (AbstractIIDDiscreteDistribution)\n",
- " d 2^(6)\n",
- " replications 1\n",
- " entropy 14445467058155448935378672762555839489\n"
- ]
- }
- ],
- "source": [
- "iidBrownian = qmcpy.BrownianMotion(qmcpy.IIDStdUniform(n_time_steps))\n",
- "payoff = qmcpy.AsianOption(iidBrownian, **options)\n",
- "IIDstop = qmcpy.CubMCG(payoff,abs_tol=abs_tol)\n",
- "price,data = IIDstop.integrate()\n",
- "print(data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "HzGDIfvanzgk"
- },
- "source": [
- "#### Adaptive Multilevel MC from Mike Giles\n",
- "\n",
- "[CubMCML]: https://pubsonline.informs.org/doi/abs/10.1287/opre.1070.0496 \"Giles, M. Multilevel Monte Carlo Path Simulation. Operations Research 56,607–617 (June 2008).\"\n",
- "\n",
- "[GilesSoftware]: http://people.maths.ox.ac.uk/~gilesm/mlmc/\n",
- "\n",
- "Mike Giles and his collaborators have developed several ML(Q)MC algorithms. The ML IID MC algorithm and stopping criterion implemented here are from [this paper][CubMCML] and [this code][GilesSoftware]. The answer is expected to be different than above, even with the same parameters, as the `MLCallOptions` uses a different discretization. The algorithm considers the SDE for logarithm of the stock price, which allows exact time stepping for constant interest rates and volatirilities, while Giles uses a Milstein discretization for the SDE for the stock price iteself."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "d2ZmrKjyoYY0",
- "outputId": "b662e8fb-5ef3-4960-8ffd-0b9cf41bc94e"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Data (Data)\n",
- " solution 3.681\n",
- " n_total 430326\n",
- " levels 3\n",
- " n_level [341502 61469 27319]\n",
- " mean_level [3.589 0.071 0.021]\n",
- " var_level [39.742 2.575 0.665]\n",
- " cost_per_sample [2. 4. 8.]\n",
- " alpha 1.769\n",
- " beta 1.953\n",
- " gamma 1.000\n",
- " time_integrate 0.077\n",
- "CubMLMC (AbstractStoppingCriterion)\n",
- " rmse_tol 0.019\n",
- " n_init 2^(8)\n",
- " levels_min 2^(1)\n",
- " levels_max 10\n",
- " theta 2^(-1)\n",
- "AsianOption (AbstractIntegrand)\n",
- " option ASIAN\n",
- " call_put CALL\n",
- " volatility 2^(-1)\n",
- " start_price 30\n",
- " strike_price 30\n",
- " interest_rate 0.050\n",
- " t_final 1\n",
- " asian_mean ARITHMETIC\n",
- "BrownianMotion (AbstractTrueMeasure)\n",
- " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
- " drift 0\n",
- " mean [0. 0. 0. ... 0. 0. 0.]\n",
- " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
- " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
- " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
- " ...\n",
- " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
- " decomp_type PCA\n",
- " transform BrownianMotion (AbstractTrueMeasure)\n",
- " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
- " drift 0\n",
- " mean [0. 0. 0. ... 0. 0. 0.]\n",
- " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
- " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
- " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
- " ...\n",
- " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
- " decomp_type PCA\n",
- "IIDStdUniform (AbstractIIDDiscreteDistribution)\n",
- " d 2^(6)\n",
- " replications 1\n",
- " entropy 14445467058155448935378672762555839489\n"
- ]
- }
- ],
- "source": [
- "giles_MLMC_stop = qmcpy.CubMCML(payoff,abs_tol=abs_tol)\n",
- "solution,data = giles_MLMC_stop.integrate()\n",
- "print(data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "HTkrRMi9rk_b"
- },
- "source": [
- "#### Single Level QMC Baseline\n",
- "\n",
- "[CubQMCSobolG]: https://arxiv.org/abs/1410.8615 \"Fred J. Hickernell and Lluis Antoni Jimenez Rugama, 'Reliable adaptive cubature using digital sequences,' Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014 (R. Cools and D. Nuyens, eds.), Springer Proceedings in Mathematics and Statistics, vol. 163, Springer-Verlag, Berlin, 2016, arXiv:1410.8615 [math.NA], pp. 367-383.\"\n",
- "\n",
- "Tony Jiménez's stopping criterion for cubature via Sobol' sequences in [this paper][CubQMCSobolG] does not yet work for multi-level problems. Here it is treating the option pricing problem as a high dimensional integral."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "XtUqkZ1Qr2tH",
- "outputId": "07d0855f-1747-407b-a04c-b6d9cc18c1e1"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Data (Data)\n",
- " solution 3.697\n",
- " comb_bound_low 3.672\n",
- " comb_bound_high 3.721\n",
- " comb_bound_diff 0.049\n",
- " comb_flags 1\n",
- " n_total 2^(10)\n",
- " n 2^(10)\n",
- " time_integrate 0.003\n",
- "CubQMCNetG (AbstractStoppingCriterion)\n",
- " abs_tol 0.050\n",
- " rel_tol 0\n",
- " n_init 2^(10)\n",
- " n_limit 2^(35)\n",
- "AsianOption (AbstractIntegrand)\n",
- " option ASIAN\n",
- " call_put CALL\n",
- " volatility 2^(-1)\n",
- " start_price 30\n",
- " strike_price 30\n",
- " interest_rate 0.050\n",
- " t_final 1\n",
- " asian_mean ARITHMETIC\n",
- "BrownianMotion (AbstractTrueMeasure)\n",
- " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
- " drift 0\n",
- " mean [0. 0. 0. ... 0. 0. 0.]\n",
- " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
- " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
- " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
- " ...\n",
- " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
- " decomp_type PCA\n",
- " transform BrownianMotion (AbstractTrueMeasure)\n",
- " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
- " drift 0\n",
- " mean [0. 0. 0. ... 0. 0. 0.]\n",
- " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
- " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
- " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
- " ...\n",
- " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
- " decomp_type PCA\n",
- "DigitalNetB2 (AbstractLDDiscreteDistribution)\n",
- " d 2^(6)\n",
- " replications 1\n",
- " randomize LMS DS\n",
- " gen_mats_source joe_kuo.6.21201.txt\n",
- " order RADICAL INVERSE\n",
- " t 63\n",
- " alpha 1\n",
- " n_limit 2^(32)\n",
- " entropy 267658111069601541207388819540918772545\n"
- ]
- }
- ],
- "source": [
- "sobol_brownian = qmcpy.BrownianMotion(qmcpy.Sobol(n_time_steps))\n",
- "integrand = qmcpy.AsianOption(sobol_brownian, **options)\n",
- "stopping_criterion = qmcpy.CubQMCSobolG(integrand,abs_tol = abs_tol)\n",
- "solution,data = stopping_criterion.integrate()\n",
- "print(data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ViBnbZ2GQDGK"
- },
- "source": [
- "### Importance Sampling\n",
- "\n",
- "For the option pricing problem, we may add a drift to the Brownian motion as an example of importance sampling.\n",
- "\n",
- "First, we need to change our problem to one for which importance sampling can show some benefit. We consider an out-of-the-money call option."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "id": "Le1wHMsTQana"
- },
- "outputs": [],
- "source": [
- "abs_tol = .001\n",
- "n_time_steps = 64\n",
- "\n",
- "options = {\n",
- " 'interest_rate': .05,\n",
- " 'volatility': .5,\n",
- " 'start_price': 30,\n",
- " 'strike_price': 40 #a larger strike price than before\n",
- "}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "LIqdo4uuQsRd"
- },
- "source": [
- "First we price it as above using single level QMC."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "NeXo-ZgUQ0IN",
- "outputId": "8cabc874-c146-49f0-ea71-2413ee05367d"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Data (Data)\n",
- " solution 1.018\n",
- " comb_bound_low 1.017\n",
- " comb_bound_high 1.019\n",
- " comb_bound_diff 0.002\n",
- " comb_flags 1\n",
- " n_total 2^(15)\n",
- " n 2^(15)\n",
- " time_integrate 0.118\n",
- "CubQMCNetG (AbstractStoppingCriterion)\n",
- " abs_tol 0.001\n",
- " rel_tol 0\n",
- " n_init 2^(10)\n",
- " n_limit 2^(35)\n",
- "AsianOption (AbstractIntegrand)\n",
- " option ASIAN\n",
- " call_put CALL\n",
- " volatility 2^(-1)\n",
- " start_price 30\n",
- " strike_price 40\n",
- " interest_rate 0.050\n",
- " t_final 1\n",
- " asian_mean ARITHMETIC\n",
- "BrownianMotion (AbstractTrueMeasure)\n",
- " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
- " drift 0\n",
- " mean [0. 0. 0. ... 0. 0. 0.]\n",
- " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
- " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
- " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
- " ...\n",
- " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
- " decomp_type PCA\n",
- " transform BrownianMotion (AbstractTrueMeasure)\n",
- " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
- " drift 0\n",
- " mean [0. 0. 0. ... 0. 0. 0.]\n",
- " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
- " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
- " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
- " ...\n",
- " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
- " decomp_type PCA\n",
- "DigitalNetB2 (AbstractLDDiscreteDistribution)\n",
- " d 2^(6)\n",
- " replications 1\n",
- " randomize LMS DS\n",
- " gen_mats_source joe_kuo.6.21201.txt\n",
- " order RADICAL INVERSE\n",
- " t 63\n",
- " alpha 1\n",
- " n_limit 2^(32)\n",
- " entropy 68741035845447190293894879002444698271\n"
- ]
- }
- ],
- "source": [
- "sobol_brownian = qmcpy.BrownianMotion(qmcpy.Sobol(n_time_steps))\n",
- "integrand = qmcpy.AsianOption(sobol_brownian, **options)\n",
- "stopping_criterion = qmcpy.CubQMCSobolG(integrand,abs_tol = abs_tol)\n",
- "solution,data = stopping_criterion.integrate()\n",
- "print(data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "1pgJzDBVRIzH"
- },
- "source": [
- "Next, we introduce an upward drift in the Brownian motion, which produces more stock price paths with positive payoffs. This produces a smaller varation in the integrand and a generally faster run time. (There still remains the question of how to automatically choose an optimal drift.)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "MAXEi_UkRstV",
- "outputId": "f2732860-468e-4861-c693-106a97d6f719"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Data (Data)\n",
- " solution 1.018\n",
- " comb_bound_low 1.018\n",
- " comb_bound_high 1.019\n",
- " comb_bound_diff 0.001\n",
- " comb_flags 1\n",
- " n_total 2^(15)\n",
- " n 2^(15)\n",
- " time_integrate 0.115\n",
- "CubQMCNetG (AbstractStoppingCriterion)\n",
- " abs_tol 0.001\n",
- " rel_tol 0\n",
- " n_init 2^(10)\n",
- " n_limit 2^(35)\n",
- "AsianOption (AbstractIntegrand)\n",
- " option ASIAN\n",
- " call_put CALL\n",
- " volatility 2^(-1)\n",
- " start_price 30\n",
- " strike_price 40\n",
- " interest_rate 0.050\n",
- " t_final 1\n",
- " asian_mean ARITHMETIC\n",
- "BrownianMotion (AbstractTrueMeasure)\n",
- " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
- " drift 0\n",
- " mean [0. 0. 0. ... 0. 0. 0.]\n",
- " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
- " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
- " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
- " ...\n",
- " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
- " decomp_type PCA\n",
- " transform BrownianMotion (AbstractTrueMeasure)\n",
- " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
- " drift 2^(-1)\n",
- " mean [0.008 0.016 0.023 ... 0.484 0.492 0.5 ]\n",
- " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
- " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
- " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
- " ...\n",
- " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
- " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
- " decomp_type PCA\n",
- "DigitalNetB2 (AbstractLDDiscreteDistribution)\n",
- " d 2^(6)\n",
- " replications 1\n",
- " randomize LMS DS\n",
- " gen_mats_source joe_kuo.6.21201.txt\n",
- " order RADICAL INVERSE\n",
- " t 63\n",
- " alpha 1\n",
- " n_limit 2^(32)\n",
- " entropy 195386499743864987571337198121595404839\n"
- ]
- }
- ],
- "source": [
- "sobol_drift_brownian = qmcpy.BrownianMotion(qmcpy.Sobol(n_time_steps), drift = 0.5)\n",
- "integrand = qmcpy.AsianOption(sobol_drift_brownian, **options)\n",
- "stopping_criterion = qmcpy.CubQMCSobolG(integrand,abs_tol = abs_tol)\n",
- "solution,data = stopping_criterion.integrate()\n",
- "print(data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Qx-HQwxacQAA"
- },
- "source": [
- "**_Pause for questions_**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "76dT8RIbKvr3"
- },
- "source": [
- "## Under the hood\n",
- "\n",
- "The structure of ``qmcpy`` is that there are five major classes:\n",
- "\n",
- "* ``DiscreteDistribution`` used to generate LD sequences, primarily on $[0,1]^d$\n",
- "* ``TrueMeasure`` for using these LD sequences to mimic other distributions and to define integrals with respect to other measures\n",
- "* ``Integrand`` to define the integrand for the multivariate integration problems\n",
- "* ``StoppingCriterion`` to determine when the desired accuracy has been reached\n",
- "* ``AccumulateData`` the invisible class used to keep track of important data as you continue to sample\n",
- "\n",
- "We look at some of the important parameters that the corresponding objects have."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "UlHMBejwK4_v"
- },
- "source": [
- "[GAIL]: http://gailgithub.github.io/GAIL_Dev/ \"Sou-Cheng T. Choi, Yuhan Ding, Fred J. Hickernell, Lan Jiang, Lluis Antoni Jimenez Rugama, Da Li, Jagadeeswaran Rathinavel, Xin Tong, Kan Zhang, Yizhi Zhang, and Xuan Zhou, GAIL: Guaranteed Automatic Integration Library (Version 2.3.1) [MATLAB Software], 2020. Available from `http://gailgithub.github.io/GAIL_Dev/`\"\n",
- "\n",
- "[QRNG]: https://CRAN.R-project.org/package=qrng \"Marius Hofert and Christiane Lemieux (2019). qrng: (Randomized) Quasi-Random Number Generators. R package version 0.0-7. `https://CRAN.R-project.org/package=qrng`.\"\n",
- "\n",
- "[OwenHalton]: https://arxiv.org/abs/1706.02808 \"Owen, A. B. 'A randomized Halton algorithm in R,' 2017. arXiv:1706.02808 [stat.CO]\"\n",
- "\n",
- "[MPS]: https://people.cs.kuleuven.be/~dirk.nuyens/qmc-generators/ \"F. Y. Kuo and D. Nuyens. 'Application of quasi-Monte Carlo methods to elliptic PDEs with random diffusion coefficients - a survey of analysis and implementation,' Foundations of Computational Mathematics, 16(6):1631-1696, 2016. ([springer link](https://link.springer.com/article/10.1007/s10208-016-9329-5), [arxiv link](https://arxiv.org/abs/1606.06613))\"\n",
- "\n",
- "[GilesSoftware]: http://people.maths.ox.ac.uk/~gilesm/mlmc/\n",
- "\n",
- "[PyTorch]: https://pytorch.org\n",
- "\n",
- "[LatticeBuilder]: http://simul.iro.umontreal.ca/latbuilder/\n",
- "\n",
- "### LD sequence generators\n",
- "\n",
- "The LD generators (``DiscreteDistribution`` objects) implemented here are drawn from several sources, which are denoted ``backend`` (first listed is the default):\n",
- "- Sobol: [QRNG], [MPS], & [PyTorch]\n",
- "- Lattice: [GAIL] & [MPS], with default generating vectors from [Lattice Builder][LatticeBuilder]\n",
- "- Halton: [Art Owen's][OwenHalton] & [QRNG]\n",
- "- Korobov: [QRNG]\n",
- "\n",
- "We illustrate some of the features of these varous backends and some of the other parameters that you can set."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "M60jJ1nGqzy6"
- },
- "source": [
- "#### Different lattice backends and generators\n",
- "\n",
- "The ``qmcpy.Lattice`` generator using the GAIL and MPS ``backends`` with the same generating vectors yield the same points but in a different order. The ``stopping criterion`` ``qmcpy.CubLatticeG`` requires the GAIL order, but not all stopping criteria do."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 690
- },
- "id": "4WN-EBMh6Jqh",
- "outputId": "03a61a6c-a3ea-4079-ae2d-edf0f0177a5c"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "GAIL Samples\n",
- "[0. 0. 0. 0.]\n",
- "[0.125 0.375 0.375 0.875]\n",
- "[0.25 0.75 0.75 0.75]\n",
- "[0.375 0.125 0.125 0.625]\n",
- "[0.5 0.5 0.5 0.5]\n",
- "[0.625 0.875 0.875 0.375]\n",
- "[0.75 0.25 0.25 0.25]\n",
- "[0.875 0.625 0.625 0.125]\n",
- "\n",
- "\n",
- "MPS Samples\n",
- "[0. 0. 0. 0.]\n",
- "[0.5 0.5 0.5 0.5]\n",
- "[0.25 0.75 0.75 0.75]\n",
- "[0.75 0.25 0.25 0.25]\n",
- "[0.125 0.375 0.375 0.875]\n",
- "[0.625 0.875 0.875 0.375]\n",
- "[0.375 0.125 0.125 0.625]\n",
- "[0.875 0.625 0.625 0.125]\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "d=4; n=8\n",
- "x_gail = qmcpy.Lattice(d,order='linear',randomize=False).gen_samples(n,warn=False)\n",
- "print('GAIL Samples')\n",
- "for i in range(n): print(x_gail[i])\n",
- "x_mps = qmcpy.Lattice(d,order='natural',randomize=False).gen_samples(n,warn=False)\n",
- "print('\\n\\nMPS Samples')\n",
- "for i in range(n): print(x_mps[i])\n",
- "fig,ax = pyplot.subplots(nrows=1,ncols=2,figsize=(10,5.5))\n",
- "ax[0].scatter(x_gail[0:n,0],x_gail[0:n,1],color='b')\n",
- "ax[0].set_title('GAIL backend')\n",
- "ax[1].scatter(x_mps[0:n,0],x_mps[0:n,1],color='b')\n",
- "ax[1].set_title('MPS backend')\n",
- "for ii in range(2):\n",
- " ax[ii].set_xlim([0,1]); ax[ii].set_xticks([0,1]); ax[ii].set_xlabel('$x_{i,1}$')\n",
- " ax[ii].set_ylim([0,1]); ax[ii].set_yticks([0,1]); ax[ii].set_ylabel('$x_{i,2}$')\n",
- " ax[ii].set_aspect(1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "vhY1WrmaL-HS"
- },
- "source": [
- "#### Default LDs are randomized\n",
- "\n",
- "LDs can be purely deterministic or they can be randomized. For lattices this corresponds to a random shift modulo one. For Sobol' sequences this corresponds to a random digital shift. (PyTorch also uses random linear scrambling.)\n",
- "\n",
- "This randomization is turned on by default, but can also be turned off. With randomization off, the points will always look the same. Below the first sequence of points is randomized and so is different every time the code is run. The second sequence is not randomized and stay the same.\n",
- "\n",
- "Turning off randomization throws a warning, which stops execution in Colab so we disable the warnings."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "T5M5DMXTgewR",
- "outputId": "c7132888-4c6f-4667-b95f-cd4b0b883c00"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Randomized LD Points with shape (16, 2)\n",
- "[[0.00944244 0.29799738]\n",
- " [0.50944244 0.79799738]\n",
- " [0.25944244 0.04799738]\n",
- " [0.75944244 0.54799738]\n",
- " [0.13444244 0.67299738]\n",
- " [0.63444244 0.17299738]\n",
- " [0.38444244 0.42299738]\n",
- " [0.88444244 0.92299738]\n",
- " [0.07194244 0.98549738]\n",
- " [0.57194244 0.48549738]\n",
- " [0.32194244 0.73549738]\n",
- " [0.82194244 0.23549738]\n",
- " [0.19694244 0.36049738]\n",
- " [0.69694244 0.86049738]\n",
- " [0.44694244 0.11049738]\n",
- " [0.94694244 0.61049738]]\n",
- "\n",
- "Nonrandomized LD Points with shape (16, 2)\n",
- "[[0. 0. ]\n",
- " [0.5 0.5 ]\n",
- " [0.25 0.75 ]\n",
- " [0.75 0.25 ]\n",
- " [0.125 0.375 ]\n",
- " [0.625 0.875 ]\n",
- " [0.375 0.125 ]\n",
- " [0.875 0.625 ]\n",
- " [0.0625 0.6875]\n",
- " [0.5625 0.1875]\n",
- " [0.3125 0.4375]\n",
- " [0.8125 0.9375]\n",
- " [0.1875 0.0625]\n",
- " [0.6875 0.5625]\n",
- " [0.4375 0.8125]\n",
- " [0.9375 0.3125]]\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "warnings.simplefilter('ignore') #turn off warnings which stop execution in Colab\n",
- "n = 16\n",
- "ldA = qmcpy.Lattice(2)\n",
- "ldB = qmcpy.Lattice(2, randomize = False)\n",
- "pointsA = ldA.gen_samples(n) #construct some points\n",
- "pointsB = ldB.gen_samples(n) #construct some points\n",
- "print(f'\\nRandomized LD Points with shape {pointsA.shape}\\n'+str(pointsA)) #these points have 15 significant digit precision but only three digits are shown\n",
- "print(f'\\nNonrandomized LD Points with shape {pointsB.shape}\\n'+str(pointsB)) #these points have 15 significant digit precision but only three digits are shown\n",
- "fig,ax = pyplot.subplots(nrows=1,ncols=2,figsize=(10,5.5))\n",
- "ax[0].scatter(pointsA[0:n,0],pointsA[0:n,1],color='b')\n",
- "ax[1].scatter(pointsB[0:n,0],pointsB[0:n,1],color='b')\n",
- "for ii in range(2):\n",
- " ax[ii].set_xlim([0,1]); ax[ii].set_xticks([0,1]); ax[ii].set_xlabel('$x_{i,1}$')\n",
- " ax[ii].set_ylim([0,1]); ax[ii].set_yticks([0,1]); ax[ii].set_ylabel('$x_{i,2}$')\n",
- " ax[ii].set_aspect(1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "cfp-PHhOMOJ_"
- },
- "source": [
- "#### Unrandomized LDs start with $\\boldsymbol{0}$\n",
- "\n",
- "By definition, without randomization the first point in all the popluar LD sequences is the origin. You can try."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "3QEtEYJuiJOB",
- "outputId": "63714e97-1d0d-427e-e39c-9666d7044025"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "LD Points with shape (4, 6)\n",
- "[[0. 0. 0. 0. 0. 0. ]\n",
- " [0.5 0.5 0.5 0.5 0.5 0.5 ]\n",
- " [0.25 0.75 0.75 0.75 0.25 0.75]\n",
- " [0.75 0.25 0.25 0.25 0.75 0.25]]\n"
- ]
- }
- ],
- "source": [
- "warnings.simplefilter('ignore') #turn off warnings which stop execution in Colab\n",
- "ld = qmcpy.Lattice(6,randomize=False)\n",
- "points = ld.gen_samples(4) #construct some points\n",
- "print(f'\\nLD Points with shape {points.shape}\\n'+str(points)) #these points have 15 significant digit precision but only three digits are shown"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "95B7fJ5kMX1H"
- },
- "source": [
- "#### Coordinate values of zero and one may be problemetic if transformed to $\\mathbb{R}^d$\n",
- "\n",
- "If a coordinate value of a point constructed on $[0,1]^d$ is zero or one, then then $0$ is mapped to $-\\infty$ and $1$ is mapped to $\\infty$ when these points are transformed to $\\mathbb{R}^d$, as is done when solving problems with Gaussian distributions. In Python, these may turn out to be ``nan``. For many applications, infinities or ``nan`` will be troublesome."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "t13Hkd1kkzjE",
- "outputId": "b9785a1f-87cb-4498-f09b-b0efcce81318"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "LD Points with shape (2, 6)\n",
- "[[0. 0. 0. 0. 0. 0. ]\n",
- " [0.5 0.5 0.5 0.5 0.5 0.5]]\n",
- "\n",
- "LD Points with shape (2, 6)\n",
- "[[nan nan nan nan nan nan]\n",
- " [ 0. 0. 0. 0. 0. 0.]]\n"
- ]
- }
- ],
- "source": [
- "warnings.simplefilter('ignore') #turn off warnings which stop execution in Colab\n",
- "ld = qmcpy.Lattice(6,randomize=False)\n",
- "unif_points = ld.gen_samples(2) #construct some points\n",
- "print(f'\\nLD Points with shape {unif_points.shape}\\n'+str(unif_points))\n",
- "ld_gauss = qmcpy.Gaussian(ld)\n",
- "gauss_points = ld_gauss.gen_samples(2)\n",
- "print(f'\\nLD Points with shape {gauss_points.shape}\\n'+str(gauss_points))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "RKLIJy_U9v-2"
- },
- "source": [
- "#### Replications with a fixed seed\n",
- "\n",
- "For debugging purposes, you may wish to fix the seed of your randomized points. This gives the advantage of an unchanging answer while avoiding the boundaries of the unit cube. When rerunning the code below, the answers are unchanged iff the seed is fixed. For Tony's stopping criterion from GAIL, the points are randomized, but the algorithm is deterministic. For the fixed-multilevel CLT stopping criterion, the points and the algorithm are both random."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "tSr40DWa4sYW",
- "outputId": "127db88b-e93f-40ac-ec0e-383177055109"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solution for GAIL stopping criterion with FIXED seed 1.8088685362448778\n",
- "Solution for CLT stopping criterion with FIXED seed 1.806594507374441\n",
- "Solution for GAIL stopping criterion 1.807944316997558\n",
- "Solution for CLT stopping criterion 1.80672801265591\n"
- ]
- }
- ],
- "source": [
- "solution_G_fixed = qmcpy.CubQMCSobolG(qmcpy.Keister(qmcpy.Gaussian(qmcpy.Sobol(2,seed=47),covariance=1/2))).integrate()[0]\n",
- "solution_CLT_fixed = qmcpy.CubQMCCLT(qmcpy.Keister(qmcpy.Gaussian(qmcpy.Sobol(2,seed=47,replications=15),covariance=1/2))).integrate()[0]\n",
- "solution_G = qmcpy.CubQMCSobolG(qmcpy.Keister(qmcpy.Gaussian(qmcpy.Sobol(2),covariance=1/2))).integrate()[0]\n",
- "solution_CLT = qmcpy.CubQMCCLT(qmcpy.Keister(qmcpy.Gaussian(qmcpy.Sobol(2,replications=15),covariance=1/2))).integrate()[0]\n",
- "print(f'Solution for GAIL stopping criterion with FIXED seed {solution_G_fixed}')\n",
- "print(f'Solution for CLT stopping criterion with FIXED seed {solution_CLT_fixed}')\n",
- "print(f'Solution for GAIL stopping criterion {solution_G}')\n",
- "print(f'Solution for CLT stopping criterion {solution_CLT}')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "5GNVeK8n_v2b"
- },
- "source": [
- "#### Help\n",
- "\n",
- "You can obtain help on any object by the ``help(...)`` or ``dir(...)`` commands."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "MT_coxDLnfr4",
- "outputId": "8d3d8f0f-bd40-440e-b991-6d129f732da0"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Help on class DigitalNetB2 in module qmcpy.discrete_distribution.digital_net_b2.digital_net_b2:\n",
- "\n",
- "class DigitalNetB2(qmcpy.discrete_distribution.abstract_discrete_distribution.AbstractLDDiscreteDistribution)\n",
- " | DigitalNetB2(dimension=1, replications=None, seed=None, randomize='LMS DS', generating_matrices='joe_kuo.6.21201.txt', order='RADICAL INVERSE', t=63, alpha=1, msb=None, _verbose=False, graycode=None, t_max=None, t_lms=None)\n",
- " |\n",
- " | Low discrepancy digital net in base 2.\n",
- " |\n",
- " | Note:\n",
- " | - Digital net sample sizes should be powers of $2$ e.g. $1$, $2$, $4$, $8$, $16$, $\\dots$.\n",
- " | - The first point of an unrandomized digital nets is the origin.\n",
- " | - `Sobol` is an alias for `DigitalNetB2`.\n",
- " | - To use higher order digital nets, either:\n",
- " |\n",
- " | - Pass in `generating_matrices` *without* interlacing and supply `alpha`>1 to apply interlacing, or\n",
- " | - Pass in `generating_matrices` *with* interlacing and set `alpha=1` to avoid additional interlacing\n",
- " |\n",
- " | i.e. do *not* pass in interlaced `generating_matrices` and set `alpha>1`, this will apply additional interlacing.\n",
- " |\n",
- " | Examples:\n",
- " | >>> discrete_distrib = DigitalNetB2(2,seed=7)\n",
- " | >>> discrete_distrib(4)\n",
- " | array([[0.72162356, 0.914955 ],\n",
- " | [0.16345554, 0.42964856],\n",
- " | [0.98676255, 0.03436384],\n",
- " | [0.42956655, 0.55876342]])\n",
- " | >>> discrete_distrib(1) # first point in the sequence\n",
- " | array([[0.72162356, 0.914955 ]])\n",
- " | >>> discrete_distrib\n",
- " | DigitalNetB2 (AbstractLDDiscreteDistribution)\n",
- " | d 2^(1)\n",
- " | replications 1\n",
- " | randomize LMS DS\n",
- " | gen_mats_source joe_kuo.6.21201.txt\n",
- " | order RADICAL INVERSE\n",
- " | t 63\n",
- " | alpha 1\n",
- " | n_limit 2^(32)\n",
- " | entropy 7\n",
- " |\n",
- " | Replications of independent randomizations\n",
- " |\n",
- " | >>> x = DigitalNetB2(dimension=3,seed=7,replications=2)(4)\n",
- " | >>> x.shape\n",
- " | (2, 4, 3)\n",
- " | >>> x\n",
- " | array([[[0.24653277, 0.1821862 , 0.74732591],\n",
- " | [0.68152903, 0.66169442, 0.42891961],\n",
- " | [0.48139855, 0.79818233, 0.08201287],\n",
- " | [0.91541325, 0.29520621, 0.77495809]],\n",
- " | \n",
- " | [[0.44876891, 0.85899604, 0.50549679],\n",
- " | [0.53635924, 0.04353443, 0.33564946],\n",
- " | [0.23214143, 0.29281506, 0.06841036],\n",
- " | [0.75295715, 0.60241448, 0.76962976]]])\n",
- " |\n",
- " | Different orderings (avoid warnings that the first point is the origin)\n",
- " |\n",
- " | >>> DigitalNetB2(dimension=2,randomize=False,order=\"GRAY\")(n_min=2,n_max=4,warn=False)\n",
- " | array([[0.75, 0.25],\n",
- " | [0.25, 0.75]])\n",
- " | >>> DigitalNetB2(dimension=2,randomize=False,order=\"RADICAL INVERSE\")(n_min=2,n_max=4,warn=False)\n",
- " | array([[0.25, 0.75],\n",
- " | [0.75, 0.25]])\n",
- " |\n",
- " | Generating matrices from [https://github.com/QMCSoftware/LDData/tree/main/dnet](https://github.com/QMCSoftware/LDData/tree/main/dnet)\n",
- " |\n",
- " | >>> DigitalNetB2(dimension=3,randomize=False,generating_matrices=\"mps.nx_s5_alpha2_m32.txt\")(8,warn=False)\n",
- " | array([[0. , 0. , 0. ],\n",
- " | [0.75841841, 0.45284834, 0.48844557],\n",
- " | [0.57679828, 0.13226272, 0.10061957],\n",
- " | [0.31858402, 0.32113875, 0.39369111],\n",
- " | [0.90278927, 0.45867532, 0.01803333],\n",
- " | [0.14542431, 0.02548793, 0.4749614 ],\n",
- " | [0.45587539, 0.33081476, 0.11474426],\n",
- " | [0.71318879, 0.15377192, 0.37629925]])\n",
- " |\n",
- " | All randomizations\n",
- " |\n",
- " | >>> DigitalNetB2(dimension=3,randomize='LMS DS',seed=5)(8)\n",
- " | array([[0.69346401, 0.20118185, 0.64779396],\n",
- " | [0.43998032, 0.90102467, 0.0936172 ],\n",
- " | [0.86663563, 0.60910036, 0.26043276],\n",
- " | [0.11327376, 0.30772653, 0.93959283],\n",
- " | [0.62102883, 0.79169756, 0.77051637],\n",
- " | [0.37451038, 0.1231324 , 0.46634012],\n",
- " | [0.94785596, 0.38577413, 0.13377215],\n",
- " | [0.20121617, 0.71843325, 0.56293458]])\n",
- " | >>> DigitalNetB2(dimension=3,randomize='LMS',seed=5)(8,warn=False)\n",
- " | array([[0. , 0. , 0. ],\n",
- " | [0.75446077, 0.83265937, 0.69584079],\n",
- " | [0.42329494, 0.65793842, 0.90427279],\n",
- " | [0.67763292, 0.48937304, 0.33344964],\n",
- " | [0.18550714, 0.97332905, 0.3772791 ],\n",
- " | [0.93104851, 0.17195496, 0.82311652],\n",
- " | [0.26221346, 0.31742386, 0.53093284],\n",
- " | [0.50787715, 0.5172669 , 0.2101083 ]])\n",
- " | >>> DigitalNetB2(dimension=3,randomize='DS',seed=5)(8)\n",
- " | array([[0.68383949, 0.04047995, 0.42903182],\n",
- " | [0.18383949, 0.54047995, 0.92903182],\n",
- " | [0.93383949, 0.79047995, 0.67903182],\n",
- " | [0.43383949, 0.29047995, 0.17903182],\n",
- " | [0.55883949, 0.66547995, 0.05403182],\n",
- " | [0.05883949, 0.16547995, 0.55403182],\n",
- " | [0.80883949, 0.41547995, 0.80403182],\n",
- " | [0.30883949, 0.91547995, 0.30403182]])\n",
- " | >>> DigitalNetB2(dimension=3,randomize='OWEN',seed=5)(8)\n",
- " | array([[0.33595486, 0.05834975, 0.30066401],\n",
- " | [0.89110875, 0.84905188, 0.81833285],\n",
- " | [0.06846074, 0.59997956, 0.67064205],\n",
- " | [0.6693703 , 0.25824002, 0.10469644],\n",
- " | [0.44586618, 0.99161977, 0.1873488 ],\n",
- " | [0.84245267, 0.16445553, 0.56544372],\n",
- " | [0.18546359, 0.44859876, 0.97389524],\n",
- " | [0.61215442, 0.64341386, 0.44529863]])\n",
- " |\n",
- " | Higher order net without randomization\n",
- " |\n",
- " | >>> DigitalNetB2(dimension=3,randomize='FALSE',seed=7,alpha=2)(4,warn=False)\n",
- " | array([[0. , 0. , 0. ],\n",
- " | [0.75 , 0.75 , 0.75 ],\n",
- " | [0.4375, 0.9375, 0.1875],\n",
- " | [0.6875, 0.1875, 0.9375]])\n",
- " |\n",
- " |\n",
- " | Higher order nets with randomizations and replications\n",
- " |\n",
- " | >>> DigitalNetB2(dimension=3,randomize='LMS DS',seed=7,replications=2,alpha=2)(4,warn=False)\n",
- " | array([[[0.42955149, 0.89149058, 0.43867111],\n",
- " | [0.68701828, 0.07601148, 0.51312447],\n",
- " | [0.10088033, 0.16293661, 0.25144138],\n",
- " | [0.85846252, 0.87103178, 0.70041789]],\n",
- " | \n",
- " | [[0.27151905, 0.42406763, 0.21917369],\n",
- " | [0.55035224, 0.67864387, 0.90033876],\n",
- " | [0.19356758, 0.57589964, 0.00347701],\n",
- " | [0.97235125, 0.32168581, 0.86920948]]])\n",
- " | >>> DigitalNetB2(dimension=3,randomize='LMS',seed=7,replications=2,alpha=2)(4,warn=False)\n",
- " | array([[[0. , 0. , 0. ],\n",
- " | [0.75817062, 0.96603053, 0.94947625],\n",
- " | [0.45367986, 0.80295638, 0.18778553],\n",
- " | [0.71171791, 0.2295424 , 0.76175441]],\n",
- " | \n",
- " | [[0. , 0. , 0. ],\n",
- " | [0.78664636, 0.75470215, 0.86876474],\n",
- " | [0.45336727, 0.99953621, 0.22253579],\n",
- " | [0.73996397, 0.24544824, 0.9008679 ]]])\n",
- " | >>> DigitalNetB2(dimension=3,randomize='DS',seed=7,replications=2,alpha=2)(4)\n",
- " | array([[[0.04386058, 0.58727432, 0.3691824 ],\n",
- " | [0.79386058, 0.33727432, 0.6191824 ],\n",
- " | [0.48136058, 0.39977432, 0.4316824 ],\n",
- " | [0.73136058, 0.64977432, 0.6816824 ]],\n",
- " | \n",
- " | [[0.65212985, 0.69669968, 0.10605352],\n",
- " | [0.40212985, 0.44669968, 0.85605352],\n",
- " | [0.83962985, 0.25919968, 0.16855352],\n",
- " | [0.08962985, 0.50919968, 0.91855352]]])\n",
- " | >>> DigitalNetB2(dimension=3,randomize='OWEN',seed=7,replications=2,alpha=2)(4)\n",
- " | array([[[0.46368517, 0.03964427, 0.62172094],\n",
- " | [0.7498683 , 0.76141348, 0.4243043 ],\n",
- " | [0.01729754, 0.97968459, 0.65963223],\n",
- " | [0.75365329, 0.1903774 , 0.34141493]],\n",
- " | \n",
- " | [[0.52252547, 0.5679709 , 0.05949112],\n",
- " | [0.27248656, 0.36488289, 0.81844058],\n",
- " | [0.94219959, 0.39172304, 0.20285965],\n",
- " | [0.19716391, 0.64741585, 0.92494554]]])\n",
- " |\n",
- " | **References:**\n",
- " |\n",
- " | 1. Marius Hofert and Christiane Lemieux.\n",
- " | qrng: (Randomized) Quasi-Random Number Generators (2019).\n",
- " | R package version 0.0-7.\n",
- " | [https://CRAN.R-project.org/package=qrng](https://CRAN.R-project.org/package=qrng).\n",
- " |\n",
- " | 2. Faure, Henri, and Christiane Lemieux.\n",
- " | Implementation of Irreducible Sobol' Sequences in Prime Power Bases.\n",
- " | Mathematics and Computers in Simulation 161 (2019): 13-22. Crossref. Web.\n",
- " |\n",
- " | 3. F.Y. Kuo, D. Nuyens.\n",
- " | Application of quasi-Monte Carlo methods to elliptic PDEs with random diffusion coefficients \\- a survey of analysis and implementation.\n",
- " | Foundations of Computational Mathematics, 16(6):1631-1696, 2016.\n",
- " | [https://link.springer.com/article/10.1007/s10208-016-9329-5](https://link.springer.com/article/10.1007/s10208-016-9329-5).\n",
- " |\n",
- " | 4. D. Nuyens.\n",
- " | The Magic Point Shop of QMC point generators and generating vectors.\n",
- " | MATLAB and Python software, 2018.\n",
- " | [https://people.cs.kuleuven.be/~dirk.nuyens/](https://people.cs.kuleuven.be/~dirk.nuyens/).\n",
- " |\n",
- " | 5. R. Cools, F.Y. Kuo, D. Nuyens.\n",
- " | Constructing embedded lattice rules for multivariate integration.\n",
- " | SIAM J. Sci. Comput., 28(6), 2162-2188.\n",
- " |\n",
- " | 6. I.M. Sobol', V.I. Turchaninov, Yu.L. Levitan, B.V. Shukhman.\n",
- " | Quasi-Random Sequence Generators.\n",
- " | Keldysh Institute of Applied Mathematics.\n",
- " | Russian Academy of Sciences, Moscow (1992).\n",
- " |\n",
- " | 7. Sobol, Ilya & Asotsky, Danil & Kreinin, Alexander & Kucherenko, Sergei. (2011).\n",
- " | Construction and Comparison of High-Dimensional Sobol' Generators. Wilmott. 2011.\n",
- " | [10.1002/wilm.10056](https://onlinelibrary.wiley.com/doi/abs/10.1002/wilm.10056).\n",
- " |\n",
- " | 8. Paul Bratley and Bennett L. Fox.\n",
- " | Algorithm 659: Implementing Sobol's quasirandom sequence generator.\n",
- " | ACM Trans. Math. Softw. 14, 1 (March 1988), 88-100. 1988.\n",
- " | [https://doi.org/10.1145/42288.214372](https://doi.org/10.1145/42288.2143720).\n",
- " |\n",
- " | Method resolution order:\n",
- " | DigitalNetB2\n",
- " | qmcpy.discrete_distribution.abstract_discrete_distribution.AbstractLDDiscreteDistribution\n",
- " | qmcpy.discrete_distribution.abstract_discrete_distribution.AbstractDiscreteDistribution\n",
- " | builtins.object\n",
- " |\n",
- " | Methods defined here:\n",
- " |\n",
- " | __init__(self, dimension=1, replications=None, seed=None, randomize='LMS DS', generating_matrices='joe_kuo.6.21201.txt', order='RADICAL INVERSE', t=63, alpha=1, msb=None, _verbose=False, graycode=None, t_max=None, t_lms=None)\n",
- " | Args:\n",
- " | dimension (Union[int, np.ndarray]): Dimension of the generator.\n",
- " |\n",
- " | - If an `int` is passed in, use generating vector components at indices 0,...,`dimension`-1.\n",
- " | - If an `np.ndarray` is passed in, use generating vector components at these indices.\n",
- " |\n",
- " | replications (int): Number of independent randomizations of a pointset.\n",
- " | seed (Union[None, int, np.random.SeedSeq]): Seed the random number generator for reproducibility.\n",
- " | randomize (str): Options are\n",
- " |\n",
- " | - `'LMS DS'`: Linear matrix scramble with digital shift.\n",
- " | - `'LMS'`: Linear matrix scramble only.\n",
- " | - `'DS'`: Digital shift only.\n",
- " | - `'NUS'`: Nested uniform scrambling. Also known as Owen scrambling.\n",
- " | - `'FALSE'`: No randomization. In this case the first point will be the origin.\n",
- " |\n",
- " | generating_matrices (Union[str, np.ndarray, int]): Specify the generating matrices.\n",
- " |\n",
- " | - A `str` should be the name (or path) of a file from the LDData repo at [https://github.com/QMCSoftware/LDData/tree/main/dnet](https://github.com/QMCSoftware/LDData/tree/main/dnet).\n",
- " | - An `np.ndarray` of integers with shape $(d,m_\\mathrm{max})$ or $(r,d,m_\\mathrm{max})$ where $d$ is the number of dimensions, $r$ is the number of replications, and $2^{m_\\mathrm{max}}$ is the maximum number of supported points. Setting `msb=False` will flip the bits of ints in the generating matrices.\n",
- " |\n",
- " | order (str): `'RADICAL INVERSE'`, or `'GRAY'` ordering. See the doctest example above.\n",
- " | t (int): Number of bits in integer represetation of points *after* randomization. The number of bits in the generating matrices is inferred based on the largest value.\n",
- " | alpha (int): Interlacing factor for higher order nets.\n",
- " | When `alpha`>1, interlacing is performed regardless of the generating matrices,\n",
- " | i.e., for `alpha`>1 do *not* pass in generating matrices which are already interlaced.\n",
- " | The Note for this class contains more info.\n",
- " | msb (bool): Flag for Most Significant Bit (MSB) vs Least Significant Bit (LSB) integer representations in generating matrices. If `msb=False` (LSB order), then integers in generating matrices will be bit-reversed.\n",
- " | _verbose (bool): If `True`, print linear matrix scrambling matrices.\n",
- " |\n",
- " | ----------------------------------------------------------------------\n",
- " | Methods inherited from qmcpy.discrete_distribution.abstract_discrete_distribution.AbstractLDDiscreteDistribution:\n",
- " |\n",
- " | __repr__(self)\n",
- " | Return repr(self).\n",
- " |\n",
- " | ----------------------------------------------------------------------\n",
- " | Methods inherited from qmcpy.discrete_distribution.abstract_discrete_distribution.AbstractDiscreteDistribution:\n",
- " |\n",
- " | __call__(self, n=None, n_min=None, n_max=None, return_binary=False, warn=True)\n",
- " | - If just `n` is supplied, generate samples from the sequence at indices 0,...,`n`-1.\n",
- " | - If `n_min` and `n_max` are supplied, generate samples from the sequence at indices `n_min`,...,`n_max`-1.\n",
- " | - If `n` and `n_min` are supplied, then generate samples from the sequence at indices `n`,...,`n_min`-1.\n",
- " |\n",
- " | Args:\n",
- " | n (Union[None,int]): Number of points to generate.\n",
- " | n_min (Union[None,int]): Starting index of sequence.\n",
- " | n_max (Union[None,int]): Final index of sequence.\n",
- " | return_binary (bool): Only used for `DigitalNetB2`.\n",
- " | If `True`, *only* return the integer representation `x_integer` of base 2 digital net.\n",
- " | warn (bool): If `False`, disable warnings when generating samples.\n",
- " |\n",
- " | Returns:\n",
- " | x (np.ndarray): Samples from the sequence.\n",
- " |\n",
- " | - If `replications` is `None` then this will be of size (`n_max`-`n_min`) $\\times$ `dimension`\n",
- " | - If `replications` is a positive int, then `x` will be of size `replications` $\\times$ (`n_max`-`n_min`) $\\times$ `dimension`\n",
- " |\n",
- " | Note that if `return_binary=True` then `x` is returned where `x` are integer representations of the digital net points.\n",
- " |\n",
- " | gen_samples(self, n=None, n_min=None, n_max=None, return_binary=False, warn=True)\n",
- " |\n",
- " | pdf(self, x)\n",
- " |\n",
- " | spawn(self, s=1, dimensions=None)\n",
- " | Spawn new instances of the current discrete distribution but with new seeds and dimensions.\n",
- " | Used by multi-level QMC algorithms which require different seeds and dimensions on each level.\n",
- " |\n",
- " | Note:\n",
- " | Use `replications` instead of using `spawn` when possible, e.g., when spawning copies which all have the same dimension.\n",
- " |\n",
- " | Args:\n",
- " | s (int): Number of copies to spawn\n",
- " | dimensions (np.ndarray): Length `s` array of dimensions for each copy. Defaults to the current dimension.\n",
- " |\n",
- " | Returns:\n",
- " | spawned_discrete_distribs (list): Discrete distributions with new seeds and dimensions.\n",
- " |\n",
- " | ----------------------------------------------------------------------\n",
- " | Data descriptors inherited from qmcpy.discrete_distribution.abstract_discrete_distribution.AbstractDiscreteDistribution:\n",
- " |\n",
- " | __dict__\n",
- " | dictionary for instance variables\n",
- " |\n",
- " | __weakref__\n",
- " | list of weak references to the object\n",
- "\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "['__call__',\n",
- " '__class__',\n",
- " '__delattr__',\n",
- " '__dict__',\n",
- " '__dir__',\n",
- " '__doc__',\n",
- " '__eq__',\n",
- " '__format__',\n",
- " '__ge__',\n",
- " '__getattribute__',\n",
- " '__getstate__',\n",
- " '__gt__',\n",
- " '__hash__',\n",
- " '__init__',\n",
- " '__init_subclass__',\n",
- " '__le__',\n",
- " '__lt__',\n",
- " '__module__',\n",
- " '__ne__',\n",
- " '__new__',\n",
- " '__reduce__',\n",
- " '__reduce_ex__',\n",
- " '__repr__',\n",
- " '__setattr__',\n",
- " '__sizeof__',\n",
- " '__str__',\n",
- " '__subclasshook__',\n",
- " '__weakref__',\n",
- " '_gen_samples',\n",
- " '_spawn',\n",
- " 'gen_samples',\n",
- " 'pdf',\n",
- " 'spawn']"
- ]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "help(qmcpy.Sobol)\n",
- "dir(qmcpy.Sobol)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Dogw4vktLGmK"
- },
- "source": [
- "### Stopping criteria\n",
- "\n",
- "[GAIL]: http://gailgithub.github.io/GAIL_Dev/ \"Sou-Cheng T. Choi, Yuhan Ding, Fred J. Hickernell, Lan Jiang, Lluis Antoni Jimenez Rugama, Da Li, Jagadeeswaran Rathinavel, Xin Tong, Kan Zhang, Yizhi Zhang, and Xuan Zhou, GAIL: Guaranteed Automatic Integration Library (Version 2.3.1) [MATLAB Software], 2020. Available from `http://gailgithub.github.io/GAIL_Dev/`\"\n",
- "\n",
- "\n",
- "[CubMCML]: https://pubsonline.informs.org/doi/abs/10.1287/opre.1070.0496 \"Giles, M. Multilevel Monte Carlo Path Simulation. Operations Research 56,607–617 (June 2008).\"\n",
- "\n",
- "[CubQMCML]: https://www.semanticscholar.org/paper/Multilevel-quasi-Monte-Carlo-path-simulation-Giles-Waterhouse/25a9ca3aa216aa371d1be06fa9b93927187ee4ca \"Giles, M. B. & Waterhouse, B. J. Multilevel quasi-Monte Carlo path simulation. Advanced Financial Modelling, Radon Series on Computational and Applied Mathematics 8,165–181 (2009).\"\n",
- "\n",
- "[GilesSoftware]: http://people.maths.ox.ac.uk/~gilesm/mlmc/\n",
- "\n",
- "[CubMCG]: https://arxiv.org/abs/1208.4318 \"Hickernell, F.J., Jiang, L., Liu, Y., & Owen, A. Guaranteed Conservative Fixed Width Confidence Intervals via Monte Carlo Sampling in Monte Carlo and Quasi-Monte Carlo Methods 2012 (eds Dick, J., Kuo, F. Y., Peters, G. W., & Sloan, I. H.)\n",
- "Springer Proceedings in Mathematics and Statistics, vol. 65, (Springer-Verlag, Berlin, 2013), 105–128.\"\n",
- "\n",
- "[CubQMCLatticeG]: https://arxiv.org/abs/1411.1966 \"Jiménez Rugama, Ll. A. & Hickernell, F. J. Adaptive Multidimensional Integration Based on Rank-1 Lattices in Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014 (eds Cools, R. & Nuyens,D.) 163.arXiv:1411.1966 (Springer-Verlag, Berlin, 2016), 407–422.\"\n",
- "\n",
- "[CubQMCSobolG]: https://arxiv.org/abs/1410.8615 \"Hickernell, F. J. & Jiménez Rugama, Ll. A. Reliable Adaptive Cubature Using Digital Sequences in Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014 (eds Cools, R. & Nuyens,D.) 163.arXiv:1411.1966 (Springer-Verlag, Berlin, 2016), 367--383.\"\n",
- "\n",
- "\n",
- "The stopping criteria implemented come from several sources:\n",
- "- There are Central Limit Theorem (CLT) criteria for IID and LD sampling\n",
- "- Rigorous stopping criteria imported from [GAIL] have been implemented for\n",
- " - [IID][CubMCG] sampling\n",
- " - [Lattice][CubQMCLatticeG] sampling\n",
- " - [Sobol'][CubQMCSobolG] sampling\n",
- "- Multilevel stopping criteria due to [Giles][GilesSoftware] are given for [IID][CubMCML] and [LD][CubQMCML] sampling"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "3yQgB-Y1f_AQ"
- },
- "source": [
- "#### Changing the tolerance\n",
- "\n",
- "If you want to change the error tolerance, without creating a new ``StoppingCriterion`` object, there is a ``set_tolerance`` method."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "kvsEiUG55mEi",
- "outputId": "43765bc9-fede-4129-f83f-d60d0bb06adf"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Data (Data)\n",
- " solution 1.135\n",
- " comb_bound_low 1.129\n",
- " comb_bound_high 1.142\n",
- " comb_bound_diff 0.012\n",
- " comb_flags 1\n",
- " n_total 2^(13)\n",
- " n 2^(13)\n",
- " time_integrate 0.007\n",
- "CubQMCLatticeG (AbstractStoppingCriterion)\n",
- " abs_tol 0.010\n",
- " rel_tol 0\n",
- " n_init 2^(10)\n",
- " n_limit 2^(30)\n",
- "Keister (AbstractIntegrand)\n",
- "Gaussian (AbstractTrueMeasure)\n",
- " mean 0\n",
- " covariance 2^(-1)\n",
- " decomp_type PCA\n",
- " transform Gaussian (AbstractTrueMeasure)\n",
- " mean 0\n",
- " covariance 2^(-1)\n",
- " decomp_type PCA\n",
- "Lattice (AbstractLDDiscreteDistribution)\n",
- " d 5\n",
- " replications 1\n",
- " randomize SHIFT\n",
- " gen_vec_source kuo.lattice-33002-1024-1048576.9125.txt\n",
- " order RADICAL INVERSE\n",
- " n_limit 2^(20)\n",
- " entropy 7 \n",
- "\n",
- "\n",
- "Data (Data)\n",
- " solution 1.136\n",
- " comb_bound_low 1.135\n",
- " comb_bound_high 1.136\n",
- " comb_bound_diff 0.001\n",
- " comb_flags 1\n",
- " n_total 2^(17)\n",
- " n 2^(17)\n",
- " time_integrate 0.040\n",
- "CubQMCLatticeG (AbstractStoppingCriterion)\n",
- " abs_tol 0.001\n",
- " rel_tol 0\n",
- " n_init 2^(10)\n",
- " n_limit 2^(30)\n",
- "Keister (AbstractIntegrand)\n",
- "Gaussian (AbstractTrueMeasure)\n",
- " mean 0\n",
- " covariance 2^(-1)\n",
- " decomp_type PCA\n",
- " transform Gaussian (AbstractTrueMeasure)\n",
- " mean 0\n",
- " covariance 2^(-1)\n",
- " decomp_type PCA\n",
- "Lattice (AbstractLDDiscreteDistribution)\n",
- " d 5\n",
- " replications 1\n",
- " randomize SHIFT\n",
- " gen_vec_source kuo.lattice-33002-1024-1048576.9125.txt\n",
- " order RADICAL INVERSE\n",
- " n_limit 2^(20)\n",
- " entropy 7 \n",
- "\n",
- "\n"
- ]
- }
- ],
- "source": [
- "sc = qmcpy.CubQMCLatticeG(qmcpy.Keister(qmcpy.Gaussian(qmcpy.Lattice(5,seed=7),covariance=1/2)),abs_tol=.01)\n",
- "print(sc.integrate()[1],'\\n\\n')\n",
- "sc.set_tolerance(abs_tol=.001) #changing the tolerance\n",
- "print(sc.integrate()[1],'\\n\\n')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "5xDmWnPHE-qY"
- },
- "source": [
- "#### Relative error tolerances\n",
- "\n",
- "For some problems a relative error tolerance may make more sense. By default ``rel_tol`` is zero. The stopping criterion stops when either of the two tolerances is met."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "FpKNsUzFFVyt",
- "outputId": "0a71942d-b3cc-4833-eba5-17d291bf536d"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Data (Data)\n",
- " solution 1.135\n",
- " comb_bound_low 1.129\n",
- " comb_bound_high 1.142\n",
- " comb_bound_diff 0.012\n",
- " comb_flags 1\n",
- " n_total 2^(13)\n",
- " n 2^(13)\n",
- " time_integrate 0.004\n",
- "CubQMCLatticeG (AbstractStoppingCriterion)\n",
- " abs_tol 0.010\n",
- " rel_tol 0\n",
- " n_init 2^(10)\n",
- " n_limit 2^(30)\n",
- "Keister (AbstractIntegrand)\n",
- "Gaussian (AbstractTrueMeasure)\n",
- " mean 0\n",
- " covariance 2^(-1)\n",
- " decomp_type PCA\n",
- " transform Gaussian (AbstractTrueMeasure)\n",
- " mean 0\n",
- " covariance 2^(-1)\n",
- " decomp_type PCA\n",
- "Lattice (AbstractLDDiscreteDistribution)\n",
- " d 5\n",
- " replications 1\n",
- " randomize SHIFT\n",
- " gen_vec_source kuo.lattice-33002-1024-1048576.9125.txt\n",
- " order RADICAL INVERSE\n",
- " n_limit 2^(20)\n",
- " entropy 7 \n",
- "\n",
- "\n",
- "Data (Data)\n",
- " solution 1.135\n",
- " comb_bound_low 1.131\n",
- " comb_bound_high 1.139\n",
- " comb_bound_diff 0.008\n",
- " comb_flags 1\n",
- " n_total 2^(14)\n",
- " n 2^(14)\n",
- " time_integrate 0.006\n",
- "CubQMCLatticeG (AbstractStoppingCriterion)\n",
- " abs_tol 0\n",
- " rel_tol 0.005\n",
- " n_init 2^(10)\n",
- " n_limit 2^(30)\n",
- "Keister (AbstractIntegrand)\n",
- "Gaussian (AbstractTrueMeasure)\n",
- " mean 0\n",
- " covariance 2^(-1)\n",
- " decomp_type PCA\n",
- " transform Gaussian (AbstractTrueMeasure)\n",
- " mean 0\n",
- " covariance 2^(-1)\n",
- " decomp_type PCA\n",
- "Lattice (AbstractLDDiscreteDistribution)\n",
- " d 5\n",
- " replications 1\n",
- " randomize SHIFT\n",
- " gen_vec_source kuo.lattice-33002-1024-1048576.9125.txt\n",
- " order RADICAL INVERSE\n",
- " n_limit 2^(20)\n",
- " entropy 7\n"
- ]
- }
- ],
- "source": [
- "sc = qmcpy.CubQMCLatticeG(qmcpy.Keister(qmcpy.Gaussian(qmcpy.Lattice(5,seed=7),covariance=1/2)),abs_tol=.01) #zero relative tolerance by default\n",
- "print(sc.integrate()[1],'\\n\\n')\n",
- "sc.set_tolerance(abs_tol=0,rel_tol=0.005) #nonzero relative tolerance and zero absolute tolerance\n",
- "print(sc.integrate()[1])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "vJH-4-_1LMOH"
- },
- "source": [
- "### Use cases\n",
- "\n",
- "Only a few use cases have been coded so far. These include\n",
- "* ``qmcpy.keister`` Keiser's example\n",
- "* European and Asian option pricing (Single and Multi-level)\n",
- "* Computing Q-Noisy Expected Improvement (qEI) for Bayesian Optimization, see the blog at https://qmcpy.wpcomstaging.com/2020/07/19/qei-with-qmcpy/ and the Colaboratory notebook at https://drive.google.com/drive/folders/1EOREUL7lx4hytuZRQXB50UV8aE9JYN_a\n",
- "* Importance Sampling\n",
- "* Acceptance Rejection Sampling\n",
- "* Custom Sampling by Inverse CDF Transform\n",
- "\n",
- "``qmcpy`` would benefit from contributions.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "YGwMtWj_Wa41"
- },
- "source": [
- "## Acknowledgements and Contributing\n",
- "\n",
- "The ``qmcpy`` code has primarily been written by Aleksei Sorokin (BS/MS expected in 2021), with the generous financial support of SigOpt https://sigopt.com. However, much of the code is adapted from that of our friends (see above)\n",
- "\n",
- "We hope that QMCPy will become supported by the community. Your contribution will add value to QMCPy, while allowing you to take advantage of the contribution of others.\n",
- "\n",
- "We highlight the value of community owned software."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "LZkzVU91mJ_1"
- },
- "source": [
- "### Benefitting from each other's work is easier when we share\n",
- "\n",
- "* Most of us are very good at just one or two things:\n",
- " * LD sequence generators\n",
- " * Increasing efficiency (e.g., MLMC)\n",
- " * Stopping criteria\n",
- " * Realistic use cases\n",
- "\n",
- " Having a shared software library let's us take advantage of the best\n",
- "\n",
- "* Provides a consistent interface for different pieces from different places\n",
- "\n",
- "* Supports reproducible computational research\n",
- "\n",
- "* Tedious stuff only needs to be figured out once"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "8Gem3kfF6qFt"
- },
- "source": [
- "### A community helps find and correct code errors\n",
- "\n",
- "By having more eyes on code than just the developer's we are more likely to spot errors or idiosyncracies. Here are two examples."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "OFUXDQWu63T9"
- },
- "source": [
- "#### MATLAB's Sobol' generator\n",
- "\n",
- "Several years ago Lluís Antoni Jiménez Rugama discovered that the scrambling of the Sobol' generators implemented in MATLAB's Statistics Toolbox was wrong. After reporting the problem to the developers, it was corrected in MATLAB 2017a"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "tOtbO49TSXbS"
- },
- "source": [
- "#### PyTorch's Sobol' generator\n",
- "\n",
- "PyTorch is a popular Python library with its own Sobol generator. However, it should be used with care.\n",
- "\n",
- "* As noted above, the first point is skipped. \n",
- "* We found that unless you specify double precision, you get points that have 1 as a coordinate far too often. The developer seemed unaware of this.\n",
- "\n",
- "These issues have been reported at https://github.com/pytorch/pytorch/issues/32047.\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "9KVfvHiym6Pp"
- },
- "source": [
- "###How you can contribute\n",
- "\n",
- "After trying QMCPy out help us out by\n",
- "\n",
- "**Easy.** \n",
- "> Submit your bugs and feature requests as issues to https://github.com/QMCSoftware/QMCSoftware/issues\n",
- "\n",
- "**Moderately Difficult.** \n",
- "> Ask your students or collaborators to try QMCPy for their own work and submit their bugs and feature requests\n",
- "\n",
- "**Heroic.** \n",
- "> Add a feature or use case and make a pull request at https://github.com/QMCSoftware/QMCSoftware/pulls so that we can included it in our next release\n",
- "\n",
- "Questions? Email us at qmc-software@googlegroups.com\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "1q5YeKPMAVuW"
- },
- "source": [
- "## References\n",
- "\n",
- "1. S.-C. T. Choi, Y. Ding, F. J. Hickernell, L. Jiang, Ll. A. Jimenez Rugama, D. Li, Jagadeeswaran R., X. Tong, K. Zhang, Y. Zhang, and X. Zhou, GAIL: Guaranteed Automatic Integration Library (Version 2.3.1) [MATLAB Software], 2020. Available from `http://gailgithub.github.io/GAIL_Dev/`\n",
- "\n",
- "1. H. Faure and C. Lemieux. “Implementation of Irreducible Sobol’ Sequences in Prime Power Bases,” Mathematics and Computers in Simulation 161 (2019): 13–22.\n",
- "\n",
- "1. M. B. Giles. \"Multi-level Monte Carlo path simulation,\" Operations Research, 56(3):607-617, 2008. `http://people.maths.ox.ac.uk/~gilesm/files/OPRE_2008.pdf`.\n",
- "\n",
- "1. M. B. Giles. \"Improved multilevel Monte Carlo convergence using the Milstein scheme,\" 343-358, in Monte Carlo and Quasi-Monte Carlo Methods 2006, Springer, 2008. `http://people.maths.ox.ac.uk/~gilesm/files/mcqmc06.pdf`.\n",
- "\n",
- "1. M. B. Giles and B. J. Waterhouse. \"Multilevel quasi-Monte Carlo path simulation,\" pp.165-181 in Advanced Financial Modelling, in Radon Series on Computational and Applied Mathematics, de Gruyter, 2009. `http://people.maths.ox.ac.uk/~gilesm/files/radon.pdf`\n",
- "\n",
- "1. F. J. Hickernell, L. Jiang, Y. Liu, and A. B. Owen, \"Guaranteed conservative fixed width confidence intervals via Monte Carlo sampling,\" Monte Carlo and Quasi-Monte Carlo Methods 2012 (J. Dick, F.Y. Kuo, G. W. Peters, and I. H. Sloan, eds.), pp. 105-128, Springer-Verlag, Berlin, 2014. DOI: 10.1007/978-3-642-41095-6_5\n",
- "\n",
- "1. F. J. Hickernell and Lluis Antoni Jimenez Rugama, \"Reliable adaptive cubature using digital sequences,\" Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014 (R. Cools and D. Nuyens, eds.), Springer Proceedings in Mathematics and Statistics, vol. 163, Springer-Verlag, Berlin, 2016, arXiv:1410.8615 [math.NA], pp. 367-383.\n",
- "\n",
- "1. M. Hofert and C. Lemieux (2019). qrng: (Randomized) Quasi-Random Number Generators. R package version 0.0-7. `https://CRAN.R-project.org/package=qrng`.\n",
- "\n",
- "1. Ll. A. Jimenez Rugama and F. J. Hickernell, \"Adaptive multidimensional integration based on rank-1 lattices,\" Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014 (R. Cools and D. Nuyens, eds.), Springer Proceedings in Mathematics and Statistics, vol. 163, Springer-Verlag, Berlin, 2016, arXiv:1411.1966, pp. 407-422.\n",
- "\n",
- "1. B. D. Keister, Multidimensional Quadrature Algorithms, 'Computers in Physics', *10*, pp. 119-122, 1996.\n",
- "\n",
- "1. F. Y. Kuo and D. Nuyens. \"Application of quasi-Monte Carlo methods to elliptic PDEs with random diffusion coefficients - a survey of analysis and implementation,\" Foundations of Computational Mathematics, 16(6):1631-1696, 2016. ([springer link](https://link.springer.com/article/10.1007/s10208-016-9329-5), [arxiv link](https://arxiv.org/abs/1606.06613))\n",
- "\n",
- "1. P. L’Ecuyer and D. Munger, \"LatticeBuilder: A General Software Tool for Constructing Rank-1 Lattice Rules,\" ACM Transactions on Mathematical Software. *42* (2015). 10.1145/2754929.\n",
- "\n",
- "1. Y. Li, L. Kang, L., and F. J. Hickernell, Is a Transformed Low Discrepancy Design Also Low Discrepancy? in Contemporary Experimental Design, Multivariate Analysis and Data Mining, Festschrift in Honour of Professor Kai-Tai Fang (J. Fan and J. Pan, eds.), p. 69–92, 2020, https://arxiv.org/abs/2004.09887.\n",
- "\n",
- "1. A. B. Owen, \"A randomized Halton algorithm in R,\" 2017. arXiv:1706.02808 [stat.CO]\n",
- "\n"
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0xwyIP8iF1oa"
+ },
+ "source": [
+ "[QMCPy]: https://qmcsoftware.github.io/QMCSoftware/ \"Choi, S.-C. T., Hickernell, F. J., McCourt, M. & Sorokin, A. A quasi-Monte Carlo Python Library. https://qmcsoftware.github.io/QMCSoftware/. 2020.\"\n",
+ "\n",
+ "[GAIL]: http://gailgithub.github.io/GAIL_Dev/ \"Sou-Cheng T. Choi, Yuhan Ding, Fred J. Hickernell, Lan Jiang, Lluis Antoni Jimenez Rugama, Da Li, Jagadeeswaran Rathinavel, Xin Tong, Kan Zhang, Yizhi Zhang, and Xuan Zhou, GAIL: Guaranteed Automatic Integration Library (Version 2.3.1) [MATLAB Software], 2020. Available from `http://gailgithub.github.io/GAIL_Dev/`\"\n",
+ "\n",
+ "[NumPy]: https://pypi.org/project/numpy/ \"Oliphant, T. Guide to NumPy https://ecs.wgtn.ac.nz/foswiki/pub/Support/ManualPagesAndDocumentation/numpybook.pdf (TrelgolPublishing USA, 2006).\"\n",
+ "\n",
+ "[Scipy]: https://pypi.org/project/scipy/ \"Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E.A. Quintero, Charles R Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. (2020) SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, in press.\"\n",
+ "\n",
+ "[QRNG]: https://CRAN.R-project.org/package=qrng \"Marius Hofert and Christiane Lemieux (2019). qrng: (Randomized) Quasi-Random Number Generators. R package version 0.0-7. `https://CRAN.R-project.org/package=qrng`.\"\n",
+ "\n",
+ "[OwenHalton]: https://arxiv.org/abs/1706.02808 \"Owen, A. B. 'A randomized Halton algorithm in R,' 2017. arXiv:1706.02808 [stat.CO]\"\n",
+ "\n",
+ "[MPS]: https://people.cs.kuleuven.be/~dirk.nuyens/qmc-generators/ \"F. Y. Kuo and D. Nuyens. 'Application of quasi-Monte Carlo methods to elliptic PDEs with random diffusion coefficients - a survey of analysis and implementation,' Foundations of Computational Mathematics, 16(6):1631-1696, 2016. ([springer link](https://link.springer.com/article/10.1007/s10208-016-9329-5), [arxiv link](https://arxiv.org/abs/1606.06613))\"\n",
+ "\n",
+ "[CubMCML]: https://pubsonline.informs.org/doi/abs/10.1287/opre.1070.0496 \"Giles, M. Multilevel Monte Carlo Path Simulation. Operations Research 56,607–617 (June 2008).\"\n",
+ "\n",
+ "[CubQMCML]: https://www.semanticscholar.org/paper/Multilevel-quasi-Monte-Carlo-path-simulation-Giles-Waterhouse/25a9ca3aa216aa371d1be06fa9b93927187ee4ca \"Giles, M. B. & Waterhouse, B. J. Multilevel quasi-Monte Carlo path simulation. Advanced Financial Modelling, Radon Series on Computational and Applied Mathematics 8,165–181 (2009).\"\n",
+ "\n",
+ "[GilesSoftware]: http://people.maths.ox.ac.uk/~gilesm/mlmc/\n",
+ "\n",
+ "[PyTorch]: https://pytorch.org\n",
+ "\n",
+ "[LatticeBuilder]: http://simul.iro.umontreal.ca/latbuilder/ \"L’Ecuyer, Pierre & Munger, David. (2015). LatticeBuilder: A General Software Tool for Constructing Rank-1 Lattice Rules. ACM Transactions on Mathematical Software. 42. 10.1145/2754929.\"\n",
+ "\n",
+ "# Quasi-Monte Carlo (QMC) Software in QMCPy\n",
+ "\n",
+ "A tutorial based on this notebook is at https://media.ed.ac.uk/playlist/dedicated/51612401/1_0z0wec2z/1_2k12mwiw.\n",
+ "\n",
+ "As an example of available QMC software, we introduce the Python library [QMCPy][QMCPy]. This Jupyter notebook saves your typing. \n",
+ "\n",
+ "QMCPy is a community effort. This early release includes contributions from\n",
+ "\n",
+ "* Mike Giles [MLMC][CubMCML] and [MLQMC][CubQMCML] [software][GilesSoftware]\n",
+ "* Marius Hofert and Christiane Lemieux's [QRNG][QRNG]\n",
+ "* Pierre L'Ecuyer's [Lattice Builder][LatticeBuilder]\n",
+ "* Dirk Nuyens's [Magic Point Shop (MPS)][MPS]\n",
+ "* Art Owen's [Halton sequences][OwenHalton]\n",
+ "* [PyTorch][PyTorch]\n",
+ "* Guaranteed Automatic Integration Library [(GAIL)][GAIL]\n",
+ "\n",
+ "and depends on the [NumPy][NumPy] and [SciPy][SciPy] Python packages.\n",
+ "\n",
+ "View the companion pdf slides at https://speakerdeck.com/fjhickernell/quasi-monte-carlo-software and the introductory QMCPy blog at https://qmcpy.wordpress.com."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EbOXiuCQVs46"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "## Installation\n",
+ "QMCPy can be installed with ``pip install qmcpy`` or cloned from the [QMCSoftware GitHub repository](https://github.com/QMCSoftware/QMCSoftware). "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/talk_paper_demos/MCQMC_Tutorial_2020/MCQMC_2020_QMC_Software_Tutorial.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ " !pip install -q torch\n",
+ "except:\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "6Z9pGNDeWIBw",
+ "outputId": "481b6697-de4a-498b-ce14-18fe3926f839"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "QMCPy Version 1.6.3.2a\n"
+ ]
+ }
+ ],
+ "source": [
+ "import qmcpy #we import the environment at the start to use it\n",
+ "import numpy as np #basic numerical routines in Python\n",
+ "import time #timing routines\n",
+ "import warnings #to suppress warnings when needed\n",
+ "import torch #only needed for PyTorch Sobol' backend\n",
+ "from torch.quasirandom import SobolEngine\n",
+ "from matplotlib import pyplot; #plotting\n",
+ "\n",
+ "pyplot.rc('font', size=16) #set defaults so that the plots are readable\n",
+ "pyplot.rc('axes', titlesize=16)\n",
+ "pyplot.rc('axes', labelsize=16)\n",
+ "pyplot.rc('xtick', labelsize=16)\n",
+ "pyplot.rc('ytick', labelsize=16)\n",
+ "pyplot.rc('legend', fontsize=16)\n",
+ "pyplot.rc('figure', titlesize=16)\n",
+ "\n",
+ "#a helpful plotting method to show increasing numbers of points\n",
+ "def plot_successive_points(distrib,ld_name,first_n=64,n_cols=1,pt_clr='bgkcmy',\n",
+ " xlim=[0,1],ylim=[0,1],coord1 = 0,coord2 = 1):\n",
+ " fig,ax = pyplot.subplots(nrows=1,ncols=n_cols,figsize=(5*n_cols,5.5))\n",
+ " if n_cols==1: ax = [ax]\n",
+ " last_n = first_n*(2**n_cols)\n",
+ " points = distrib.gen_samples(n=last_n)\n",
+ " for i in range(n_cols):\n",
+ " n = first_n\n",
+ " nstart = 0\n",
+ " for j in range(i+1):\n",
+ " n = first_n*(2**j)\n",
+ " ax[i].scatter(points[nstart:n,coord1],points[nstart:n,coord2],color=pt_clr[j])\n",
+ " nstart = n\n",
+ " ax[i].set_title('n = %d'%n)\n",
+ " ax[i].set_xlim(xlim); ax[i].set_xticks(xlim); ax[i].set_xlabel('$x_{i,%d}$'%(coord1+1))\n",
+ " ax[i].set_ylim(ylim); ax[i].set_yticks(ylim); ax[i].set_ylabel('$x_{i,%d}$'%(coord2+1))\n",
+ " ax[i].set_aspect((xlim[1]-xlim[0])/(ylim[1]-ylim[0]))\n",
+ " fig.suptitle('%s Points'%ld_name)\n",
+ "print('QMCPy Version',qmcpy.__version__)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VuiiIKLQZW0G"
+ },
+ "source": [
+ "## Generating low discrepancy (LD) points via a ``DiscreteDistribution`` object\n",
+ "\n",
+ "We generate some points used for quasi-Monte Carlo methods. These points are called low discrepancy (LD for short) and are created as an instance of a ``DiscreteDistribution`` class."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bWz-RtrMHll7"
+ },
+ "source": [
+ "### Integration lattices\n",
+ "\n",
+ "Here are some (randomly shifted) integration lattice points. This is a two step procees: \n",
+ "\n",
+ "i) construct the ``DiscreteDistribution`` object ``lattice``, and then\n",
+ "\n",
+ "ii) construct a number of points from the sequence. \n",
+ "\n",
+ "The structure of these points favors ``n`` that is a power of 2."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 834
+ },
+ "id": "JhMZcYfOhchK",
+ "outputId": "90ac5112-0e25-4c18-99f6-494d9164fa85"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Lattice (AbstractLDDiscreteDistribution)\n",
+ " d 2^(1)\n",
+ " replications 1\n",
+ " randomize SHIFT\n",
+ " gen_vec_source kuo.lattice-33002-1024-1048576.9125.txt\n",
+ " order RADICAL INVERSE\n",
+ " n_limit 2^(20)\n",
+ " entropy 264030601762852985433978628854999910438\n",
+ "\n",
+ "LD Lattice Points with shape (16, 2)\n",
+ "[[0.14448802 0.23129441]\n",
+ " [0.64448802 0.73129441]\n",
+ " [0.39448802 0.98129441]\n",
+ " [0.89448802 0.48129441]\n",
+ " [0.26948802 0.60629441]\n",
+ " [0.76948802 0.10629441]\n",
+ " [0.51948802 0.35629441]\n",
+ " [0.01948802 0.85629441]\n",
+ " [0.20698802 0.91879441]\n",
+ " [0.70698802 0.41879441]\n",
+ " [0.45698802 0.66879441]\n",
+ " [0.95698802 0.16879441]\n",
+ " [0.33198802 0.29379441]\n",
+ " [0.83198802 0.79379441]\n",
+ " [0.58198802 0.04379441]\n",
+ " [0.08198802 0.54379441]]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
}
- ],
- "metadata": {
+ ],
+ "source": [
+ "lattice = qmcpy.Lattice(dimension=2) #define a discrete LD distribution based on an integration lattice\n",
+ "print(lattice) #print the properties of the lattice object\n",
+ "n = 16 #number of points to generate\n",
+ "points = lattice.gen_samples(n) #construct some points\n",
+ "print(f'\\nLD Lattice Points with shape {points.shape}\\n'+str(points)) #these points have 15 significant digit precision but only three digits are shown\n",
+ "plot_successive_points(lattice,'Lattice',n)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WztUPDqptYOp"
+ },
+ "source": [
+ "Rerunning the commands above yields a different sequence of points because these points are _randomly shifted_ modulo 1.\n",
+ "\n",
+ "We may also construct a subsequence of points in the middle of the sequence. Note that the points below match those above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
"colab": {
- "provenance": [],
- "toc_visible": true
- },
- "kernelspec": {
- "display_name": "qmcpy",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.3"
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "wTM9Mi5otlZF",
+ "outputId": "b1b5e967-e8cc-4f32-c3c3-307cacfb51fa"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "LD Lattice Points with shape (12, 2) \n",
+ "[[0.26948802 0.60629441]\n",
+ " [0.76948802 0.10629441]\n",
+ " [0.51948802 0.35629441]\n",
+ " [0.01948802 0.85629441]\n",
+ " [0.20698802 0.91879441]\n",
+ " [0.70698802 0.41879441]\n",
+ " [0.45698802 0.66879441]\n",
+ " [0.95698802 0.16879441]\n",
+ " [0.33198802 0.29379441]\n",
+ " [0.83198802 0.79379441]\n",
+ " [0.58198802 0.04379441]\n",
+ " [0.08198802 0.54379441]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "more_points = lattice.gen_samples(n_min=4,n_max=n) #get more points\n",
+ "print('LD Lattice Points with shape',more_points.shape,'\\n'+str(more_points))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aR5Z5rSMFBN9"
+ },
+ "source": [
+ "Each $d$-dimensional point is _one row_ in the array.\n",
+ "\n",
+ "As we increase the number of points, they fill $[0,1]^d$ evenly. The next points are placed in between the existing points. Here we illustrate with $d=2$."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 321
+ },
+ "id": "sEF-IqqeckC-",
+ "outputId": "991b6b14-442f-4b30-bb87-b8c4b6e3be5b"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_successive_points(lattice,'Lattice',n_cols=5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "14UexIYSJr2r"
+ },
+ "source": [
+ "### IID uniform points do not fill space as well\n",
+ "\n",
+ "Contrast this with independent and identically distributed (IID) points. Although successive points fill the square, they do so without knowledge of the others and produce clusters and gaps. Think of it this way\n",
+ "\n",
+ "* LD = evenly spread\n",
+ "* IID = points do not know about each other\n",
+ "\n",
+ "(Since the first parameter in the ``DiscreteDistribution`` object is the dimension, you need not identify it.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "rWJEGbqPJ4Ap",
+ "outputId": "84fc821b-f4a9-4b27-895c-b5ee710f05c5"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "IIDStdUniform (AbstractIIDDiscreteDistribution)\n",
+ " d 2^(1)\n",
+ " replications 1\n",
+ " entropy 151929946340103232908357805579611369175\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "iid = qmcpy.IIDStdUniform(2) #standard uniform IID random vector generator from NumPy\n",
+ "print(iid) #print the properties of iid\n",
+ "plot_successive_points(iid,'IID Uniform',n_cols=5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "57P6amgaMV36"
+ },
+ "source": [
+ "### Rows and columns are _not_ interchangeable for LD\n",
+ "\n",
+ "For LD sequences we must differentiate between the cooordinates of the point (column) and which point (row). The transpose of an LD array is _not_ LD. This differs from IID multivariate points with IID marginals.\n",
+ "\n",
+ "In the example below we reverse the roles of the rows and columns. The transposed lattice points do not fill space at all, while the transposed IID points are as good as the originals."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 346
+ },
+ "id": "TluhjiIyM12p",
+ "outputId": "56834502-f203-4630-8755-d7f278b3f47f"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "d = 16 #dimension\n",
+ "n = 2 #number of points\n",
+ "lattice = qmcpy.Lattice(d) #define a discrete LD distribution based on an integration lattice\n",
+ "lattice_pts = lattice.gen_samples(n) #the first parameter in the .gen_samples method is the number of points\n",
+ "iid = qmcpy.IIDStdUniform(d)\n",
+ "iid_pts = iid.gen_samples(n)\n",
+ "fig,ax = pyplot.subplots(nrows=1,ncols=2,figsize=(10,5.5))\n",
+ "ax[0].scatter(lattice_pts[0,0:d],lattice_pts[1,0:d],color='b')\n",
+ "ax[0].set_title('Transposed Lattice Points')\n",
+ "ax[1].scatter(iid_pts[0,0:d],iid_pts[1,0:d],color='b')\n",
+ "ax[1].set_title('Transposed IID Points')\n",
+ "for ii in range(2):\n",
+ " ax[ii].set_xlim([0,1]); ax[ii].set_xticks([0,1]); ax[ii].set_xlabel('$x_{1,j}$')\n",
+ " ax[ii].set_ylim([0,1]); ax[ii].set_yticks([0,1]); ax[ii].set_ylabel('$x_{2,j}$')\n",
+ " ax[ii].set_aspect(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5TUH0dtfICP8"
+ },
+ "source": [
+ "### Sobol' points\n",
+ "\n",
+ "Another LD sequence is the Sobol' points. Again, new points fill in the gaps between existing points. Since these are randomly digitally shifted Sobol' points, rerunning this command gives a different set of points. For Sobol' points as well, ``n`` should normally be a power of 2."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 753
+ },
+ "id": "iZN7mN98ktsR",
+ "outputId": "b20e7b2e-229e-4d5d-e271-345553f2eedb"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DigitalNetB2 (AbstractLDDiscreteDistribution)\n",
+ " d 2^(1)\n",
+ " replications 1\n",
+ " randomize LMS DS\n",
+ " gen_mats_source joe_kuo.6.21201.txt\n",
+ " order RADICAL INVERSE\n",
+ " t 63\n",
+ " alpha 1\n",
+ " n_limit 2^(32)\n",
+ " entropy 73208827127383995610457310988530317873\n",
+ "\n",
+ "LD Sobol' Points with shape (16, 2)\n",
+ "[[0.9431598 0.47365992]\n",
+ " [0.22125498 0.72515188]\n",
+ " [0.66129464 0.90348679]\n",
+ " [0.37738574 0.15491697]\n",
+ " [0.79964553 0.60309908]\n",
+ " [0.02104669 0.35362196]\n",
+ " [0.58028993 0.0173281 ]\n",
+ " [0.36467173 0.76778926]\n",
+ " [0.88650748 0.81477058]\n",
+ " [0.16853181 0.06430855]\n",
+ " [0.72964997 0.30712722]\n",
+ " [0.44965495 0.55660491]\n",
+ " [0.87190807 0.20193054]\n",
+ " [0.0894106 0.95049973]\n",
+ " [0.52754482 0.67862835]\n",
+ " [0.30804326 0.4271372 ]]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ld = qmcpy.Lattice(2)\n",
+ "gaussian_ld = qmcpy.Gaussian(ld, mean=[3,2], covariance=[[9,5], [5,4]]) #specify the desired mean and covariance of your multivariate Gaussian distribution\n",
+ "points = gaussian_ld.gen_samples(2**8)\n",
+ "print(gaussian_ld)\n",
+ "print(f'\\nGaussian LD Points with shape {points.shape}\\n'+str(points)) #these points have 15 significant digit precision but only three digits are shown\n",
+ "plot_successive_points(gaussian_ld,'Gaussian LD',first_n=2**8,xlim=[-6,12],ylim=[-2,6])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lcgMPIqo4i0-"
+ },
+ "source": [
+ "[TransformedLD]: https://arxiv.org/abs/2004.09887 \"Li, Y., Kang, L., and Hickernell, F. J. Is a Transformed Low Discrepancy Design Also Low Discrepancy? in Contemporary Experimental Design, Multivariate Analysis and Data Mining, Festschrift in Honour of Professor Kai-Tai Fang (J. Fan and J. Pan, eds.), p. 69–92, 2020.\"\n",
+ "\n",
+ "Transformations of low discrepancy sequences often have good properties, but may not be low discrepancy themselves, depending on your definition of discrepancy as shown by [Yiou Li and Lulu Kang][TransformedLD]."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "azx_-HNzb6m5"
+ },
+ "source": [
+ "**_Pause for questions_**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iaAiqS7ccQ2v"
+ },
+ "source": [
+ "## Integration\n",
+ "\n",
+ "[Keister]: https://aip.scitation.org/doi/pdf/10.1063/1.168565 \"Keister, B. D. Multidimensional Quadrature Algorithms. Computers in Physics 10, 119–122 (1996).\"\n",
+ "\n",
+ "Cubature—the approximation of multivariate integrals—is an important application area for QMC. To solve this problem we need the ``Integrand`` and ``StoppingCriterion`` classes.\n",
+ "\n",
+ "Consider the followng $d$-variate integral due to [Keister][Keister]:\n",
+ "\\begin{equation*}\n",
+ "\\mu = \\int_{\\mathbb{R}^d} \\cos(\\lVert\\boldsymbol{t}\\rVert) \\exp( - \\lVert \\boldsymbol{t} \\rVert^2) \\, \\mathrm{d} \\boldsymbol{t},\n",
+ "\\end{equation*}\n",
+ "where $\\lVert \\cdot \\rVert$ is the Euclidean norm. To approximate this integral via (Q)MC methods, it needs to be written as the integral with respect to a probability measure, e.g.,\n",
+ "\\begin{equation*}\n",
+ "\\mu = \\int_{\\mathbb{R}^d} \\underbrace{\\pi^{d/2} \\cos(\\lVert\\boldsymbol{t}\\rVert)}_{g(\\boldsymbol{t})} \\; \\underbrace{\\pi^{-d/2} \\exp( - \\lVert \\boldsymbol{t} \\rVert^2) \\, \\mathrm{d} \\boldsymbol{t}}_{\\mathcal{N}(\\boldsymbol{0}_d,\\mathsf{I}_d/2) \\text{ measure}}.\n",
+ "\\end{equation*}\n",
+ "Using transformation techniques highlighted above, this integral can be further transformed to an integral over the unit cube, which is suitable for certain stopping criteria:\n",
+ "\\begin{equation*}\n",
+ "\\mu = \\int_{[0,1]^d} \\underbrace{\\pi^{d/2} \\cos\\left(\\sqrt{ \\frac 12 \\sum_{j=1}^d \\bigl[\\Phi^{-1}(x_j)\\bigr]^2}\\right)}_{f(\\boldsymbol{x})} \\, \\rm d \\boldsymbol{x}.\n",
+ "\\end{equation*}\n",
+ "\n",
+ "Although it may seem counter-intuitive, we set up our numerical problem by\n",
+ "\n",
+ "* first choosing the ``DiscreteDistribution`` object,\n",
+ "* next the ``TrueMeasure`` object,\n",
+ "* thirdly choosing ``Integrand`` object, and\n",
+ "* finally the ``StoppingCriterion`` object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "id": "_w1FeeDRcbok"
+ },
+ "outputs": [],
+ "source": [
+ "d = 5 #coded as parameters so that\n",
+ "tol = 1e-3 #you can change here and propagate them through this example\n",
+ "lattice = qmcpy.Lattice(d)\n",
+ "gaussian_lattice = qmcpy.Gaussian(lattice, mean = 0, covariance = 1/2) #mean and covariance of the distribution identified above"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HXqFIrY-kKky"
+ },
+ "source": [
+ "### ``Integrand`` objects\n",
+ "\n",
+ "The ``TrueMeasure`` object becomes input to an ``Integrand`` object, which transforms our original integrand, $g$, to our eventual integrand, $f$. This transformation relies on the ``TrueMeasure`` object along with its corresponding ``DiscreteDistribution`` object. The object ``qmcpy.Keister`` has already been coded as a use case in ``qmcpy``."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "id": "x4xC0suLkGCf"
+ },
+ "outputs": [],
+ "source": [
+ "keister = qmcpy.Keister(gaussian_lattice) #transform the original integrand to the eventual one"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lerpTkUtkMNn"
+ },
+ "source": [
+ "### ``StoppingCriterion`` objects and the ``integrate`` method\n",
+ "\n",
+ "[CubQMCLatticeG]: https://arxiv.org/abs/1411.1966 \"Jiménez Rugama, Ll. A. & Hickernell, F. J. Adaptive Multidimensional Integration Based on Rank-1 Lattices in Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014 (eds Cools, R. & Nuyens,D.) 163.arXiv:1411.1966 (Springer-Verlag, Berlin, 2016), 407–422.\"\n",
+ "\n",
+ "Determining the sample size needed requires a stopping criterion, which typically depends on the error tolerance, ``abs_tol``. The stopping criterion attempts to produce the answer satisfying the error tolerance with not much more work than is truly needed. There are several ``StoppingCriterion`` objects available, but they tend to work for specific LD sequences. This one comes from [Tony Jiménez Rugama][CubQMCLatticeG]. It takes as its input the ``Integrand`` object, which carries information about the ``TrueMeasure`` object and its ``DiscreteDistribution`` object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "id": "xNaRz44KkGZt"
+ },
+ "outputs": [],
+ "source": [
+ "keister_lattice_gauss_g = qmcpy.CubQMCLatticeG(keister, abs_tol = tol) #using Tony's stopping criterion"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "h09IBoHscjaY"
+ },
+ "source": [
+ "Invoking the ``integrate`` method returns the numerical solution and a data object. Printing the data object provides a neat summary of the integration problem. For details of the output fields, see the online, searchable QMCPy Documentation at [https://qmcpy.readthedocs.io/](https://qmcpy.readthedocs.io/en/latest/algorithms.html#module-qmcpy.integrand.keister)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "bvo8WUAociQk",
+ "outputId": "e355bb54-2e9c-4721-91b0-1f32ab23a994"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Data (Data)\n",
+ " solution 1.135\n",
+ " comb_bound_low 1.135\n",
+ " comb_bound_high 1.136\n",
+ " comb_bound_diff 0.001\n",
+ " comb_flags 1\n",
+ " n_total 2^(17)\n",
+ " n 2^(17)\n",
+ " time_integrate 0.042\n",
+ "CubQMCLatticeG (AbstractStoppingCriterion)\n",
+ " abs_tol 0.001\n",
+ " rel_tol 0\n",
+ " n_init 2^(10)\n",
+ " n_limit 2^(30)\n",
+ "Keister (AbstractIntegrand)\n",
+ "Gaussian (AbstractTrueMeasure)\n",
+ " mean 0\n",
+ " covariance 2^(-1)\n",
+ " decomp_type PCA\n",
+ " transform Gaussian (AbstractTrueMeasure)\n",
+ " mean 0\n",
+ " covariance 2^(-1)\n",
+ " decomp_type PCA\n",
+ "Lattice (AbstractLDDiscreteDistribution)\n",
+ " d 5\n",
+ " replications 1\n",
+ " randomize SHIFT\n",
+ " gen_vec_source kuo.lattice-33002-1024-1048576.9125.txt\n",
+ " order RADICAL INVERSE\n",
+ " n_limit 2^(20)\n",
+ " entropy 38563684764170563855999902143753109741\n"
+ ]
+ }
+ ],
+ "source": [
+ "solution, data = keister_lattice_gauss_g.integrate()\n",
+ "print(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Tp1gEJNvHrCH"
+ },
+ "source": [
+ "#### Fixed sample budget computation\n",
+ "\n",
+ "If you are not concerned about meeting an error tolerance but can only afford ``n_max`` function values, then you can set an ``abs_tol`` small enough and set ``n_max`` to your desired sample size. You will get an error bound."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "qqQBnGnpdQGG",
+ "outputId": "7d9a57a6-c4fb-40f8-f4cc-45ef1ed8940d"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Data (Data)\n",
+ " solution 1.129\n",
+ " comb_bound_low 1.116\n",
+ " comb_bound_high 1.142\n",
+ " comb_bound_diff 0.026\n",
+ " comb_flags 0\n",
+ " n_total 2^(12)\n",
+ " n 2^(12)\n",
+ " time_integrate 0.003\n",
+ "CubQMCLatticeG (AbstractStoppingCriterion)\n",
+ " abs_tol 1.00e-06\n",
+ " rel_tol 0\n",
+ " n_init 2^(10)\n",
+ " n_limit 2^(12)\n",
+ "Keister (AbstractIntegrand)\n",
+ "Gaussian (AbstractTrueMeasure)\n",
+ " mean 0\n",
+ " covariance 2^(-1)\n",
+ " decomp_type PCA\n",
+ " transform Gaussian (AbstractTrueMeasure)\n",
+ " mean 0\n",
+ " covariance 2^(-1)\n",
+ " decomp_type PCA\n",
+ "Lattice (AbstractLDDiscreteDistribution)\n",
+ " d 5\n",
+ " replications 1\n",
+ " randomize SHIFT\n",
+ " gen_vec_source kuo.lattice-33002-1024-1048576.9125.txt\n",
+ " order RADICAL INVERSE\n",
+ " n_limit 2^(20)\n",
+ " entropy 38563684764170563855999902143753109741\n"
+ ]
+ }
+ ],
+ "source": [
+ "warnings.simplefilter(\"ignore\")\n",
+ "keister_lattice_gauss_g_small_n = qmcpy.CubQMCLatticeG(keister, abs_tol = 1e-6, n_limit = 2**12) #the default n_max is 2**35\n",
+ "solution, data = keister_lattice_gauss_g_small_n.integrate()\n",
+ "print(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TfKJMMnwo_6k"
+ },
+ "source": [
+ "#### QMC is much faster than MC. (Q)MC cost is mostly dimension dependent\n",
+ "\n",
+ "Run this next code block to see how the run time and the number of function evaluations increase as the tolerance decreases. QMC using LD sequences uses much less time and much fewer function values than MC using IID sequences.\n",
+ "\n",
+ "Tensor product rules have a time or function value cost that is $\\mathcal{O}(\\varepsilon^{-d/r})$, where $\\varepsilon$ is the error tolerance and $r$ is bounded above by both the smoothness of the integrand and the quality of the algorithm. Such rules have a _curse of dimensionality_ because their cost blows up exponentially with dimension.\n",
+ "\n",
+ "Unlike tensor product cubature rules, the cost of (Q)MC cubature is essentially dimension independent: $\\mathcal{O}(\\varepsilon^{-2})$ for IID MC and typically $\\mathcal{O}(\\varepsilon^{-1-\\delta})$ for QMC. Although Q(MC) is not particularly fast, its performance usually does not degrade as the number of variables of in the integrand increases."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 436
+ },
+ "id": "EzaSx-P5UWHY",
+ "outputId": "2dc5b0eb-310c-4aa1-b185-f6024a1221ea"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Keister integral = 1.1356452963607024\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "d = 5; tol = 2.5e-3 #re-construct the example\n",
+ "#d = 7; tol = 3e-3 #if you change the dimension\n",
+ "#d = 10; tol = 3e-2 #you may also wish to change the tolerance, since the value of the integral changes\n",
+ "ld_keister = qmcpy.Keister(qmcpy.Gaussian(qmcpy.Lattice(d), mean = 0, covariance = 1/2)) #mean and covariance of the distribution identified above\n",
+ "iid_keister = qmcpy.Keister(qmcpy.Gaussian(qmcpy.IIDStdUniform(d), mean = 0, covariance = 1/2))\n",
+ "\n",
+ "n_tol = 7\n",
+ "ii_iid = 2 #make this larger to reduce the time required\n",
+ "tol_vec = [tol*2**(ii) for ii in range(n_tol)] #initialize\n",
+ "ld_time = [0]*n_tol; ld_n = [0]*n_tol #low discrepancy time and number of function values\n",
+ "iid_time = [0]*n_tol; iid_n = [0]*n_tol #IID time and number of function values\n",
+ "for ii in range(n_tol):\n",
+ " solution, data = qmcpy.CubQMCLatticeG(ld_keister, abs_tol = tol_vec[ii]).integrate()\n",
+ " if ii == 0:\n",
+ " print(f'\\nKeister integral = {solution}\\n')\n",
+ " ld_time[ii] = data.time_integrate\n",
+ " ld_n[ii] = data.n_total\n",
+ " if ii >= ii_iid:\n",
+ " solution, data = qmcpy.CubMCG(iid_keister, abs_tol = tol_vec[ii]).integrate()\n",
+ " iid_time[ii] = data.time_integrate\n",
+ " iid_n[ii] = data.n_total\n",
+ "fig,ax = pyplot.subplots(nrows=1,ncols=2,figsize=(13,5.5))\n",
+ "ax[0].scatter(tol_vec[0:n_tol],ld_time[0:n_tol],color='b');\n",
+ "ax[0].plot(tol_vec[0:n_tol],[(ld_time[0]*tol_vec[0])/tol_vec[jj] for jj in range(n_tol)],color='b')\n",
+ "ax[0].scatter(tol_vec[ii_iid:n_tol],iid_time[ii_iid:n_tol],color='g');\n",
+ "ax[0].plot(tol_vec[ii_iid:n_tol],[(iid_time[ii_iid]*(tol_vec[ii_iid]**2))/(tol_vec[jj]**2) for jj in range(ii_iid,n_tol)],color='g')\n",
+ "ax[0].set_ylim([0.001,1000]); ax[0].set_ylabel('Time (s)')\n",
+ "ax[1].scatter(tol_vec[0:n_tol],ld_n[0:n_tol],color='b');\n",
+ "ax[1].plot(tol_vec[0:n_tol],[(ld_n[0]*tol_vec[0])/tol_vec[jj] for jj in range(n_tol)],color='b')\n",
+ "ax[1].scatter(tol_vec[ii_iid:n_tol],iid_n[ii_iid:n_tol],color='g');\n",
+ "ax[1].plot(tol_vec[ii_iid:n_tol],[(iid_n[ii_iid]*(tol_vec[ii_iid]**2))/(tol_vec[jj]**2) for jj in range(ii_iid,n_tol)],color='g')\n",
+ "ax[1].set_ylim([1e2,1e8]); ax[1].set_ylabel('n')\n",
+ "for ii in range(2):\n",
+ " ax[ii].set_xlim([tol,100*tol]); ax[ii].set_xlabel('Tolerance, '+r'$\\varepsilon$')\n",
+ " ax[ii].set_xscale('log'); ax[ii].set_yscale('log')\n",
+ " ax[ii].legend([r'$\\mathcal{O}(\\varepsilon^{-1})$',r'$\\mathcal{O}(\\varepsilon^{-2})$','LD','IID'],frameon=False)\n",
+ " ax[ii].set_aspect(0.35)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xjYc-Lco8mfm"
+ },
+ "source": [
+ "### Alternatives for ``StoppingCriterion``\n",
+ "\n",
+ "Other ``StoppingCriterion`` objects are available. Most are tied to particular ``DiscreteDistribution`` objects. For LD points one can use replications and the Central Limit Theorem (CLT)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "jlMvENFj83_Q",
+ "outputId": "5a8ab21d-fbd1-4bf8-a2af-15f617c7b7da"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Data (Data)\n",
+ " solution 1.135\n",
+ " comb_bound_low 1.134\n",
+ " comb_bound_high 1.136\n",
+ " comb_bound_diff 0.002\n",
+ " comb_flags 1\n",
+ " n_total 122880\n",
+ " n 122880\n",
+ " n_rep 2^(13)\n",
+ " time_integrate 0.029\n",
+ "CubQMCRepStudentT (AbstractStoppingCriterion)\n",
+ " inflate 1\n",
+ " alpha 0.010\n",
+ " abs_tol 0.003\n",
+ " rel_tol 0\n",
+ " n_init 2^(8)\n",
+ " n_limit 2^(30)\n",
+ "Keister (AbstractIntegrand)\n",
+ "Gaussian (AbstractTrueMeasure)\n",
+ " mean 0\n",
+ " covariance 2^(-1)\n",
+ " decomp_type PCA\n",
+ " transform Gaussian (AbstractTrueMeasure)\n",
+ " mean 0\n",
+ " covariance 2^(-1)\n",
+ " decomp_type PCA\n",
+ "Lattice (AbstractLDDiscreteDistribution)\n",
+ " d 5\n",
+ " replications 15\n",
+ " randomize SHIFT\n",
+ " gen_vec_source kuo.lattice-33002-1024-1048576.9125.txt\n",
+ " order RADICAL INVERSE\n",
+ " n_limit 2^(20)\n",
+ " entropy 275034244784380212109446453686580990022\n"
+ ]
+ }
+ ],
+ "source": [
+ "lattice_rep = qmcpy.Lattice(d,replications=15)\n",
+ "gaussian_lattice_rep = qmcpy.Gaussian(lattice_rep, mean = 0, covariance = 1/2)\n",
+ "keister_rep = qmcpy.Keister(gaussian_lattice_rep)\n",
+ "keister_lattice_gauss_CLT = qmcpy.CubQMCCLT(keister_rep, abs_tol = tol) #using a CLT stopping criterion with random replications\n",
+ "solution, data = keister_lattice_gauss_CLT.integrate()\n",
+ "print(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HCadgtCYb_AJ"
+ },
+ "source": [
+ "This answer agrees with the one above."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "n1BT5mFBiwA0"
+ },
+ "source": [
+ "### Alternatives for ``TrueMeasure``\n",
+ "\n",
+ "The Keister integrand may also be solved using a Lebesgue ``TrueMeasure`` object:\n",
+ "$$\\mu = \\int_{\\mathbb{R}^d} \\underbrace{\\cos(\\lVert\\boldsymbol{t}\\rVert) \\exp( - \\lVert\\boldsymbol{t}\\rVert^2)}_{g(\\boldsymbol{t})} \\, \\underbrace{\\mathrm{d} \\boldsymbol{t}}_{\\text{Lebesgue measure}}$$\n",
+ "The ``TrueMeasure`` object contains the appropriate information so that the ``Integrand`` object can obtain the correct eventual integrand, $f$, in terms of the original integrand, $g$. \n",
+ "\n",
+ "Since ``TrueMeasure`` is not a probability measure, so it cannot be mimicked, but it can be used to solve the integration problem. The ``Integrand`` object maps the problem using an affine transformation for finite boxes and an inverse normal distribution function transformation for $\\mathbb{R}^d$.\n",
+ "\n",
+ "The code below also shows how to take our own integrand defined with a simple input and output, and turn it into a ``qmcpy`` ready integrand using the ``qmcpy.CustomFun`` object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Y8FUgw9PVaMt",
+ "outputId": "5518418c-da80-41b8-c0be-7f7bc51cec3f"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Data (Data)\n",
+ " solution 1.136\n",
+ " comb_bound_low 1.133\n",
+ " comb_bound_high 1.138\n",
+ " comb_bound_diff 0.004\n",
+ " comb_flags 1\n",
+ " n_total 2^(16)\n",
+ " n 2^(16)\n",
+ " time_integrate 0.030\n",
+ "CubQMCNetG (AbstractStoppingCriterion)\n",
+ " abs_tol 0.003\n",
+ " rel_tol 0\n",
+ " n_init 2^(10)\n",
+ " n_limit 2^(35)\n",
+ "CustomFun (AbstractIntegrand)\n",
+ "Lebesgue (AbstractTrueMeasure)\n",
+ " transform Gaussian (AbstractTrueMeasure)\n",
+ " mean 0\n",
+ " covariance 1\n",
+ " decomp_type PCA\n",
+ "DigitalNetB2 (AbstractLDDiscreteDistribution)\n",
+ " d 5\n",
+ " replications 1\n",
+ " randomize LMS DS\n",
+ " gen_mats_source joe_kuo.6.21201.txt\n",
+ " order RADICAL INVERSE\n",
+ " t 63\n",
+ " alpha 1\n",
+ " n_limit 2^(32)\n",
+ " entropy 91580573475794728146679265749810645603\n"
+ ]
+ }
+ ],
+ "source": [
+ "def my_Keister(x): #this could be a functional of a solution to a PDE with random coefficients\n",
+ " #or anything that you would like\n",
+ " \"\"\"\n",
+ " x: nxd numpy ndarray\n",
+ " n samples\n",
+ " d dimensions\n",
+ "\n",
+ " returns n-vector of the Keister function\n",
+ " evaluated at the n input samples\n",
+ " \"\"\"\n",
+ " d = x.shape[1]\n",
+ " norm = np.sqrt((x**2).sum(1))\n",
+ " k = np.cos(norm)*np.exp(-norm**2)\n",
+ " return k #size n vector\n",
+ "\n",
+ "ld = qmcpy.Sobol(d) #choose the LD points\n",
+ "lebesgue = qmcpy.Lebesgue(qmcpy.Gaussian(ld)) #now choose the Lebesgue distribution\n",
+ "f = qmcpy.CustomFun(lebesgue, g=my_Keister)\n",
+ "keister_lebesgue_ld_g = qmcpy.CubQMCSobolG(f, abs_tol = tol) #the stopping criterion does need to match the LD points\n",
+ "solution, data = keister_lebesgue_ld_g.integrate()\n",
+ "print(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_2SFs_6Ma-9q"
+ },
+ "source": [
+ "This answer agrees with the answers above for the same integration problem.\n",
+ "\n",
+ "The initial ``DiscreteDistribution`` does not need to mimic the standard uniform distribution as this next example shows."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "8a0KABTT3Lai",
+ "outputId": "85d5d973-00f1-4945-be11-83e84da6426a"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Data (Data)\n",
+ " solution 1.127\n",
+ " bound_low 1.099\n",
+ " bound_high 1.155\n",
+ " bound_diff 0.056\n",
+ " n_total 1694184\n",
+ " time_integrate 0.361\n",
+ "CubMCCLT (AbstractStoppingCriterion)\n",
+ " abs_tol 0.025\n",
+ " rel_tol 0\n",
+ " n_init 2^(10)\n",
+ " n_limit 2^(30)\n",
+ " inflate 1.200\n",
+ " alpha 0.010\n",
+ "CustomFun (AbstractIntegrand)\n",
+ "Lebesgue (AbstractTrueMeasure)\n",
+ " transform Gaussian (AbstractTrueMeasure)\n",
+ " mean 0\n",
+ " covariance 1\n",
+ " decomp_type PCA\n",
+ "IIDStdUniform (AbstractIIDDiscreteDistribution)\n",
+ " d 5\n",
+ " replications 1\n",
+ " entropy 110067297374675390351789060258764096093\n"
+ ]
+ }
+ ],
+ "source": [
+ "iid = qmcpy.IIDStdUniform(d) #choose the LD points\n",
+ "lebesgue = qmcpy.Lebesgue(qmcpy.Gaussian(iid)) #now choose the Lebesgue distribution\n",
+ "f = qmcpy.CustomFun(lebesgue, g=my_Keister)\n",
+ "keister_lebesgue_ld_g = qmcpy.CubMCCLT(f, abs_tol = 10*tol) #the stopping criterion does need to match the points\n",
+ "solution, data = keister_lebesgue_ld_g.integrate()\n",
+ "print(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "py0Q1ysPyhdn"
+ },
+ "source": [
+ "### Multi-level (Q)MC\n",
+ "\n",
+ "When the dimesion of the multivariate integral is high, multi-level (Quasi-)Monte Carlo (ML(Q)MC) methods may save computation time. The cost of one integrand value depends on the number of input variables, $d$, which corresponds to the dimension of our integration problem. ML(Q)MC methods allow us attain our accuracy requirements by evaluating low dimensional integrands many times and high dimensional integrands much fewer times.\n",
+ "\n",
+ "High or infinte dimesional integration problems arise when computing the expectations of quantities coming stochastic differential equations (SDEs). These problems arise in finance applications. The dimension of the integrand typically refers to the number of time steps used to discretize the SDE.\n",
+ "\n",
+ "Here are some parameters for the Asian option examples below. Changing them here allows you to compare the run times of these examples in a fair way."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "id": "V_IXXRnNuBAa"
+ },
+ "outputs": [],
+ "source": [
+ "abs_tol = .05 #a nickel\n",
+ "n_time_steps = 64 #being a power of 2 will help for multi-level\n",
+ "\n",
+ "options = { #there should be nothing magic about these choices\n",
+ " 'interest_rate': .05,\n",
+ " 'volatility': .5,\n",
+ " 'start_price': 30,\n",
+ " 'strike_price': 30\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "byuaaQO4rYpS"
+ },
+ "source": [
+ "#### Single Level MC\n",
+ "\n",
+ "The vanilla way to solve this problem is IID Monte Carlo. The fair price of the option is an expectation or integral, and the dimension is the number of time steps of used to discretize the Brownian motion that drives the SDE describing the price of the underlying asset. The number of time steps should be fairly large.\n",
+ "\n",
+ "[CubMCG]: https://arxiv.org/abs/1208.4318 \"Fred J. Hickernell, Lan Jiang, Yuewei Liu, and Art B. Owen, 'Guaranteed conservative fixed width confidence intervals via Monte Carlo sampling,' Monte Carlo and Quasi-Monte Carlo Methods 2012 (J. Dick, F.Y. Kuo, G. W. Peters, and I. H. Sloan, eds.), pp. 105-128, Springer-Verlag, Berlin, 2014. DOI: 10.1007/978-3-642-41095-6_5\"\n",
+ "\n",
+ "[This paper by Lan Jiang and collaborators][CubMCG] describes the stopping criterion."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "DoxNSMfFl5C-",
+ "outputId": "c093118a-cc82-4bf3-81ed-2f85a69ff446"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Data (Data)\n",
+ " solution 3.686\n",
+ " bound_low 3.636\n",
+ " bound_high 3.736\n",
+ " bound_diff 0.100\n",
+ " n_total 225848\n",
+ " time_integrate 0.834\n",
+ "CubMCG (AbstractStoppingCriterion)\n",
+ " abs_tol 0.050\n",
+ " rel_tol 0\n",
+ " n_init 2^(10)\n",
+ " n_limit 2^(30)\n",
+ " inflate 1.200\n",
+ " alpha 0.010\n",
+ " kurtmax 1.478\n",
+ "AsianOption (AbstractIntegrand)\n",
+ " option ASIAN\n",
+ " call_put CALL\n",
+ " volatility 2^(-1)\n",
+ " start_price 30\n",
+ " strike_price 30\n",
+ " interest_rate 0.050\n",
+ " t_final 1\n",
+ " asian_mean ARITHMETIC\n",
+ "BrownianMotion (AbstractTrueMeasure)\n",
+ " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
+ " drift 0\n",
+ " mean [0. 0. 0. ... 0. 0. 0.]\n",
+ " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
+ " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
+ " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
+ " ...\n",
+ " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
+ " decomp_type PCA\n",
+ " transform BrownianMotion (AbstractTrueMeasure)\n",
+ " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
+ " drift 0\n",
+ " mean [0. 0. 0. ... 0. 0. 0.]\n",
+ " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
+ " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
+ " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
+ " ...\n",
+ " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
+ " decomp_type PCA\n",
+ "IIDStdUniform (AbstractIIDDiscreteDistribution)\n",
+ " d 2^(6)\n",
+ " replications 1\n",
+ " entropy 14445467058155448935378672762555839489\n"
+ ]
}
+ ],
+ "source": [
+ "iidBrownian = qmcpy.BrownianMotion(qmcpy.IIDStdUniform(n_time_steps))\n",
+ "payoff = qmcpy.AsianOption(iidBrownian, **options)\n",
+ "IIDstop = qmcpy.CubMCG(payoff,abs_tol=abs_tol)\n",
+ "price,data = IIDstop.integrate()\n",
+ "print(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HzGDIfvanzgk"
+ },
+ "source": [
+ "#### Adaptive Multilevel MC from Mike Giles\n",
+ "\n",
+ "[CubMCML]: https://pubsonline.informs.org/doi/abs/10.1287/opre.1070.0496 \"Giles, M. Multilevel Monte Carlo Path Simulation. Operations Research 56,607–617 (June 2008).\"\n",
+ "\n",
+ "[GilesSoftware]: http://people.maths.ox.ac.uk/~gilesm/mlmc/\n",
+ "\n",
+ "Mike Giles and his collaborators have developed several ML(Q)MC algorithms. The ML IID MC algorithm and stopping criterion implemented here are from [this paper][CubMCML] and [this code][GilesSoftware]. The answer is expected to be different than above, even with the same parameters, as the `MLCallOptions` uses a different discretization. The algorithm considers the SDE for logarithm of the stock price, which allows exact time stepping for constant interest rates and volatirilities, while Giles uses a Milstein discretization for the SDE for the stock price iteself."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "d2ZmrKjyoYY0",
+ "outputId": "b662e8fb-5ef3-4960-8ffd-0b9cf41bc94e"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Data (Data)\n",
+ " solution 3.681\n",
+ " n_total 430326\n",
+ " levels 3\n",
+ " n_level [341502 61469 27319]\n",
+ " mean_level [3.589 0.071 0.021]\n",
+ " var_level [39.742 2.575 0.665]\n",
+ " cost_per_sample [2. 4. 8.]\n",
+ " alpha 1.769\n",
+ " beta 1.953\n",
+ " gamma 1.000\n",
+ " time_integrate 0.077\n",
+ "CubMLMC (AbstractStoppingCriterion)\n",
+ " rmse_tol 0.019\n",
+ " n_init 2^(8)\n",
+ " levels_min 2^(1)\n",
+ " levels_max 10\n",
+ " theta 2^(-1)\n",
+ "AsianOption (AbstractIntegrand)\n",
+ " option ASIAN\n",
+ " call_put CALL\n",
+ " volatility 2^(-1)\n",
+ " start_price 30\n",
+ " strike_price 30\n",
+ " interest_rate 0.050\n",
+ " t_final 1\n",
+ " asian_mean ARITHMETIC\n",
+ "BrownianMotion (AbstractTrueMeasure)\n",
+ " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
+ " drift 0\n",
+ " mean [0. 0. 0. ... 0. 0. 0.]\n",
+ " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
+ " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
+ " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
+ " ...\n",
+ " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
+ " decomp_type PCA\n",
+ " transform BrownianMotion (AbstractTrueMeasure)\n",
+ " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
+ " drift 0\n",
+ " mean [0. 0. 0. ... 0. 0. 0.]\n",
+ " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
+ " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
+ " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
+ " ...\n",
+ " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
+ " decomp_type PCA\n",
+ "IIDStdUniform (AbstractIIDDiscreteDistribution)\n",
+ " d 2^(6)\n",
+ " replications 1\n",
+ " entropy 14445467058155448935378672762555839489\n"
+ ]
+ }
+ ],
+ "source": [
+ "giles_MLMC_stop = qmcpy.CubMCML(payoff,abs_tol=abs_tol)\n",
+ "solution,data = giles_MLMC_stop.integrate()\n",
+ "print(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HTkrRMi9rk_b"
+ },
+ "source": [
+ "#### Single Level QMC Baseline\n",
+ "\n",
+ "[CubQMCSobolG]: https://arxiv.org/abs/1410.8615 \"Fred J. Hickernell and Lluis Antoni Jimenez Rugama, 'Reliable adaptive cubature using digital sequences,' Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014 (R. Cools and D. Nuyens, eds.), Springer Proceedings in Mathematics and Statistics, vol. 163, Springer-Verlag, Berlin, 2016, arXiv:1410.8615 [math.NA], pp. 367-383.\"\n",
+ "\n",
+ "Tony Jiménez's stopping criterion for cubature via Sobol' sequences in [this paper][CubQMCSobolG] does not yet work for multi-level problems. Here it is treating the option pricing problem as a high dimensional integral."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "XtUqkZ1Qr2tH",
+ "outputId": "07d0855f-1747-407b-a04c-b6d9cc18c1e1"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Data (Data)\n",
+ " solution 3.697\n",
+ " comb_bound_low 3.672\n",
+ " comb_bound_high 3.721\n",
+ " comb_bound_diff 0.049\n",
+ " comb_flags 1\n",
+ " n_total 2^(10)\n",
+ " n 2^(10)\n",
+ " time_integrate 0.003\n",
+ "CubQMCNetG (AbstractStoppingCriterion)\n",
+ " abs_tol 0.050\n",
+ " rel_tol 0\n",
+ " n_init 2^(10)\n",
+ " n_limit 2^(35)\n",
+ "AsianOption (AbstractIntegrand)\n",
+ " option ASIAN\n",
+ " call_put CALL\n",
+ " volatility 2^(-1)\n",
+ " start_price 30\n",
+ " strike_price 30\n",
+ " interest_rate 0.050\n",
+ " t_final 1\n",
+ " asian_mean ARITHMETIC\n",
+ "BrownianMotion (AbstractTrueMeasure)\n",
+ " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
+ " drift 0\n",
+ " mean [0. 0. 0. ... 0. 0. 0.]\n",
+ " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
+ " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
+ " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
+ " ...\n",
+ " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
+ " decomp_type PCA\n",
+ " transform BrownianMotion (AbstractTrueMeasure)\n",
+ " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
+ " drift 0\n",
+ " mean [0. 0. 0. ... 0. 0. 0.]\n",
+ " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
+ " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
+ " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
+ " ...\n",
+ " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
+ " decomp_type PCA\n",
+ "DigitalNetB2 (AbstractLDDiscreteDistribution)\n",
+ " d 2^(6)\n",
+ " replications 1\n",
+ " randomize LMS DS\n",
+ " gen_mats_source joe_kuo.6.21201.txt\n",
+ " order RADICAL INVERSE\n",
+ " t 63\n",
+ " alpha 1\n",
+ " n_limit 2^(32)\n",
+ " entropy 267658111069601541207388819540918772545\n"
+ ]
+ }
+ ],
+ "source": [
+ "sobol_brownian = qmcpy.BrownianMotion(qmcpy.Sobol(n_time_steps))\n",
+ "integrand = qmcpy.AsianOption(sobol_brownian, **options)\n",
+ "stopping_criterion = qmcpy.CubQMCSobolG(integrand,abs_tol = abs_tol)\n",
+ "solution,data = stopping_criterion.integrate()\n",
+ "print(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ViBnbZ2GQDGK"
+ },
+ "source": [
+ "### Importance Sampling\n",
+ "\n",
+ "For the option pricing problem, we may add a drift to the Brownian motion as an example of importance sampling.\n",
+ "\n",
+ "First, we need to change our problem to one for which importance sampling can show some benefit. We consider an out-of-the-money call option."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "id": "Le1wHMsTQana"
+ },
+ "outputs": [],
+ "source": [
+ "abs_tol = .001\n",
+ "n_time_steps = 64\n",
+ "\n",
+ "options = {\n",
+ " 'interest_rate': .05,\n",
+ " 'volatility': .5,\n",
+ " 'start_price': 30,\n",
+ " 'strike_price': 40 #a larger strike price than before\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LIqdo4uuQsRd"
+ },
+ "source": [
+ "First we price it as above using single level QMC."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "NeXo-ZgUQ0IN",
+ "outputId": "8cabc874-c146-49f0-ea71-2413ee05367d"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Data (Data)\n",
+ " solution 1.018\n",
+ " comb_bound_low 1.017\n",
+ " comb_bound_high 1.019\n",
+ " comb_bound_diff 0.002\n",
+ " comb_flags 1\n",
+ " n_total 2^(15)\n",
+ " n 2^(15)\n",
+ " time_integrate 0.118\n",
+ "CubQMCNetG (AbstractStoppingCriterion)\n",
+ " abs_tol 0.001\n",
+ " rel_tol 0\n",
+ " n_init 2^(10)\n",
+ " n_limit 2^(35)\n",
+ "AsianOption (AbstractIntegrand)\n",
+ " option ASIAN\n",
+ " call_put CALL\n",
+ " volatility 2^(-1)\n",
+ " start_price 30\n",
+ " strike_price 40\n",
+ " interest_rate 0.050\n",
+ " t_final 1\n",
+ " asian_mean ARITHMETIC\n",
+ "BrownianMotion (AbstractTrueMeasure)\n",
+ " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
+ " drift 0\n",
+ " mean [0. 0. 0. ... 0. 0. 0.]\n",
+ " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
+ " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
+ " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
+ " ...\n",
+ " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
+ " decomp_type PCA\n",
+ " transform BrownianMotion (AbstractTrueMeasure)\n",
+ " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
+ " drift 0\n",
+ " mean [0. 0. 0. ... 0. 0. 0.]\n",
+ " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
+ " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
+ " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
+ " ...\n",
+ " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
+ " decomp_type PCA\n",
+ "DigitalNetB2 (AbstractLDDiscreteDistribution)\n",
+ " d 2^(6)\n",
+ " replications 1\n",
+ " randomize LMS DS\n",
+ " gen_mats_source joe_kuo.6.21201.txt\n",
+ " order RADICAL INVERSE\n",
+ " t 63\n",
+ " alpha 1\n",
+ " n_limit 2^(32)\n",
+ " entropy 68741035845447190293894879002444698271\n"
+ ]
+ }
+ ],
+ "source": [
+ "sobol_brownian = qmcpy.BrownianMotion(qmcpy.Sobol(n_time_steps))\n",
+ "integrand = qmcpy.AsianOption(sobol_brownian, **options)\n",
+ "stopping_criterion = qmcpy.CubQMCSobolG(integrand,abs_tol = abs_tol)\n",
+ "solution,data = stopping_criterion.integrate()\n",
+ "print(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1pgJzDBVRIzH"
+ },
+ "source": [
+ "Next, we introduce an upward drift in the Brownian motion, which produces more stock price paths with positive payoffs. This produces a smaller varation in the integrand and a generally faster run time. (There still remains the question of how to automatically choose an optimal drift.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "MAXEi_UkRstV",
+ "outputId": "f2732860-468e-4861-c693-106a97d6f719"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Data (Data)\n",
+ " solution 1.018\n",
+ " comb_bound_low 1.018\n",
+ " comb_bound_high 1.019\n",
+ " comb_bound_diff 0.001\n",
+ " comb_flags 1\n",
+ " n_total 2^(15)\n",
+ " n 2^(15)\n",
+ " time_integrate 0.115\n",
+ "CubQMCNetG (AbstractStoppingCriterion)\n",
+ " abs_tol 0.001\n",
+ " rel_tol 0\n",
+ " n_init 2^(10)\n",
+ " n_limit 2^(35)\n",
+ "AsianOption (AbstractIntegrand)\n",
+ " option ASIAN\n",
+ " call_put CALL\n",
+ " volatility 2^(-1)\n",
+ " start_price 30\n",
+ " strike_price 40\n",
+ " interest_rate 0.050\n",
+ " t_final 1\n",
+ " asian_mean ARITHMETIC\n",
+ "BrownianMotion (AbstractTrueMeasure)\n",
+ " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
+ " drift 0\n",
+ " mean [0. 0. 0. ... 0. 0. 0.]\n",
+ " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
+ " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
+ " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
+ " ...\n",
+ " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
+ " decomp_type PCA\n",
+ " transform BrownianMotion (AbstractTrueMeasure)\n",
+ " time_vec [0.016 0.031 0.047 ... 0.969 0.984 1. ]\n",
+ " drift 2^(-1)\n",
+ " mean [0.008 0.016 0.023 ... 0.484 0.492 0.5 ]\n",
+ " covariance [[0.016 0.016 0.016 ... 0.016 0.016 0.016]\n",
+ " [0.016 0.031 0.031 ... 0.031 0.031 0.031]\n",
+ " [0.016 0.031 0.047 ... 0.047 0.047 0.047]\n",
+ " ...\n",
+ " [0.016 0.031 0.047 ... 0.969 0.969 0.969]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 0.984]\n",
+ " [0.016 0.031 0.047 ... 0.969 0.984 1. ]]\n",
+ " decomp_type PCA\n",
+ "DigitalNetB2 (AbstractLDDiscreteDistribution)\n",
+ " d 2^(6)\n",
+ " replications 1\n",
+ " randomize LMS DS\n",
+ " gen_mats_source joe_kuo.6.21201.txt\n",
+ " order RADICAL INVERSE\n",
+ " t 63\n",
+ " alpha 1\n",
+ " n_limit 2^(32)\n",
+ " entropy 195386499743864987571337198121595404839\n"
+ ]
+ }
+ ],
+ "source": [
+ "sobol_drift_brownian = qmcpy.BrownianMotion(qmcpy.Sobol(n_time_steps), drift = 0.5)\n",
+ "integrand = qmcpy.AsianOption(sobol_drift_brownian, **options)\n",
+ "stopping_criterion = qmcpy.CubQMCSobolG(integrand,abs_tol = abs_tol)\n",
+ "solution,data = stopping_criterion.integrate()\n",
+ "print(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Qx-HQwxacQAA"
+ },
+ "source": [
+ "**_Pause for questions_**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "76dT8RIbKvr3"
+ },
+ "source": [
+ "## Under the hood\n",
+ "\n",
+ "The structure of ``qmcpy`` is that there are five major classes:\n",
+ "\n",
+ "* ``DiscreteDistribution`` used to generate LD sequences, primarily on $[0,1]^d$\n",
+ "* ``TrueMeasure`` for using these LD sequences to mimic other distributions and to define integrals with respect to other measures\n",
+ "* ``Integrand`` to define the integrand for the multivariate integration problems\n",
+ "* ``StoppingCriterion`` to determine when the desired accuracy has been reached\n",
+ "* ``AccumulateData`` the invisible class used to keep track of important data as you continue to sample\n",
+ "\n",
+ "We look at some of the important parameters that the corresponding objects have."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UlHMBejwK4_v"
+ },
+ "source": [
+ "[GAIL]: http://gailgithub.github.io/GAIL_Dev/ \"Sou-Cheng T. Choi, Yuhan Ding, Fred J. Hickernell, Lan Jiang, Lluis Antoni Jimenez Rugama, Da Li, Jagadeeswaran Rathinavel, Xin Tong, Kan Zhang, Yizhi Zhang, and Xuan Zhou, GAIL: Guaranteed Automatic Integration Library (Version 2.3.1) [MATLAB Software], 2020. Available from `http://gailgithub.github.io/GAIL_Dev/`\"\n",
+ "\n",
+ "[QRNG]: https://CRAN.R-project.org/package=qrng \"Marius Hofert and Christiane Lemieux (2019). qrng: (Randomized) Quasi-Random Number Generators. R package version 0.0-7. `https://CRAN.R-project.org/package=qrng`.\"\n",
+ "\n",
+ "[OwenHalton]: https://arxiv.org/abs/1706.02808 \"Owen, A. B. 'A randomized Halton algorithm in R,' 2017. arXiv:1706.02808 [stat.CO]\"\n",
+ "\n",
+ "[MPS]: https://people.cs.kuleuven.be/~dirk.nuyens/qmc-generators/ \"F. Y. Kuo and D. Nuyens. 'Application of quasi-Monte Carlo methods to elliptic PDEs with random diffusion coefficients - a survey of analysis and implementation,' Foundations of Computational Mathematics, 16(6):1631-1696, 2016. ([springer link](https://link.springer.com/article/10.1007/s10208-016-9329-5), [arxiv link](https://arxiv.org/abs/1606.06613))\"\n",
+ "\n",
+ "[GilesSoftware]: http://people.maths.ox.ac.uk/~gilesm/mlmc/\n",
+ "\n",
+ "[PyTorch]: https://pytorch.org\n",
+ "\n",
+ "[LatticeBuilder]: http://simul.iro.umontreal.ca/latbuilder/\n",
+ "\n",
+ "### LD sequence generators\n",
+ "\n",
+ "The LD generators (``DiscreteDistribution`` objects) implemented here are drawn from several sources, which are denoted ``backend`` (first listed is the default):\n",
+ "- Sobol: [QRNG], [MPS], & [PyTorch]\n",
+ "- Lattice: [GAIL] & [MPS], with default generating vectors from [Lattice Builder][LatticeBuilder]\n",
+ "- Halton: [Art Owen's][OwenHalton] & [QRNG]\n",
+ "- Korobov: [QRNG]\n",
+ "\n",
+ "We illustrate some of the features of these varous backends and some of the other parameters that you can set."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "M60jJ1nGqzy6"
+ },
+ "source": [
+ "#### Different lattice backends and generators\n",
+ "\n",
+ "The ``qmcpy.Lattice`` generator using the GAIL and MPS ``backends`` with the same generating vectors yield the same points but in a different order. The ``stopping criterion`` ``qmcpy.CubLatticeG`` requires the GAIL order, but not all stopping criteria do."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 690
+ },
+ "id": "4WN-EBMh6Jqh",
+ "outputId": "03a61a6c-a3ea-4079-ae2d-edf0f0177a5c"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GAIL Samples\n",
+ "[0. 0. 0. 0.]\n",
+ "[0.125 0.375 0.375 0.875]\n",
+ "[0.25 0.75 0.75 0.75]\n",
+ "[0.375 0.125 0.125 0.625]\n",
+ "[0.5 0.5 0.5 0.5]\n",
+ "[0.625 0.875 0.875 0.375]\n",
+ "[0.75 0.25 0.25 0.25]\n",
+ "[0.875 0.625 0.625 0.125]\n",
+ "\n",
+ "\n",
+ "MPS Samples\n",
+ "[0. 0. 0. 0.]\n",
+ "[0.5 0.5 0.5 0.5]\n",
+ "[0.25 0.75 0.75 0.75]\n",
+ "[0.75 0.25 0.25 0.25]\n",
+ "[0.125 0.375 0.375 0.875]\n",
+ "[0.625 0.875 0.875 0.375]\n",
+ "[0.375 0.125 0.125 0.625]\n",
+ "[0.875 0.625 0.625 0.125]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "d=4; n=8\n",
+ "x_gail = qmcpy.Lattice(d,order='linear',randomize=False).gen_samples(n,warn=False)\n",
+ "print('GAIL Samples')\n",
+ "for i in range(n): print(x_gail[i])\n",
+ "x_mps = qmcpy.Lattice(d,order='natural',randomize=False).gen_samples(n,warn=False)\n",
+ "print('\\n\\nMPS Samples')\n",
+ "for i in range(n): print(x_mps[i])\n",
+ "fig,ax = pyplot.subplots(nrows=1,ncols=2,figsize=(10,5.5))\n",
+ "ax[0].scatter(x_gail[0:n,0],x_gail[0:n,1],color='b')\n",
+ "ax[0].set_title('GAIL backend')\n",
+ "ax[1].scatter(x_mps[0:n,0],x_mps[0:n,1],color='b')\n",
+ "ax[1].set_title('MPS backend')\n",
+ "for ii in range(2):\n",
+ " ax[ii].set_xlim([0,1]); ax[ii].set_xticks([0,1]); ax[ii].set_xlabel('$x_{i,1}$')\n",
+ " ax[ii].set_ylim([0,1]); ax[ii].set_yticks([0,1]); ax[ii].set_ylabel('$x_{i,2}$')\n",
+ " ax[ii].set_aspect(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vhY1WrmaL-HS"
+ },
+ "source": [
+ "#### Default LDs are randomized\n",
+ "\n",
+ "LDs can be purely deterministic or they can be randomized. For lattices this corresponds to a random shift modulo one. For Sobol' sequences this corresponds to a random digital shift. (PyTorch also uses random linear scrambling.)\n",
+ "\n",
+ "This randomization is turned on by default, but can also be turned off. With randomization off, the points will always look the same. Below the first sequence of points is randomized and so is different every time the code is run. The second sequence is not randomized and stay the same.\n",
+ "\n",
+ "Turning off randomization throws a warning, which stops execution in Colab so we disable the warnings."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "T5M5DMXTgewR",
+ "outputId": "c7132888-4c6f-4667-b95f-cd4b0b883c00"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Randomized LD Points with shape (16, 2)\n",
+ "[[0.00944244 0.29799738]\n",
+ " [0.50944244 0.79799738]\n",
+ " [0.25944244 0.04799738]\n",
+ " [0.75944244 0.54799738]\n",
+ " [0.13444244 0.67299738]\n",
+ " [0.63444244 0.17299738]\n",
+ " [0.38444244 0.42299738]\n",
+ " [0.88444244 0.92299738]\n",
+ " [0.07194244 0.98549738]\n",
+ " [0.57194244 0.48549738]\n",
+ " [0.32194244 0.73549738]\n",
+ " [0.82194244 0.23549738]\n",
+ " [0.19694244 0.36049738]\n",
+ " [0.69694244 0.86049738]\n",
+ " [0.44694244 0.11049738]\n",
+ " [0.94694244 0.61049738]]\n",
+ "\n",
+ "Nonrandomized LD Points with shape (16, 2)\n",
+ "[[0. 0. ]\n",
+ " [0.5 0.5 ]\n",
+ " [0.25 0.75 ]\n",
+ " [0.75 0.25 ]\n",
+ " [0.125 0.375 ]\n",
+ " [0.625 0.875 ]\n",
+ " [0.375 0.125 ]\n",
+ " [0.875 0.625 ]\n",
+ " [0.0625 0.6875]\n",
+ " [0.5625 0.1875]\n",
+ " [0.3125 0.4375]\n",
+ " [0.8125 0.9375]\n",
+ " [0.1875 0.0625]\n",
+ " [0.6875 0.5625]\n",
+ " [0.4375 0.8125]\n",
+ " [0.9375 0.3125]]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "warnings.simplefilter('ignore') #turn off warnings which stop execution in Colab\n",
+ "n = 16\n",
+ "ldA = qmcpy.Lattice(2)\n",
+ "ldB = qmcpy.Lattice(2, randomize = False)\n",
+ "pointsA = ldA.gen_samples(n) #construct some points\n",
+ "pointsB = ldB.gen_samples(n) #construct some points\n",
+ "print(f'\\nRandomized LD Points with shape {pointsA.shape}\\n'+str(pointsA)) #these points have 15 significant digit precision but only three digits are shown\n",
+ "print(f'\\nNonrandomized LD Points with shape {pointsB.shape}\\n'+str(pointsB)) #these points have 15 significant digit precision but only three digits are shown\n",
+ "fig,ax = pyplot.subplots(nrows=1,ncols=2,figsize=(10,5.5))\n",
+ "ax[0].scatter(pointsA[0:n,0],pointsA[0:n,1],color='b')\n",
+ "ax[1].scatter(pointsB[0:n,0],pointsB[0:n,1],color='b')\n",
+ "for ii in range(2):\n",
+ " ax[ii].set_xlim([0,1]); ax[ii].set_xticks([0,1]); ax[ii].set_xlabel('$x_{i,1}$')\n",
+ " ax[ii].set_ylim([0,1]); ax[ii].set_yticks([0,1]); ax[ii].set_ylabel('$x_{i,2}$')\n",
+ " ax[ii].set_aspect(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cfp-PHhOMOJ_"
+ },
+ "source": [
+ "#### Unrandomized LDs start with $\\boldsymbol{0}$\n",
+ "\n",
+ "By definition, without randomization the first point in all the popluar LD sequences is the origin. You can try."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "3QEtEYJuiJOB",
+ "outputId": "63714e97-1d0d-427e-e39c-9666d7044025"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "LD Points with shape (4, 6)\n",
+ "[[0. 0. 0. 0. 0. 0. ]\n",
+ " [0.5 0.5 0.5 0.5 0.5 0.5 ]\n",
+ " [0.25 0.75 0.75 0.75 0.25 0.75]\n",
+ " [0.75 0.25 0.25 0.25 0.75 0.25]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "warnings.simplefilter('ignore') #turn off warnings which stop execution in Colab\n",
+ "ld = qmcpy.Lattice(6,randomize=False)\n",
+ "points = ld.gen_samples(4) #construct some points\n",
+ "print(f'\\nLD Points with shape {points.shape}\\n'+str(points)) #these points have 15 significant digit precision but only three digits are shown"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "95B7fJ5kMX1H"
+ },
+ "source": [
+ "#### Coordinate values of zero and one may be problemetic if transformed to $\\mathbb{R}^d$\n",
+ "\n",
+ "If a coordinate value of a point constructed on $[0,1]^d$ is zero or one, then then $0$ is mapped to $-\\infty$ and $1$ is mapped to $\\infty$ when these points are transformed to $\\mathbb{R}^d$, as is done when solving problems with Gaussian distributions. In Python, these may turn out to be ``nan``. For many applications, infinities or ``nan`` will be troublesome."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "t13Hkd1kkzjE",
+ "outputId": "b9785a1f-87cb-4498-f09b-b0efcce81318"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "LD Points with shape (2, 6)\n",
+ "[[0. 0. 0. 0. 0. 0. ]\n",
+ " [0.5 0.5 0.5 0.5 0.5 0.5]]\n",
+ "\n",
+ "LD Points with shape (2, 6)\n",
+ "[[nan nan nan nan nan nan]\n",
+ " [ 0. 0. 0. 0. 0. 0.]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "warnings.simplefilter('ignore') #turn off warnings which stop execution in Colab\n",
+ "ld = qmcpy.Lattice(6,randomize=False)\n",
+ "unif_points = ld.gen_samples(2) #construct some points\n",
+ "print(f'\\nLD Points with shape {unif_points.shape}\\n'+str(unif_points))\n",
+ "ld_gauss = qmcpy.Gaussian(ld)\n",
+ "gauss_points = ld_gauss.gen_samples(2)\n",
+ "print(f'\\nLD Points with shape {gauss_points.shape}\\n'+str(gauss_points))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RKLIJy_U9v-2"
+ },
+ "source": [
+ "#### Replications with a fixed seed\n",
+ "\n",
+ "For debugging purposes, you may wish to fix the seed of your randomized points. This gives the advantage of an unchanging answer while avoiding the boundaries of the unit cube. When rerunning the code below, the answers are unchanged iff the seed is fixed. For Tony's stopping criterion from GAIL, the points are randomized, but the algorithm is deterministic. For the fixed-multilevel CLT stopping criterion, the points and the algorithm are both random."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "tSr40DWa4sYW",
+ "outputId": "127db88b-e93f-40ac-ec0e-383177055109"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Solution for GAIL stopping criterion with FIXED seed 1.8088685362448778\n",
+ "Solution for CLT stopping criterion with FIXED seed 1.806594507374441\n",
+ "Solution for GAIL stopping criterion 1.807944316997558\n",
+ "Solution for CLT stopping criterion 1.80672801265591\n"
+ ]
+ }
+ ],
+ "source": [
+ "solution_G_fixed = qmcpy.CubQMCSobolG(qmcpy.Keister(qmcpy.Gaussian(qmcpy.Sobol(2,seed=47),covariance=1/2))).integrate()[0]\n",
+ "solution_CLT_fixed = qmcpy.CubQMCCLT(qmcpy.Keister(qmcpy.Gaussian(qmcpy.Sobol(2,seed=47,replications=15),covariance=1/2))).integrate()[0]\n",
+ "solution_G = qmcpy.CubQMCSobolG(qmcpy.Keister(qmcpy.Gaussian(qmcpy.Sobol(2),covariance=1/2))).integrate()[0]\n",
+ "solution_CLT = qmcpy.CubQMCCLT(qmcpy.Keister(qmcpy.Gaussian(qmcpy.Sobol(2,replications=15),covariance=1/2))).integrate()[0]\n",
+ "print(f'Solution for GAIL stopping criterion with FIXED seed {solution_G_fixed}')\n",
+ "print(f'Solution for CLT stopping criterion with FIXED seed {solution_CLT_fixed}')\n",
+ "print(f'Solution for GAIL stopping criterion {solution_G}')\n",
+ "print(f'Solution for CLT stopping criterion {solution_CLT}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5GNVeK8n_v2b"
+ },
+ "source": [
+ "#### Help\n",
+ "\n",
+ "You can obtain help on any object by the ``help(...)`` or ``dir(...)`` commands."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "MT_coxDLnfr4",
+ "outputId": "8d3d8f0f-bd40-440e-b991-6d129f732da0"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Help on class DigitalNetB2 in module qmcpy.discrete_distribution.digital_net_b2.digital_net_b2:\n",
+ "\n",
+ "class DigitalNetB2(qmcpy.discrete_distribution.abstract_discrete_distribution.AbstractLDDiscreteDistribution)\n",
+ " | DigitalNetB2(dimension=1, replications=None, seed=None, randomize='LMS DS', generating_matrices='joe_kuo.6.21201.txt', order='RADICAL INVERSE', t=63, alpha=1, msb=None, _verbose=False, graycode=None, t_max=None, t_lms=None)\n",
+ " |\n",
+ " | Low discrepancy digital net in base 2.\n",
+ " |\n",
+ " | Note:\n",
+ " | - Digital net sample sizes should be powers of $2$ e.g. $1$, $2$, $4$, $8$, $16$, $\\dots$.\n",
+ " | - The first point of an unrandomized digital nets is the origin.\n",
+ " | - `Sobol` is an alias for `DigitalNetB2`.\n",
+ " | - To use higher order digital nets, either:\n",
+ " |\n",
+ " | - Pass in `generating_matrices` *without* interlacing and supply `alpha`>1 to apply interlacing, or\n",
+ " | - Pass in `generating_matrices` *with* interlacing and set `alpha=1` to avoid additional interlacing\n",
+ " |\n",
+ " | i.e. do *not* pass in interlaced `generating_matrices` and set `alpha>1`, this will apply additional interlacing.\n",
+ " |\n",
+ " | Examples:\n",
+ " | >>> discrete_distrib = DigitalNetB2(2,seed=7)\n",
+ " | >>> discrete_distrib(4)\n",
+ " | array([[0.72162356, 0.914955 ],\n",
+ " | [0.16345554, 0.42964856],\n",
+ " | [0.98676255, 0.03436384],\n",
+ " | [0.42956655, 0.55876342]])\n",
+ " | >>> discrete_distrib(1) # first point in the sequence\n",
+ " | array([[0.72162356, 0.914955 ]])\n",
+ " | >>> discrete_distrib\n",
+ " | DigitalNetB2 (AbstractLDDiscreteDistribution)\n",
+ " | d 2^(1)\n",
+ " | replications 1\n",
+ " | randomize LMS DS\n",
+ " | gen_mats_source joe_kuo.6.21201.txt\n",
+ " | order RADICAL INVERSE\n",
+ " | t 63\n",
+ " | alpha 1\n",
+ " | n_limit 2^(32)\n",
+ " | entropy 7\n",
+ " |\n",
+ " | Replications of independent randomizations\n",
+ " |\n",
+ " | >>> x = DigitalNetB2(dimension=3,seed=7,replications=2)(4)\n",
+ " | >>> x.shape\n",
+ " | (2, 4, 3)\n",
+ " | >>> x\n",
+ " | array([[[0.24653277, 0.1821862 , 0.74732591],\n",
+ " | [0.68152903, 0.66169442, 0.42891961],\n",
+ " | [0.48139855, 0.79818233, 0.08201287],\n",
+ " | [0.91541325, 0.29520621, 0.77495809]],\n",
+ " | \n",
+ " | [[0.44876891, 0.85899604, 0.50549679],\n",
+ " | [0.53635924, 0.04353443, 0.33564946],\n",
+ " | [0.23214143, 0.29281506, 0.06841036],\n",
+ " | [0.75295715, 0.60241448, 0.76962976]]])\n",
+ " |\n",
+ " | Different orderings (avoid warnings that the first point is the origin)\n",
+ " |\n",
+ " | >>> DigitalNetB2(dimension=2,randomize=False,order=\"GRAY\")(n_min=2,n_max=4,warn=False)\n",
+ " | array([[0.75, 0.25],\n",
+ " | [0.25, 0.75]])\n",
+ " | >>> DigitalNetB2(dimension=2,randomize=False,order=\"RADICAL INVERSE\")(n_min=2,n_max=4,warn=False)\n",
+ " | array([[0.25, 0.75],\n",
+ " | [0.75, 0.25]])\n",
+ " |\n",
+ " | Generating matrices from [https://github.com/QMCSoftware/LDData/tree/main/dnet](https://github.com/QMCSoftware/LDData/tree/main/dnet)\n",
+ " |\n",
+ " | >>> DigitalNetB2(dimension=3,randomize=False,generating_matrices=\"mps.nx_s5_alpha2_m32.txt\")(8,warn=False)\n",
+ " | array([[0. , 0. , 0. ],\n",
+ " | [0.75841841, 0.45284834, 0.48844557],\n",
+ " | [0.57679828, 0.13226272, 0.10061957],\n",
+ " | [0.31858402, 0.32113875, 0.39369111],\n",
+ " | [0.90278927, 0.45867532, 0.01803333],\n",
+ " | [0.14542431, 0.02548793, 0.4749614 ],\n",
+ " | [0.45587539, 0.33081476, 0.11474426],\n",
+ " | [0.71318879, 0.15377192, 0.37629925]])\n",
+ " |\n",
+ " | All randomizations\n",
+ " |\n",
+ " | >>> DigitalNetB2(dimension=3,randomize='LMS DS',seed=5)(8)\n",
+ " | array([[0.69346401, 0.20118185, 0.64779396],\n",
+ " | [0.43998032, 0.90102467, 0.0936172 ],\n",
+ " | [0.86663563, 0.60910036, 0.26043276],\n",
+ " | [0.11327376, 0.30772653, 0.93959283],\n",
+ " | [0.62102883, 0.79169756, 0.77051637],\n",
+ " | [0.37451038, 0.1231324 , 0.46634012],\n",
+ " | [0.94785596, 0.38577413, 0.13377215],\n",
+ " | [0.20121617, 0.71843325, 0.56293458]])\n",
+ " | >>> DigitalNetB2(dimension=3,randomize='LMS',seed=5)(8,warn=False)\n",
+ " | array([[0. , 0. , 0. ],\n",
+ " | [0.75446077, 0.83265937, 0.69584079],\n",
+ " | [0.42329494, 0.65793842, 0.90427279],\n",
+ " | [0.67763292, 0.48937304, 0.33344964],\n",
+ " | [0.18550714, 0.97332905, 0.3772791 ],\n",
+ " | [0.93104851, 0.17195496, 0.82311652],\n",
+ " | [0.26221346, 0.31742386, 0.53093284],\n",
+ " | [0.50787715, 0.5172669 , 0.2101083 ]])\n",
+ " | >>> DigitalNetB2(dimension=3,randomize='DS',seed=5)(8)\n",
+ " | array([[0.68383949, 0.04047995, 0.42903182],\n",
+ " | [0.18383949, 0.54047995, 0.92903182],\n",
+ " | [0.93383949, 0.79047995, 0.67903182],\n",
+ " | [0.43383949, 0.29047995, 0.17903182],\n",
+ " | [0.55883949, 0.66547995, 0.05403182],\n",
+ " | [0.05883949, 0.16547995, 0.55403182],\n",
+ " | [0.80883949, 0.41547995, 0.80403182],\n",
+ " | [0.30883949, 0.91547995, 0.30403182]])\n",
+ " | >>> DigitalNetB2(dimension=3,randomize='OWEN',seed=5)(8)\n",
+ " | array([[0.33595486, 0.05834975, 0.30066401],\n",
+ " | [0.89110875, 0.84905188, 0.81833285],\n",
+ " | [0.06846074, 0.59997956, 0.67064205],\n",
+ " | [0.6693703 , 0.25824002, 0.10469644],\n",
+ " | [0.44586618, 0.99161977, 0.1873488 ],\n",
+ " | [0.84245267, 0.16445553, 0.56544372],\n",
+ " | [0.18546359, 0.44859876, 0.97389524],\n",
+ " | [0.61215442, 0.64341386, 0.44529863]])\n",
+ " |\n",
+ " | Higher order net without randomization\n",
+ " |\n",
+ " | >>> DigitalNetB2(dimension=3,randomize='FALSE',seed=7,alpha=2)(4,warn=False)\n",
+ " | array([[0. , 0. , 0. ],\n",
+ " | [0.75 , 0.75 , 0.75 ],\n",
+ " | [0.4375, 0.9375, 0.1875],\n",
+ " | [0.6875, 0.1875, 0.9375]])\n",
+ " |\n",
+ " |\n",
+ " | Higher order nets with randomizations and replications\n",
+ " |\n",
+ " | >>> DigitalNetB2(dimension=3,randomize='LMS DS',seed=7,replications=2,alpha=2)(4,warn=False)\n",
+ " | array([[[0.42955149, 0.89149058, 0.43867111],\n",
+ " | [0.68701828, 0.07601148, 0.51312447],\n",
+ " | [0.10088033, 0.16293661, 0.25144138],\n",
+ " | [0.85846252, 0.87103178, 0.70041789]],\n",
+ " | \n",
+ " | [[0.27151905, 0.42406763, 0.21917369],\n",
+ " | [0.55035224, 0.67864387, 0.90033876],\n",
+ " | [0.19356758, 0.57589964, 0.00347701],\n",
+ " | [0.97235125, 0.32168581, 0.86920948]]])\n",
+ " | >>> DigitalNetB2(dimension=3,randomize='LMS',seed=7,replications=2,alpha=2)(4,warn=False)\n",
+ " | array([[[0. , 0. , 0. ],\n",
+ " | [0.75817062, 0.96603053, 0.94947625],\n",
+ " | [0.45367986, 0.80295638, 0.18778553],\n",
+ " | [0.71171791, 0.2295424 , 0.76175441]],\n",
+ " | \n",
+ " | [[0. , 0. , 0. ],\n",
+ " | [0.78664636, 0.75470215, 0.86876474],\n",
+ " | [0.45336727, 0.99953621, 0.22253579],\n",
+ " | [0.73996397, 0.24544824, 0.9008679 ]]])\n",
+ " | >>> DigitalNetB2(dimension=3,randomize='DS',seed=7,replications=2,alpha=2)(4)\n",
+ " | array([[[0.04386058, 0.58727432, 0.3691824 ],\n",
+ " | [0.79386058, 0.33727432, 0.6191824 ],\n",
+ " | [0.48136058, 0.39977432, 0.4316824 ],\n",
+ " | [0.73136058, 0.64977432, 0.6816824 ]],\n",
+ " | \n",
+ " | [[0.65212985, 0.69669968, 0.10605352],\n",
+ " | [0.40212985, 0.44669968, 0.85605352],\n",
+ " | [0.83962985, 0.25919968, 0.16855352],\n",
+ " | [0.08962985, 0.50919968, 0.91855352]]])\n",
+ " | >>> DigitalNetB2(dimension=3,randomize='OWEN',seed=7,replications=2,alpha=2)(4)\n",
+ " | array([[[0.46368517, 0.03964427, 0.62172094],\n",
+ " | [0.7498683 , 0.76141348, 0.4243043 ],\n",
+ " | [0.01729754, 0.97968459, 0.65963223],\n",
+ " | [0.75365329, 0.1903774 , 0.34141493]],\n",
+ " | \n",
+ " | [[0.52252547, 0.5679709 , 0.05949112],\n",
+ " | [0.27248656, 0.36488289, 0.81844058],\n",
+ " | [0.94219959, 0.39172304, 0.20285965],\n",
+ " | [0.19716391, 0.64741585, 0.92494554]]])\n",
+ " |\n",
+ " | **References:**\n",
+ " |\n",
+ " | 1. Marius Hofert and Christiane Lemieux.\n",
+ " | qrng: (Randomized) Quasi-Random Number Generators (2019).\n",
+ " | R package version 0.0-7.\n",
+ " | [https://CRAN.R-project.org/package=qrng](https://CRAN.R-project.org/package=qrng).\n",
+ " |\n",
+ " | 2. Faure, Henri, and Christiane Lemieux.\n",
+ " | Implementation of Irreducible Sobol' Sequences in Prime Power Bases.\n",
+ " | Mathematics and Computers in Simulation 161 (2019): 13-22. Crossref. Web.\n",
+ " |\n",
+ " | 3. F.Y. Kuo, D. Nuyens.\n",
+ " | Application of quasi-Monte Carlo methods to elliptic PDEs with random diffusion coefficients \\- a survey of analysis and implementation.\n",
+ " | Foundations of Computational Mathematics, 16(6):1631-1696, 2016.\n",
+ " | [https://link.springer.com/article/10.1007/s10208-016-9329-5](https://link.springer.com/article/10.1007/s10208-016-9329-5).\n",
+ " |\n",
+ " | 4. D. Nuyens.\n",
+ " | The Magic Point Shop of QMC point generators and generating vectors.\n",
+ " | MATLAB and Python software, 2018.\n",
+ " | [https://people.cs.kuleuven.be/~dirk.nuyens/](https://people.cs.kuleuven.be/~dirk.nuyens/).\n",
+ " |\n",
+ " | 5. R. Cools, F.Y. Kuo, D. Nuyens.\n",
+ " | Constructing embedded lattice rules for multivariate integration.\n",
+ " | SIAM J. Sci. Comput., 28(6), 2162-2188.\n",
+ " |\n",
+ " | 6. I.M. Sobol', V.I. Turchaninov, Yu.L. Levitan, B.V. Shukhman.\n",
+ " | Quasi-Random Sequence Generators.\n",
+ " | Keldysh Institute of Applied Mathematics.\n",
+ " | Russian Academy of Sciences, Moscow (1992).\n",
+ " |\n",
+ " | 7. Sobol, Ilya & Asotsky, Danil & Kreinin, Alexander & Kucherenko, Sergei. (2011).\n",
+ " | Construction and Comparison of High-Dimensional Sobol' Generators. Wilmott. 2011.\n",
+ " | [10.1002/wilm.10056](https://onlinelibrary.wiley.com/doi/abs/10.1002/wilm.10056).\n",
+ " |\n",
+ " | 8. Paul Bratley and Bennett L. Fox.\n",
+ " | Algorithm 659: Implementing Sobol's quasirandom sequence generator.\n",
+ " | ACM Trans. Math. Softw. 14, 1 (March 1988), 88-100. 1988.\n",
+ " | [https://doi.org/10.1145/42288.214372](https://doi.org/10.1145/42288.2143720).\n",
+ " |\n",
+ " | Method resolution order:\n",
+ " | DigitalNetB2\n",
+ " | qmcpy.discrete_distribution.abstract_discrete_distribution.AbstractLDDiscreteDistribution\n",
+ " | qmcpy.discrete_distribution.abstract_discrete_distribution.AbstractDiscreteDistribution\n",
+ " | builtins.object\n",
+ " |\n",
+ " | Methods defined here:\n",
+ " |\n",
+ " | __init__(self, dimension=1, replications=None, seed=None, randomize='LMS DS', generating_matrices='joe_kuo.6.21201.txt', order='RADICAL INVERSE', t=63, alpha=1, msb=None, _verbose=False, graycode=None, t_max=None, t_lms=None)\n",
+ " | Args:\n",
+ " | dimension (Union[int, np.ndarray]): Dimension of the generator.\n",
+ " |\n",
+ " | - If an `int` is passed in, use generating vector components at indices 0,...,`dimension`-1.\n",
+ " | - If an `np.ndarray` is passed in, use generating vector components at these indices.\n",
+ " |\n",
+ " | replications (int): Number of independent randomizations of a pointset.\n",
+ " | seed (Union[None, int, np.random.SeedSeq]): Seed the random number generator for reproducibility.\n",
+ " | randomize (str): Options are\n",
+ " |\n",
+ " | - `'LMS DS'`: Linear matrix scramble with digital shift.\n",
+ " | - `'LMS'`: Linear matrix scramble only.\n",
+ " | - `'DS'`: Digital shift only.\n",
+ " | - `'NUS'`: Nested uniform scrambling. Also known as Owen scrambling.\n",
+ " | - `'FALSE'`: No randomization. In this case the first point will be the origin.\n",
+ " |\n",
+ " | generating_matrices (Union[str, np.ndarray, int]): Specify the generating matrices.\n",
+ " |\n",
+ " | - A `str` should be the name (or path) of a file from the LDData repo at [https://github.com/QMCSoftware/LDData/tree/main/dnet](https://github.com/QMCSoftware/LDData/tree/main/dnet).\n",
+ " | - An `np.ndarray` of integers with shape $(d,m_\\mathrm{max})$ or $(r,d,m_\\mathrm{max})$ where $d$ is the number of dimensions, $r$ is the number of replications, and $2^{m_\\mathrm{max}}$ is the maximum number of supported points. Setting `msb=False` will flip the bits of ints in the generating matrices.\n",
+ " |\n",
+ " | order (str): `'RADICAL INVERSE'`, or `'GRAY'` ordering. See the doctest example above.\n",
+ " | t (int): Number of bits in integer represetation of points *after* randomization. The number of bits in the generating matrices is inferred based on the largest value.\n",
+ " | alpha (int): Interlacing factor for higher order nets.\n",
+ " | When `alpha`>1, interlacing is performed regardless of the generating matrices,\n",
+ " | i.e., for `alpha`>1 do *not* pass in generating matrices which are already interlaced.\n",
+ " | The Note for this class contains more info.\n",
+ " | msb (bool): Flag for Most Significant Bit (MSB) vs Least Significant Bit (LSB) integer representations in generating matrices. If `msb=False` (LSB order), then integers in generating matrices will be bit-reversed.\n",
+ " | _verbose (bool): If `True`, print linear matrix scrambling matrices.\n",
+ " |\n",
+ " | ----------------------------------------------------------------------\n",
+ " | Methods inherited from qmcpy.discrete_distribution.abstract_discrete_distribution.AbstractLDDiscreteDistribution:\n",
+ " |\n",
+ " | __repr__(self)\n",
+ " | Return repr(self).\n",
+ " |\n",
+ " | ----------------------------------------------------------------------\n",
+ " | Methods inherited from qmcpy.discrete_distribution.abstract_discrete_distribution.AbstractDiscreteDistribution:\n",
+ " |\n",
+ " | __call__(self, n=None, n_min=None, n_max=None, return_binary=False, warn=True)\n",
+ " | - If just `n` is supplied, generate samples from the sequence at indices 0,...,`n`-1.\n",
+ " | - If `n_min` and `n_max` are supplied, generate samples from the sequence at indices `n_min`,...,`n_max`-1.\n",
+ " | - If `n` and `n_min` are supplied, then generate samples from the sequence at indices `n`,...,`n_min`-1.\n",
+ " |\n",
+ " | Args:\n",
+ " | n (Union[None,int]): Number of points to generate.\n",
+ " | n_min (Union[None,int]): Starting index of sequence.\n",
+ " | n_max (Union[None,int]): Final index of sequence.\n",
+ " | return_binary (bool): Only used for `DigitalNetB2`.\n",
+ " | If `True`, *only* return the integer representation `x_integer` of base 2 digital net.\n",
+ " | warn (bool): If `False`, disable warnings when generating samples.\n",
+ " |\n",
+ " | Returns:\n",
+ " | x (np.ndarray): Samples from the sequence.\n",
+ " |\n",
+ " | - If `replications` is `None` then this will be of size (`n_max`-`n_min`) $\\times$ `dimension`\n",
+ " | - If `replications` is a positive int, then `x` will be of size `replications` $\\times$ (`n_max`-`n_min`) $\\times$ `dimension`\n",
+ " |\n",
+ " | Note that if `return_binary=True` then `x` is returned where `x` are integer representations of the digital net points.\n",
+ " |\n",
+ " | gen_samples(self, n=None, n_min=None, n_max=None, return_binary=False, warn=True)\n",
+ " |\n",
+ " | pdf(self, x)\n",
+ " |\n",
+ " | spawn(self, s=1, dimensions=None)\n",
+ " | Spawn new instances of the current discrete distribution but with new seeds and dimensions.\n",
+ " | Used by multi-level QMC algorithms which require different seeds and dimensions on each level.\n",
+ " |\n",
+ " | Note:\n",
+ " | Use `replications` instead of using `spawn` when possible, e.g., when spawning copies which all have the same dimension.\n",
+ " |\n",
+ " | Args:\n",
+ " | s (int): Number of copies to spawn\n",
+ " | dimensions (np.ndarray): Length `s` array of dimensions for each copy. Defaults to the current dimension.\n",
+ " |\n",
+ " | Returns:\n",
+ " | spawned_discrete_distribs (list): Discrete distributions with new seeds and dimensions.\n",
+ " |\n",
+ " | ----------------------------------------------------------------------\n",
+ " | Data descriptors inherited from qmcpy.discrete_distribution.abstract_discrete_distribution.AbstractDiscreteDistribution:\n",
+ " |\n",
+ " | __dict__\n",
+ " | dictionary for instance variables\n",
+ " |\n",
+ " | __weakref__\n",
+ " | list of weak references to the object\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "['__call__',\n",
+ " '__class__',\n",
+ " '__delattr__',\n",
+ " '__dict__',\n",
+ " '__dir__',\n",
+ " '__doc__',\n",
+ " '__eq__',\n",
+ " '__format__',\n",
+ " '__ge__',\n",
+ " '__getattribute__',\n",
+ " '__getstate__',\n",
+ " '__gt__',\n",
+ " '__hash__',\n",
+ " '__init__',\n",
+ " '__init_subclass__',\n",
+ " '__le__',\n",
+ " '__lt__',\n",
+ " '__module__',\n",
+ " '__ne__',\n",
+ " '__new__',\n",
+ " '__reduce__',\n",
+ " '__reduce_ex__',\n",
+ " '__repr__',\n",
+ " '__setattr__',\n",
+ " '__sizeof__',\n",
+ " '__str__',\n",
+ " '__subclasshook__',\n",
+ " '__weakref__',\n",
+ " '_gen_samples',\n",
+ " '_spawn',\n",
+ " 'gen_samples',\n",
+ " 'pdf',\n",
+ " 'spawn']"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "help(qmcpy.Sobol)\n",
+ "dir(qmcpy.Sobol)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Dogw4vktLGmK"
+ },
+ "source": [
+ "### Stopping criteria\n",
+ "\n",
+ "[GAIL]: http://gailgithub.github.io/GAIL_Dev/ \"Sou-Cheng T. Choi, Yuhan Ding, Fred J. Hickernell, Lan Jiang, Lluis Antoni Jimenez Rugama, Da Li, Jagadeeswaran Rathinavel, Xin Tong, Kan Zhang, Yizhi Zhang, and Xuan Zhou, GAIL: Guaranteed Automatic Integration Library (Version 2.3.1) [MATLAB Software], 2020. Available from `http://gailgithub.github.io/GAIL_Dev/`\"\n",
+ "\n",
+ "\n",
+ "[CubMCML]: https://pubsonline.informs.org/doi/abs/10.1287/opre.1070.0496 \"Giles, M. Multilevel Monte Carlo Path Simulation. Operations Research 56,607–617 (June 2008).\"\n",
+ "\n",
+ "[CubQMCML]: https://www.semanticscholar.org/paper/Multilevel-quasi-Monte-Carlo-path-simulation-Giles-Waterhouse/25a9ca3aa216aa371d1be06fa9b93927187ee4ca \"Giles, M. B. & Waterhouse, B. J. Multilevel quasi-Monte Carlo path simulation. Advanced Financial Modelling, Radon Series on Computational and Applied Mathematics 8,165–181 (2009).\"\n",
+ "\n",
+ "[GilesSoftware]: http://people.maths.ox.ac.uk/~gilesm/mlmc/\n",
+ "\n",
+ "[CubMCG]: https://arxiv.org/abs/1208.4318 \"Hickernell, F.J., Jiang, L., Liu, Y., & Owen, A. Guaranteed Conservative Fixed Width Confidence Intervals via Monte Carlo Sampling in Monte Carlo and Quasi-Monte Carlo Methods 2012 (eds Dick, J., Kuo, F. Y., Peters, G. W., & Sloan, I. H.)\n",
+ "Springer Proceedings in Mathematics and Statistics, vol. 65, (Springer-Verlag, Berlin, 2013), 105–128.\"\n",
+ "\n",
+ "[CubQMCLatticeG]: https://arxiv.org/abs/1411.1966 \"Jiménez Rugama, Ll. A. & Hickernell, F. J. Adaptive Multidimensional Integration Based on Rank-1 Lattices in Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014 (eds Cools, R. & Nuyens,D.) 163.arXiv:1411.1966 (Springer-Verlag, Berlin, 2016), 407–422.\"\n",
+ "\n",
+ "[CubQMCSobolG]: https://arxiv.org/abs/1410.8615 \"Hickernell, F. J. & Jiménez Rugama, Ll. A. Reliable Adaptive Cubature Using Digital Sequences in Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014 (eds Cools, R. & Nuyens,D.) 163.arXiv:1411.1966 (Springer-Verlag, Berlin, 2016), 367--383.\"\n",
+ "\n",
+ "\n",
+ "The stopping criteria implemented come from several sources:\n",
+ "- There are Central Limit Theorem (CLT) criteria for IID and LD sampling\n",
+ "- Rigorous stopping criteria imported from [GAIL] have been implemented for\n",
+ " - [IID][CubMCG] sampling\n",
+ " - [Lattice][CubQMCLatticeG] sampling\n",
+ " - [Sobol'][CubQMCSobolG] sampling\n",
+ "- Multilevel stopping criteria due to [Giles][GilesSoftware] are given for [IID][CubMCML] and [LD][CubQMCML] sampling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3yQgB-Y1f_AQ"
+ },
+ "source": [
+ "#### Changing the tolerance\n",
+ "\n",
+ "If you want to change the error tolerance, without creating a new ``StoppingCriterion`` object, there is a ``set_tolerance`` method."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "kvsEiUG55mEi",
+ "outputId": "43765bc9-fede-4129-f83f-d60d0bb06adf"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Data (Data)\n",
+ " solution 1.135\n",
+ " comb_bound_low 1.129\n",
+ " comb_bound_high 1.142\n",
+ " comb_bound_diff 0.012\n",
+ " comb_flags 1\n",
+ " n_total 2^(13)\n",
+ " n 2^(13)\n",
+ " time_integrate 0.007\n",
+ "CubQMCLatticeG (AbstractStoppingCriterion)\n",
+ " abs_tol 0.010\n",
+ " rel_tol 0\n",
+ " n_init 2^(10)\n",
+ " n_limit 2^(30)\n",
+ "Keister (AbstractIntegrand)\n",
+ "Gaussian (AbstractTrueMeasure)\n",
+ " mean 0\n",
+ " covariance 2^(-1)\n",
+ " decomp_type PCA\n",
+ " transform Gaussian (AbstractTrueMeasure)\n",
+ " mean 0\n",
+ " covariance 2^(-1)\n",
+ " decomp_type PCA\n",
+ "Lattice (AbstractLDDiscreteDistribution)\n",
+ " d 5\n",
+ " replications 1\n",
+ " randomize SHIFT\n",
+ " gen_vec_source kuo.lattice-33002-1024-1048576.9125.txt\n",
+ " order RADICAL INVERSE\n",
+ " n_limit 2^(20)\n",
+ " entropy 7 \n",
+ "\n",
+ "\n",
+ "Data (Data)\n",
+ " solution 1.136\n",
+ " comb_bound_low 1.135\n",
+ " comb_bound_high 1.136\n",
+ " comb_bound_diff 0.001\n",
+ " comb_flags 1\n",
+ " n_total 2^(17)\n",
+ " n 2^(17)\n",
+ " time_integrate 0.040\n",
+ "CubQMCLatticeG (AbstractStoppingCriterion)\n",
+ " abs_tol 0.001\n",
+ " rel_tol 0\n",
+ " n_init 2^(10)\n",
+ " n_limit 2^(30)\n",
+ "Keister (AbstractIntegrand)\n",
+ "Gaussian (AbstractTrueMeasure)\n",
+ " mean 0\n",
+ " covariance 2^(-1)\n",
+ " decomp_type PCA\n",
+ " transform Gaussian (AbstractTrueMeasure)\n",
+ " mean 0\n",
+ " covariance 2^(-1)\n",
+ " decomp_type PCA\n",
+ "Lattice (AbstractLDDiscreteDistribution)\n",
+ " d 5\n",
+ " replications 1\n",
+ " randomize SHIFT\n",
+ " gen_vec_source kuo.lattice-33002-1024-1048576.9125.txt\n",
+ " order RADICAL INVERSE\n",
+ " n_limit 2^(20)\n",
+ " entropy 7 \n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "sc = qmcpy.CubQMCLatticeG(qmcpy.Keister(qmcpy.Gaussian(qmcpy.Lattice(5,seed=7),covariance=1/2)),abs_tol=.01)\n",
+ "print(sc.integrate()[1],'\\n\\n')\n",
+ "sc.set_tolerance(abs_tol=.001) #changing the tolerance\n",
+ "print(sc.integrate()[1],'\\n\\n')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5xDmWnPHE-qY"
+ },
+ "source": [
+ "#### Relative error tolerances\n",
+ "\n",
+ "For some problems a relative error tolerance may make more sense. By default ``rel_tol`` is zero. The stopping criterion stops when either of the two tolerances is met."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "FpKNsUzFFVyt",
+ "outputId": "0a71942d-b3cc-4833-eba5-17d291bf536d"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Data (Data)\n",
+ " solution 1.135\n",
+ " comb_bound_low 1.129\n",
+ " comb_bound_high 1.142\n",
+ " comb_bound_diff 0.012\n",
+ " comb_flags 1\n",
+ " n_total 2^(13)\n",
+ " n 2^(13)\n",
+ " time_integrate 0.004\n",
+ "CubQMCLatticeG (AbstractStoppingCriterion)\n",
+ " abs_tol 0.010\n",
+ " rel_tol 0\n",
+ " n_init 2^(10)\n",
+ " n_limit 2^(30)\n",
+ "Keister (AbstractIntegrand)\n",
+ "Gaussian (AbstractTrueMeasure)\n",
+ " mean 0\n",
+ " covariance 2^(-1)\n",
+ " decomp_type PCA\n",
+ " transform Gaussian (AbstractTrueMeasure)\n",
+ " mean 0\n",
+ " covariance 2^(-1)\n",
+ " decomp_type PCA\n",
+ "Lattice (AbstractLDDiscreteDistribution)\n",
+ " d 5\n",
+ " replications 1\n",
+ " randomize SHIFT\n",
+ " gen_vec_source kuo.lattice-33002-1024-1048576.9125.txt\n",
+ " order RADICAL INVERSE\n",
+ " n_limit 2^(20)\n",
+ " entropy 7 \n",
+ "\n",
+ "\n",
+ "Data (Data)\n",
+ " solution 1.135\n",
+ " comb_bound_low 1.131\n",
+ " comb_bound_high 1.139\n",
+ " comb_bound_diff 0.008\n",
+ " comb_flags 1\n",
+ " n_total 2^(14)\n",
+ " n 2^(14)\n",
+ " time_integrate 0.006\n",
+ "CubQMCLatticeG (AbstractStoppingCriterion)\n",
+ " abs_tol 0\n",
+ " rel_tol 0.005\n",
+ " n_init 2^(10)\n",
+ " n_limit 2^(30)\n",
+ "Keister (AbstractIntegrand)\n",
+ "Gaussian (AbstractTrueMeasure)\n",
+ " mean 0\n",
+ " covariance 2^(-1)\n",
+ " decomp_type PCA\n",
+ " transform Gaussian (AbstractTrueMeasure)\n",
+ " mean 0\n",
+ " covariance 2^(-1)\n",
+ " decomp_type PCA\n",
+ "Lattice (AbstractLDDiscreteDistribution)\n",
+ " d 5\n",
+ " replications 1\n",
+ " randomize SHIFT\n",
+ " gen_vec_source kuo.lattice-33002-1024-1048576.9125.txt\n",
+ " order RADICAL INVERSE\n",
+ " n_limit 2^(20)\n",
+ " entropy 7\n"
+ ]
+ }
+ ],
+ "source": [
+ "sc = qmcpy.CubQMCLatticeG(qmcpy.Keister(qmcpy.Gaussian(qmcpy.Lattice(5,seed=7),covariance=1/2)),abs_tol=.01) #zero relative tolerance by default\n",
+ "print(sc.integrate()[1],'\\n\\n')\n",
+ "sc.set_tolerance(abs_tol=0,rel_tol=0.005) #nonzero relative tolerance and zero absolute tolerance\n",
+ "print(sc.integrate()[1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vJH-4-_1LMOH"
+ },
+ "source": [
+ "### Use cases\n",
+ "\n",
+ "Only a few use cases have been coded so far. These include\n",
+ "* ``qmcpy.keister`` Keiser's example\n",
+ "* European and Asian option pricing (Single and Multi-level)\n",
+ "* Computing Q-Noisy Expected Improvement (qEI) for Bayesian Optimization, see the blog at https://qmcpy.wpcomstaging.com/2020/07/19/qei-with-qmcpy/ and the Colaboratory notebook at https://drive.google.com/drive/folders/1EOREUL7lx4hytuZRQXB50UV8aE9JYN_a\n",
+ "* Importance Sampling\n",
+ "* Acceptance Rejection Sampling\n",
+ "* Custom Sampling by Inverse CDF Transform\n",
+ "\n",
+ "``qmcpy`` would benefit from contributions.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YGwMtWj_Wa41"
+ },
+ "source": [
+ "## Acknowledgements and Contributing\n",
+ "\n",
+ "The ``qmcpy`` code has primarily been written by Aleksei Sorokin (BS/MS expected in 2021), with the generous financial support of SigOpt https://sigopt.com. However, much of the code is adapted from that of our friends (see above)\n",
+ "\n",
+ "We hope that QMCPy will become supported by the community. Your contribution will add value to QMCPy, while allowing you to take advantage of the contribution of others.\n",
+ "\n",
+ "We highlight the value of community owned software."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LZkzVU91mJ_1"
+ },
+ "source": [
+ "### Benefitting from each other's work is easier when we share\n",
+ "\n",
+ "* Most of us are very good at just one or two things:\n",
+ " * LD sequence generators\n",
+ " * Increasing efficiency (e.g., MLMC)\n",
+ " * Stopping criteria\n",
+ " * Realistic use cases\n",
+ "\n",
+ " Having a shared software library let's us take advantage of the best\n",
+ "\n",
+ "* Provides a consistent interface for different pieces from different places\n",
+ "\n",
+ "* Supports reproducible computational research\n",
+ "\n",
+ "* Tedious stuff only needs to be figured out once"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8Gem3kfF6qFt"
+ },
+ "source": [
+ "### A community helps find and correct code errors\n",
+ "\n",
+ "By having more eyes on code than just the developer's we are more likely to spot errors or idiosyncracies. Here are two examples."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OFUXDQWu63T9"
+ },
+ "source": [
+ "#### MATLAB's Sobol' generator\n",
+ "\n",
+ "Several years ago Lluís Antoni Jiménez Rugama discovered that the scrambling of the Sobol' generators implemented in MATLAB's Statistics Toolbox was wrong. After reporting the problem to the developers, it was corrected in MATLAB 2017a"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tOtbO49TSXbS"
+ },
+ "source": [
+ "#### PyTorch's Sobol' generator\n",
+ "\n",
+ "PyTorch is a popular Python library with its own Sobol generator. However, it should be used with care.\n",
+ "\n",
+ "* As noted above, the first point is skipped. \n",
+ "* We found that unless you specify double precision, you get points that have 1 as a coordinate far too often. The developer seemed unaware of this.\n",
+ "\n",
+ "These issues have been reported at https://github.com/pytorch/pytorch/issues/32047.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9KVfvHiym6Pp"
+ },
+ "source": [
+ "###How you can contribute\n",
+ "\n",
+ "After trying QMCPy out help us out by\n",
+ "\n",
+ "**Easy.** \n",
+ "> Submit your bugs and feature requests as issues to https://github.com/QMCSoftware/QMCSoftware/issues\n",
+ "\n",
+ "**Moderately Difficult.** \n",
+ "> Ask your students or collaborators to try QMCPy for their own work and submit their bugs and feature requests\n",
+ "\n",
+ "**Heroic.** \n",
+ "> Add a feature or use case and make a pull request at https://github.com/QMCSoftware/QMCSoftware/pulls so that we can included it in our next release\n",
+ "\n",
+ "Questions? Email us at qmc-software@googlegroups.com\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1q5YeKPMAVuW"
+ },
+ "source": [
+ "## References\n",
+ "\n",
+ "1. S.-C. T. Choi, Y. Ding, F. J. Hickernell, L. Jiang, Ll. A. Jimenez Rugama, D. Li, Jagadeeswaran R., X. Tong, K. Zhang, Y. Zhang, and X. Zhou, GAIL: Guaranteed Automatic Integration Library (Version 2.3.1) [MATLAB Software], 2020. Available from `http://gailgithub.github.io/GAIL_Dev/`\n",
+ "\n",
+ "1. H. Faure and C. Lemieux. “Implementation of Irreducible Sobol’ Sequences in Prime Power Bases,” Mathematics and Computers in Simulation 161 (2019): 13–22.\n",
+ "\n",
+ "1. M. B. Giles. \"Multi-level Monte Carlo path simulation,\" Operations Research, 56(3):607-617, 2008. `http://people.maths.ox.ac.uk/~gilesm/files/OPRE_2008.pdf`.\n",
+ "\n",
+ "1. M. B. Giles. \"Improved multilevel Monte Carlo convergence using the Milstein scheme,\" 343-358, in Monte Carlo and Quasi-Monte Carlo Methods 2006, Springer, 2008. `http://people.maths.ox.ac.uk/~gilesm/files/mcqmc06.pdf`.\n",
+ "\n",
+ "1. M. B. Giles and B. J. Waterhouse. \"Multilevel quasi-Monte Carlo path simulation,\" pp.165-181 in Advanced Financial Modelling, in Radon Series on Computational and Applied Mathematics, de Gruyter, 2009. `http://people.maths.ox.ac.uk/~gilesm/files/radon.pdf`\n",
+ "\n",
+ "1. F. J. Hickernell, L. Jiang, Y. Liu, and A. B. Owen, \"Guaranteed conservative fixed width confidence intervals via Monte Carlo sampling,\" Monte Carlo and Quasi-Monte Carlo Methods 2012 (J. Dick, F.Y. Kuo, G. W. Peters, and I. H. Sloan, eds.), pp. 105-128, Springer-Verlag, Berlin, 2014. DOI: 10.1007/978-3-642-41095-6_5\n",
+ "\n",
+ "1. F. J. Hickernell and Lluis Antoni Jimenez Rugama, \"Reliable adaptive cubature using digital sequences,\" Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014 (R. Cools and D. Nuyens, eds.), Springer Proceedings in Mathematics and Statistics, vol. 163, Springer-Verlag, Berlin, 2016, arXiv:1410.8615 [math.NA], pp. 367-383.\n",
+ "\n",
+ "1. M. Hofert and C. Lemieux (2019). qrng: (Randomized) Quasi-Random Number Generators. R package version 0.0-7. `https://CRAN.R-project.org/package=qrng`.\n",
+ "\n",
+ "1. Ll. A. Jimenez Rugama and F. J. Hickernell, \"Adaptive multidimensional integration based on rank-1 lattices,\" Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014 (R. Cools and D. Nuyens, eds.), Springer Proceedings in Mathematics and Statistics, vol. 163, Springer-Verlag, Berlin, 2016, arXiv:1411.1966, pp. 407-422.\n",
+ "\n",
+ "1. B. D. Keister, Multidimensional Quadrature Algorithms, 'Computers in Physics', *10*, pp. 119-122, 1996.\n",
+ "\n",
+ "1. F. Y. Kuo and D. Nuyens. \"Application of quasi-Monte Carlo methods to elliptic PDEs with random diffusion coefficients - a survey of analysis and implementation,\" Foundations of Computational Mathematics, 16(6):1631-1696, 2016. ([springer link](https://link.springer.com/article/10.1007/s10208-016-9329-5), [arxiv link](https://arxiv.org/abs/1606.06613))\n",
+ "\n",
+ "1. P. L’Ecuyer and D. Munger, \"LatticeBuilder: A General Software Tool for Constructing Rank-1 Lattice Rules,\" ACM Transactions on Mathematical Software. *42* (2015). 10.1145/2754929.\n",
+ "\n",
+ "1. Y. Li, L. Kang, L., and F. J. Hickernell, Is a Transformed Low Discrepancy Design Also Low Discrepancy? in Contemporary Experimental Design, Multivariate Analysis and Data Mining, Festschrift in Honour of Professor Kai-Tai Fang (J. Fan and J. Pan, eds.), p. 69–92, 2020, https://arxiv.org/abs/2004.09887.\n",
+ "\n",
+ "1. A. B. Owen, \"A randomized Halton algorithm in R,\" 2017. arXiv:1706.02808 [stat.CO]\n",
+ "\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "toc_visible": true
+ },
+ "kernelspec": {
+ "display_name": "qmcpy",
+ "language": "python",
+ "name": "python3"
},
- "nbformat": 4,
- "nbformat_minor": 0
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
}
diff --git a/demos/talk_paper_demos/Parslfest_2025/01_sequential.ipynb b/demos/talk_paper_demos/Parslfest_2025/01_sequential.ipynb
index 3367a9ff2..fc0abf424 100644
--- a/demos/talk_paper_demos/Parslfest_2025/01_sequential.ipynb
+++ b/demos/talk_paper_demos/Parslfest_2025/01_sequential.ipynb
@@ -23,6 +23,27 @@
"Our presentation slides for ParslFest are available at [Figma](https://www.figma.com/slides/k7EUosssNluMihkYTLuh1F/Parsl-Testbook-Speedup?node-id=174-95&t=t3jENVMltXWwdLdb-0)."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/talk_paper_demos/Parslfest_2025/01_sequential.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/demos/talk_paper_demos/Parslfest_2025/02_parallel.ipynb b/demos/talk_paper_demos/Parslfest_2025/02_parallel.ipynb
index d8b38bcc4..3db33f15a 100644
--- a/demos/talk_paper_demos/Parslfest_2025/02_parallel.ipynb
+++ b/demos/talk_paper_demos/Parslfest_2025/02_parallel.ipynb
@@ -2,26 +2,33 @@
"cells": [
{
"cell_type": "markdown",
- "id": "37dc281d",
"metadata": {},
"source": [
- "# [ParslFest 2025](https://parsl-project.org/parslfest/parslfest2025.html)\n",
- "\n",
- "# [Accelerating QMCPy Notebook Tests with Parsl](https://www.figma.com/slides/k7EUosssNluMihkYTLuh1F/Parsl-Testbook-Speedup?node-id=1-37&t=WnKcu2QYO8JXvtpP-0)\n",
- "\n",
- "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/talk_paper_demos/Parslfest_2025/02_parallel.ipynb)\n",
- "\n",
- "Joshua Herman, Brandon Sharp, and Sou-Cheng Choi, QMCPy Developers\n",
- "\n",
- "Aug 28 -- 29, 2025\n",
- "\n",
- "Updated: Dec 3, 2025\n",
- "\n",
- "\n",
- "**Requirements**:\n",
- "\n",
- "* testbook : `pip install testbook==0.4.2`\n",
- "* Parsl: `pip install parsl==2025.7.28`"
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/talk_paper_demos/Parslfest_2025/02_parallel.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " import sys\n",
+ " import os\n",
+ " repo_root = \"/content/QMCSoftware\"\n",
+ " notebook_dir = f\"{repo_root}/demos/talk_paper_demos/Parslfest_2025\"\n",
+ " if not os.path.isdir(repo_root):\n",
+ " !git clone -q --depth 1 https://github.com/QMCSoftware/QMCSoftware {repo_root}\n",
+ " !pip install -q qmcpy\n",
+ " !pip install -q parsl\n",
+ " os.chdir(notebook_dir)\n",
+ " if notebook_dir not in sys.path:\n",
+ " sys.path.insert(0, notebook_dir)\n",
+ "except:\n",
+ " pass"
]
},
{
diff --git a/demos/talk_paper_demos/Parslfest_2025/03_visualize_speedup.ipynb b/demos/talk_paper_demos/Parslfest_2025/03_visualize_speedup.ipynb
index 6e50f0b4f..078002436 100644
--- a/demos/talk_paper_demos/Parslfest_2025/03_visualize_speedup.ipynb
+++ b/demos/talk_paper_demos/Parslfest_2025/03_visualize_speedup.ipynb
@@ -1,5 +1,35 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/talk_paper_demos/Parslfest_2025/03_visualize_speedup.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " import sys\n",
+ " import os\n",
+ " repo_root = \"/content/QMCSoftware\"\n",
+ " notebook_dir = f\"{repo_root}/demos/talk_paper_demos/Parslfest_2025\"\n",
+ " if not os.path.isdir(repo_root):\n",
+ " !git clone -q --depth 1 https://github.com/QMCSoftware/QMCSoftware {repo_root}\n",
+ " !pip install -q qmcpy\n",
+ " os.chdir(notebook_dir)\n",
+ " if notebook_dir not in sys.path:\n",
+ " sys.path.insert(0, notebook_dir)\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/demos/talk_paper_demos/Parslfest_2025/output/01_sequential_output.ipynb b/demos/talk_paper_demos/Parslfest_2025/output/01_sequential_output.ipynb
index 14b2f7d33..b047fdd0b 100644
--- a/demos/talk_paper_demos/Parslfest_2025/output/01_sequential_output.ipynb
+++ b/demos/talk_paper_demos/Parslfest_2025/output/01_sequential_output.ipynb
@@ -23,6 +23,27 @@
"Our presentation slides for ParslFest are available at [Figma](https://www.figma.com/slides/k7EUosssNluMihkYTLuh1F/Parsl-Testbook-Speedup?node-id=174-95&t=t3jENVMltXWwdLdb-0)."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/talk_paper_demos/Parslfest_2025/output/01_sequential_output.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/demos/talk_paper_demos/SorokinThesis2025/sorokin_thesis_2025.ipynb b/demos/talk_paper_demos/SorokinThesis2025/sorokin_thesis_2025.ipynb
index 25765ec0d..0a27921b6 100644
--- a/demos/talk_paper_demos/SorokinThesis2025/sorokin_thesis_2025.ipynb
+++ b/demos/talk_paper_demos/SorokinThesis2025/sorokin_thesis_2025.ipynb
@@ -9,6 +9,27 @@
"https://www.arxiv.org/abs/2511.21915"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/talk_paper_demos/SorokinThesis2025/sorokin_thesis_2025.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/demos/talk_paper_demos/pydata_chi_2023.ipynb b/demos/talk_paper_demos/pydata_chi_2023.ipynb
index 54b117eb3..1cbf375b7 100644
--- a/demos/talk_paper_demos/pydata_chi_2023.ipynb
+++ b/demos/talk_paper_demos/pydata_chi_2023.ipynb
@@ -31,6 +31,27 @@
"## Python Setup"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/talk_paper_demos/pydata_chi_2023.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/demos/talk_paper_demos/why_add_q_to_mc_blog/why_add_q_to_mc_blog.ipynb b/demos/talk_paper_demos/why_add_q_to_mc_blog/why_add_q_to_mc_blog.ipynb
index 8709729c3..acafe6061 100644
--- a/demos/talk_paper_demos/why_add_q_to_mc_blog/why_add_q_to_mc_blog.ipynb
+++ b/demos/talk_paper_demos/why_add_q_to_mc_blog/why_add_q_to_mc_blog.ipynb
@@ -1,5 +1,26 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/talk_paper_demos/why_add_q_to_mc_blog/why_add_q_to_mc_blog.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# @title Execute this cell to install dependencies\n",
+ "try:\n",
+ " import google.colab\n",
+ " !pip install -q qmcpy\n",
+ "except:\n",
+ " pass"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/demos/vectorized_qmc.ipynb b/demos/vectorized_qmc.ipynb
index 0cad749cd..18dacbd10 100644
--- a/demos/vectorized_qmc.ipynb
+++ b/demos/vectorized_qmc.ipynb
@@ -1,1299 +1,1282 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "ce064a42",
- "metadata": {
- "id": "ce064a42"
- },
- "source": [
- "# Vectorized QMC"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6df4c2a4-92ec-437e-80a1-28a82ea6544b",
- "metadata": {
- "id": "6df4c2a4-92ec-437e-80a1-28a82ea6544b"
- },
- "source": [
- "[](https://colab.research.google.com/github/QMCSoftware/QMCSoftware/blob/develop/demos/vectorized_qmc.ipynb)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "P32_5cVWUmzj",
- "metadata": {
- "id": "P32_5cVWUmzj"
- },
- "outputs": [],
- "source": [
- "%%capture\n",
- "# @title Execute this cell to install dependancies\n",
- "try:\n",
- " import google.colab\n",
- " import os\n",
- " !pip install -q qmcpy >> /dev/null\n",
- " !apt-get update && apt-get install -y --no-install-recommends texlive-latex-base texlive-fonts-recommended texlive-latex-extra cm-super dvipng\n",
- "except:\n",
- " pass\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "plt.rcParams.update({\n",
- "\"text.usetex\": True,\n",
- "\"font.family\": \"serif\",\n",
- "\"text.latex.preamble\": r\"\\usepackage{amsmath}\\usepackage{amssymb}\\newcommand{\\bx}{\\boldsymbol{x}}\"\n",
- "})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "484d4c41",
- "metadata": {
- "id": "484d4c41"
- },
- "outputs": [],
- "source": [
- "import qmcpy as qp\n",
- "import numpy as np"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "9bf10463",
- "metadata": {
- "id": "9bf10463"
- },
- "outputs": [],
- "source": [
- "from matplotlib import pyplot\n",
- "%matplotlib inline\n",
- "root = None#'./'"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "728a1c89",
- "metadata": {
- "id": "728a1c89"
- },
- "source": [
- "## LD Sequence"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "fabaf64c",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 282
- },
- "id": "fabaf64c",
- "outputId": "7e9be5a7-30ff-450c-9ac8-026e191931e7"
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
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