Geometric Algebra for Data Science

Implementation of concepts from the Geometric Algebra for Data Science series by Agus Sudjianto.

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About

This repository provides practical Python/PyTorch implementations with GPU acceleration for the five-track series:

• Track I: Foundations - The Wedge Product and Geometric Product
• Track II: Geometric Statistics - Statistical moments as geometric shapes
• Track III: Geometric Econometrics - Time-series through rotations in phase space
• Track IV: Classical Machine Learning - SVMs, clustering, and Information Volume
• Track V: Deep Geometric Learning - Rotor Layers and Geometric Attention mechanisms

Read the series: https://agussudjianto.substack.com/p/geometric-algebra-for-data-science

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Current Status

Track I, Chapter 1: The Great Embedding - Escaping the Scalar Trap ✅ Read the article

Track I, Chapter 2: Beyond the Arrow - The Wedge Product ✅ Article scheduled for January 14, 2026

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Quick Start

# Clone repository
git clone https://github.com/asudjianto-xml/gaOS.git
cd gaOS

# Install dependencies
pip install torch numpy matplotlib jupyter

# Run examples
cd part_1
python chapter_1_examples.py
python chapter_2_examples.py

# Or use Jupyter notebooks
jupyter lab chapter_1_demo.ipynb
jupyter lab chapter_2_wedge_product_demo.ipynb

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Repository Structure

gaOS/
├── README.md
└── part_1/                                  # Track I: Foundations
    ├── README.md                            # Documentation
    ├── geometric_vector.py                  # Chapter 1: Core implementation
    ├── chapter_1_examples.py                # Chapter 1: Examples
    ├── chapter_1_demo.ipynb                 # Chapter 1: Interactive notebook
    ├── chapter_2_wedge_product.py           # Chapter 2: Wedge product
    ├── chapter_2_examples.py                # Chapter 2: Examples
    └── chapter_2_wedge_product_demo.ipynb   # Chapter 2: Interactive notebook

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Requirements

• Python 3.8+
• PyTorch >= 2.0
• NumPy >= 1.20
• Matplotlib >= 3.3
• Jupyter Lab (optional)

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Citation

Sudjianto, A. (2024). Geometric Algebra for Data Science.
https://agussudjianto.substack.com/p/geometric-algebra-for-data-science

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Contact

• Series Author: Agus Sudjianto on Substack
• Repository Issues: GitHub Issues