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Implements the Tsetlin Machine, Coalesced Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features, drop clause, Type III Feedback, focused negative sampling, multi-task classifier, autoencoder, literal budget, and one-vs-one multi-class classifier. TMU is writ…

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Tsetlin Machine Unified (TMU) - One Codebase to Rule Them All

License Python Version Maintenance

TMU is a comprehensive repository that encompasses several Tsetlin Machine implementations. Offering a rich set of features and extensions, it serves as a central resource for enthusiasts and researchers alike.

Features

Guides and Tutorials

📦 Installation

Prerequisites for Windows

Before installing TMU on Windows, ensure you have the MSVC build tools. Follow these steps:

  1. Download MSVC build tools
  2. Install the Workloads → Desktop development with C++ package. (Note: The package size is about 6-7GB.)

Dependencies

Ubuntu: sudo apt install libffi-dev

Installing TMU

To get started with TMU, run the following command:

# Installing Stable Branch
pip install git+https://github.com/cair/tmu.git

# Installing Development Branch
pip install git+https://github.com/cair/tmu.git@dev

🛠 Development

If you're looking to contribute or experiment with the codebase, follow these steps:

  1. Clone the Repository:

    git clone -b dev git@github.com:cair/tmu.git && cd tmu
  2. Set Up Development Environment: Navigate to the project directory and compile the C library:

    # Install TMU
     pip install .
    
    # (Alternative): Install TMU in Development Mode
     pip install -e .
    
    # Install TMU-Composite
     pip install .[composite]
    
    # Install TMU-Composite in Development Mode
     pip install -e .[composite]
  3. Starting a New Project: For your projects, simply create a new branch and then within the 'examples' folder, create a new project and initiate your development.


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Implements the Tsetlin Machine, Coalesced Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features, drop clause, Type III Feedback, focused negative sampling, multi-task classifier, autoencoder, literal budget, and one-vs-one multi-class classifier. TMU is writ…

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