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GeezerVison is showing github.com — GitHub - cair/tmu: 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 written in Python with wrappers for C and CUDA-based clause evaluation and updating.

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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

  • Core Implementations:

    • Tsetlin Machine
    • Coalesced Tsetlin Machine
    • Convolutional Tsetlin Machine
    • Regression Tsetlin Machine
    • Weighted Tsetlin Machine
    • Autoencoder
    • Multi-task Classifier (Upcoming)
    • One-vs-one Multi-class Classifier (Upcoming)
    • Relational Tsetlin Machine (In Progress)
  • Extended Features:

    • Support for Continuous Features
    • Drop Clause
    • Literal Budget
    • Focused Negative Sampling
    • Type III Feedback
    • Incremental Clause Evaluation (Upcoming)
    • Sparse Computation with Absorbing Actions
    • TMComposites: Plug-and-Play Collaboration Between Specialized Tsetlin Machines (In Progress)
  • Wrappers for C and CUDA-based clause evaluation and updates to enable high-performance computation.

Guides and Tutorials

  • Setting up efficient Development Environment

📦 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 [email protected]: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.


About

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 writte…

pypi.org/project/tmu/

Topics

absorbing-statesautoencoderconvolutioncudagpuincrementalincremental-computationmulti-outputpattern-recognitionpropositional-logicregressionrelational-logicsparsetsetlin-machine

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