https://github.com/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.
https://github.com/cair/tmu

absorbing-states autoencoder convolution cuda gpu incremental incremental-computation multi-output pattern-recognition propositional-logic regression relational-logic sparse tsetlin-machine

Last synced: 2 days ago
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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 written in Python with wrappers for C and CUDA-based clause evaluation and updating.

• Host: GitHub
• URL: https://github.com/cair/tmu
• Owner: cair
• License: mit
• Created: 2021-11-27T16:54:56.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-03-25T09:57:29.000Z (9 months ago)
• Last Synced: 2024-04-14T05:49:00.763Z (9 months ago)
• Topics: absorbing-states, autoencoder, convolution, cuda, gpu, incremental, incremental-computation, multi-output, pattern-recognition, propositional-logic, regression, relational-logic, sparse, tsetlin-machine
• Language: Python
• Homepage: https://pypi.org/project/tmu/
• Size: 248 MB
• Stars: 108
• Watchers: 7
• Forks: 8
• Open Issues: 20
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-tsetlin-machine - Tsetlin Machine Unified - One Codebase to Rule Them All

README

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

![License](https://img.shields.io/github/license/cair/tmu.svg?style=flat-square) ![Python Version](https://img.shields.io/pypi/pyversions/tmu.svg?style=flat-square) ![Maintenance](https://img.shields.io/maintenance/yes/2024?style=flat-square)

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](https://arxiv.org/abs/1804.01508)

    - [Coalesced Tsetlin Machine](https://arxiv.org/abs/2108.07594)

    - [Convolutional Tsetlin Machine](https://arxiv.org/abs/1905.09688)

    - [Regression Tsetlin Machine](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2019.0165)

    - [Weighted Tsetlin Machine](https://ieeexplore.ieee.org/document/9316190)

    - [Autoencoder](https://arxiv.org/abs/2301.00709)

    - Multi-task Classifier *(Upcoming)*

    - One-vs-one Multi-class Classifier *(Upcoming)*

    - [Relational Tsetlin Machine](https://link.springer.com/article/10.1007/s10844-021-00682-5) *(In Progress)*

- Extended Features:

    - [Support for Continuous Features](https://arxiv.org/abs/1905.04199)

    - [Drop Clause](https://arxiv.org/abs/2105.14506)

    - [Literal Budget](https://arxiv.org/abs/2301.08190)

    - [Focused Negative Sampling](https://ieeexplore.ieee.org/document/9923859)

    - [Type III Feedback](https://arxiv.org/abs/2309.06315)

    - Incremental Clause Evaluation *(Upcoming)*

    - [Sparse Computation with Absorbing Actions](https://arxiv.org/abs/2310.11481)

    - TMComposites: Plug-and-Play Collaboration Between Specialized Tsetlin Machines *([In Progress](https://arxiv.org/abs/2309.04801))*

- Wrappers for C and CUDA-based clause evaluation and updates to enable high-performance computation.

## Guides and Tutorials

- [Setting up efficient Development Environment](docs/tutorials/devcontainers/devcontainers.md)

## 📦 Installation

#### **Prerequisites for Windows**

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

1. [Download MSVC build tools](https://visualstudio.microsoft.com/visual-cpp-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:

```bash

# 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**:

   ```bash

   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:

   ```bash

   # 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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