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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

Science Score: 49.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: arxiv.org, springer.com, ieee.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (13.8%) to scientific vocabulary

Keywords

absorbing-states autoencoder convolution cuda gpu incremental incremental-computation multi-output pattern-recognition propositional-logic regression relational-logic sparse tsetlin-machine
Last synced: 6 months ago · JSON representation

Repository

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.

Basic Info
Statistics
  • Stars: 148
  • Watchers: 12
  • Forks: 26
  • Open Issues: 29
  • Releases: 0
Topics
absorbing-states autoencoder convolution cuda gpu incremental incremental-computation multi-output pattern-recognition propositional-logic regression relational-logic sparse tsetlin-machine
Created about 4 years ago · Last pushed 10 months ago
Metadata Files
Readme License

README.md

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: ```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 git@github.com: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] ```

  1. 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.

Owner

  • Name: Centre for Artificial Intelligence Research (CAIR)
  • Login: cair
  • Kind: organization
  • Email: cair-internal@uia.no
  • Location: Grimstad, Norway

CAIR is a centre for research excellence on artificial intelligence at the University of Agder. We attack unsolved problems, seeking superintelligence.

GitHub Events

Total
  • Issues event: 1
  • Watch event: 32
  • Delete event: 3
  • Issue comment event: 1
  • Push event: 38
  • Pull request event: 6
  • Fork event: 13
  • Create event: 7
Last Year
  • Issues event: 1
  • Watch event: 32
  • Delete event: 3
  • Issue comment event: 1
  • Push event: 38
  • Pull request event: 6
  • Fork event: 13
  • Create event: 7

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 1,143
  • Total Committers: 4
  • Avg Commits per committer: 285.75
  • Development Distribution Score (DDS): 0.144
Past Year
  • Commits: 18
  • Committers: 4
  • Avg Commits per committer: 4.5
  • Development Distribution Score (DDS): 0.333
Top Committers
Name Email Commits
Ole-Christoffer Granmo o****o@u****o 978
Per-Arne Andersen p****r@s****o 152
Mayur1009 m****e@g****m 12
Ahmed Khalid a****m@o****m 1
Committer Domains (Top 20 + Academic)
sysx.no: 1 uia.no: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 51
  • Total pull requests: 34
  • Average time to close issues: 20 days
  • Average time to close pull requests: 11 days
  • Total issue authors: 15
  • Total pull request authors: 5
  • Average comments per issue: 1.12
  • Average comments per pull request: 0.15
  • Merged pull requests: 22
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 8
  • Average time to close issues: N/A
  • Average time to close pull requests: 5 days
  • Issue authors: 2
  • Pull request authors: 4
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.13
  • Merged pull requests: 3
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • olegranmo (17)
  • perara (14)
  • satunheim (4)
  • jesseojuks (3)
  • OleGunvaldsen (1)
  • BooBSD (1)
  • Mayur1009 (1)
  • Defasium (1)
  • anayurg (1)
  • usmananjum (1)
  • akkadhim (1)
  • lowoncuties (1)
  • Baizhou-713 (1)
  • i-am-neo (1)
  • vegarddale (1)
Pull Request Authors
  • perara (24)
  • olegranmo (7)
  • akkadhim (3)
  • Mayur1009 (2)
  • m8pple (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 2
  • Total downloads:
    • pypi 840 last-month
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 1
    (may contain duplicates)
  • Total versions: 49
  • Total maintainers: 1
pypi.org: tmu2

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.

  • Versions: 10
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 57 Last month
Rankings
Dependent packages count: 4.8%
Dependent repos count: 6.3%
Average: 11.2%
Stargazers count: 16.0%
Forks count: 17.7%
Last synced: about 1 year ago
pypi.org: tmu

Your project description

  • Versions: 39
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 783 Last month
Rankings
Stargazers count: 7.1%
Dependent packages count: 10.1%
Downloads: 10.9%
Average: 12.4%
Forks count: 12.6%
Dependent repos count: 21.6%
Maintainers (1)
olegranmo
Last synced: 6 months ago

Dependencies

setup.py pypi
  • cffi >=1.0.0
examples/requirements.txt pypi
  • scikit-learn *
  • tensorflow *
pyproject.toml pypi
  • cffi >=1.0.0
  • numpy *
  • pandas *
  • scikit-learn *
  • tqdm *
scripts/performance_test/requirements.txt pypi
  • GitPython * test
  • pandas * test
.github/workflows/build-docs.yml actions
  • DenverCoder1/doxygen-github-pages-action v1.3.0 composite
.github/workflows/build-tests.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
  • actions/upload-artifact v3 composite
.github/workflows/build-wheels.yml actions
  • actions/checkout v2 composite
  • actions/download-artifact v2 composite
  • actions/github-script v5 composite
  • actions/setup-python v2 composite
  • actions/upload-artifact v2 composite
  • pypa/gh-action-pypi-publish release/v1 composite
  • shogo82148/actions-upload-release-asset v1 composite