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Open Source Science

https://github.com/cair/dpcl-classifier

https://github.com/cair/dpcl-classifier

Science Score: 13.0%

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

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    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
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    DOI references
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    Academic publication links
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    JOSS paper metadata
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    Scientific vocabulary similarity
    Low similarity (12.1%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: cair
  • Language: Python
  • Default Branch: main
  • Size: 91.8 KB
Statistics
  • Stars: 0
  • Watchers: 3
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 3 years ago · Last pushed over 1 year ago
Metadata Files
Readme

README.md

Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning

License: MIT Python Version TensorFlow Version

Table of Contents

Introduction

Tsetlin Machines (TMs) have garnered increasing interest for their ability to learn concepts via propositional formulas and their proven efficiency across various application domains. Despite this, the convergence proof for the TMs, particularly for the AND operator (conjunction of literals), in the generalized case (inputs greater than two bits) remains an open problem. This paper aims to fill this gap by presenting a comprehensive convergence analysis of Tsetlin automaton-based Machine Learning algorithms. We introduce a novel framework, referred to as Probabilistic Concept Learning (PCL), which simplifies the TM structure while incorporating dedicated feedback mechanisms and dedicated inclusion/exclusion probabilities for literals. Given $n$ features, PCL aims to learn a set of conjunction clauses $Ci$ each associated with a distinct inclusion probability $pi$. Most importantly, we establish a theoretical proof confirming that, for any clause $Ck$, PCL converges to a conjunction of literals when $0.5 < pk <1 $. This result serves as a stepping stone for future research on the convergence properties of Tsetlin automaton-based learning algorithms. Our findings not only contribute to the theoretical understanding of Tsetlin Machines but also have implications for their practical application, potentially leading to more robust and interpretable machine learning models.

Installation

Prerequisites

  • Python 3.11 or higher
  • Poetry package manager

Steps

  1. Clone the repository: bash git clone https://github.com/cair/dpcl-classifier.git

  2. Navigate to the project directory:

bash cd dpcl-classifier

  1. Install the dependencies using Poetry:

bash poetry install

Usage

You can run the main script as follows: bash python classifier_numba.py

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

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
  • Push event: 1
  • Public event: 1
Last Year
  • Push event: 1
  • Public event: 1

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 26
  • Total Committers: 2
  • Avg Commits per committer: 13.0
  • Development Distribution Score (DDS): 0.346
Past Year
  • Commits: 1
  • Committers: 1
  • Avg Commits per committer: 1.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
BachirNILU 1****U 17
Per-Arne Andersen p****r@s****o 9
Committer Domains (Top 20 + Academic)
sysx.no: 1

Issues and Pull Requests

Last synced: about 1 year ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Dependencies

pyproject.toml pypi
  • jupyter ^1.0.0
  • keras ^2.13.1
  • loguru ^0.7.0
  • numba ^0.57.1
  • numba-progress ^1.1.0
  • pandas ^2.0.3
  • python >=3.11,<3.12
  • scikit-learn ^1.3.0
  • scipy ^1.11.2
  • tensorflow ^2.13.0
  • tqdm ^4.66.1