metta-ul 0.1.0

Basic implementation of clustering algorithms in MeTTa language, including k-means, GMM, spectral clustering, and hierarchical

metta-ul: Clustering Algorithms in MeTTa

Overview

metta-ul is a basic implementation of clustering algorithms in the MeTTa language. It includes implementations of:

• K-Means
• Gaussian Mixture Models (GMM)
• Spectral Clustering
• Hierarchical Clustering

This project is packaged as a Python module and includes a Dockerized environment for running tests using pytest.

Authors

• Ramin Barati - [e-mail]
• Amirhossein Nourani Zadeh - [e-mail]
• Farhoud - [e-mail]

Requirements

• Python 3.7 or later
• Docker
• hyperon >= 0.2.2
• scikit-learn

Installation

Using pip

pip install -e .

Using Docker

Build and run the containerized environment:

docker build . -t metta_ul

Running Tests

Running tests inside Docker

You can run tests using the provided Makefile. This will:

1. Build the Docker image
2. Run tests inside a container
3. Clean up the container after the test run

To execute:

make test

Alternatively, if you want to run pytest directly inside Docker:

docker run -it --rm --mount type=bind,src=$(pwd),dst=/app --name metta_ul_run metta_ul pytest -s

Contributing

1. Fork the repository
2. Create a new branch (feature-branch)
3. Commit changes and push to your branch
4. Submit a pull request

License

This project is licensed under the MIT License.