felipetufaile / mixturemodels

Mixture Models Library

Welcome to the Mixture Models Library repository! This repository contains a Python library that provides implementations and utilities for working with mixture models.

Features

• Gaussian Mixture Models (GMMs): Includes methods for fitting Gaussian mixture models to data, estimating parameters, and performing probabilistic inference.

Installation

You can install the library using pip:

pip install MixtureModels

Getting Started

To get started with the library, refer to the documentation for detailed usage examples, API reference, and tutorials.

from MixtureModels import GaussianMixtureModel

# Fit a Gaussian Mixture Model to data
gmm = GaussianMixtureModel(n_components=3)
gmm.fit(data)

# Perform inference on new data points
probabilities = gmm.predict_proba(new_data)

Contributing

Contributions are welcome! If you have any ideas, bug reports, or feature requests, please submit an issue or a pull request following our contribution guidelines.

License

This project is licensed under the MIT License. Feel free to use, modify, and distribute this library according to the terms of the license.

Acknowledgments

We would like to thank the contributors for their valuable contributions to this project.

If you find this library helpful in your research or work, please consider citing it:

@misc{mixturemodelslibrary,
  author = {Felipe Tufaile},
  title = {MixtureModels},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/FelipeTufaile/MixtureModels}},
}

Thank you for using the Mixture Models Library! We hope it helps you in your data analysis and modeling tasks.