Metadata-Version: 2.1
Name: mulearn
Version: 1.1.2
Summary: A python package for inducing membership functions from labeled data
Author-email: Dario Malchiodi <dario.malchiodi@unimi.it>
License: Apache-2.0
Project-URL: Homepage, https://github.com/dariomalchiodi/mulearn
Project-URL: Documentation, https://mulearn.readthedocs.io/
Project-URL: Issues, https://github.com/dariomalchiodi/mulearn/issues
Keywords: fuzzy set,fuzzy membership,machine learning
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: gurobipy
Requires-Dist: json_fix
Requires-Dist: numpy<2.0
Requires-Dist: tqdm
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: tensorflow==2.17

# mulearn

[![Documentation Status](https://readthedocs.org/projects/mulearn/badge/?version=latest)](https://mulearn.readthedocs.io/en/latest/?badge=latest)

> A python package for inducing membership functions from labeled data


mulearn is a python package implementing the metodology for data-driven induction of fuzzy sets described in

- D. Malchiodi and W. Pedrycz, _Learning Membership Functions for Fuzzy Sets through Modified Support Vector Clustering_, in F. Masulli, G. Pasi e R. Yager (Eds.), Fuzzy Logic and Applications. 10th International Workshop, WILF 2013, Genoa, Italy, November 19–22, 2013. Proceedings., Vol. 8256, Springer International Publishing, Switzerland, Lecture Notes on Artificial Intelligence, 2013;
- D. Malchiodi and A. G. B. Tettamanzi, _Predicting the Possibilistic Score of OWL Axioms through Modified Support Vector Clustering_, in H. Haddad, R. L. Wainwright e R. Chbeir (Eds.), SAC'18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, ACM (ISBN 9781450351911), 1984–1991, 2018.

## Install

The package can easily be installed:

- via `pip`, by running `pip install mulearn` in a terminal;
- cloning this repo.

APIs are described at https://mulearn.readthedocs.io/.
