Dynamic Expectation-Maximization algorithms for Mixed-type Data

This repository provides tools and algorithms for the estimation of mixture models for mixed-type data. The algorithms jointly estimate the model parameters and the number of classes in the model.

This repository corresponds to the implementation of the paper 📄 Solange Pruilh, Stéphanie Allassonnière. Dynamic Expectation-Maximization algorithms for Mixed-type Data. 2024..

Requirements

The code was tested on Python 3.12.14 . In order to run the code, the Python packages listed in requirements.txt are needed. They can be installed for instance with conda.

conda create -n DEM_MD --file requirements.txt
conda activate DEM_MD

Demonstration

A jupyter notebook provides detailed experiments on different use cases: [Tutorial on DEM-MD algorithms](Tutorial on DEM-MD algorithms.ipynb).

Code

Several classes are provided, corresponding to DEM-MD algorithms to estimate different mixture models.

• DEM_MD_gaussian.py, DEM_MD_student.py and DEM_MD_sal.py contains classes to estimate mixture models with respectively Gaussian, Student and Shifted Asymmetric Laplace distributions.
• These three DEM-MD classes are based on base_DEM_MD.py, base_MD.py and base.py which are parents classes.
• utils folder contains several files with tool functions, sampling.py contains a function to sample datasets, calculation.py contains miscellaneous functions.
• history.py contains Historic class, which is directly instantiated into DEM-MD classes.