Unsupervised Learning of Finite Guassian Mixture Models

Original paper

Unsupervised learning of guassian mixture models uses a minimum message length like criterion to learn the optimal number of components in a finite guassian mixture model.

To install this python package:

pip install mml_gmm

An example jupyter notebook is provided link

The following points were generated using three bivariate guassian distributions. The clustering algorithm correctly converges to those distributions:

It is also possible to visualize this process:

This implementation is a port from the orginal authors matlab code with small modifications and it is built as a sklearn wrapper. The dependencies are:

numpy
scipy
sklearn

To run the example scripts it also advisable to install matplotlib

This code is a work in progress and it needs a lot of refactoring. It is supposed to be compatible with python2 and python3