Unsupervised Learning of Finite Gaussian Mixture Models

References

[1] M. A. T. Figueiredo and A. K. Jain, "Unsupervised learning of finite mixture models," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 3, pp. 381-396, March 2002.

Installation

Install this python package:

pip install gmm-mml

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
matplotlib (optional)

Usage

The following points were generated using three bivariate Gaussian distributions.

The clustering algorithm correctly converges to those distributions:

from gmm_mml import GmmMml

unsupervised=GmmMml(plots=True)
unsupervised.fit(X)

It is also possible to visualize this process GmmMml(plots=True,live_2d_plot=False):

Available sklearn methods:

• .fit() - fit the finite mixture model
• .fit_transform() - fit and return inputs posterior probability
• .transform() - return inputs posterior probability
• .predict() - return inputs cluster
• .predict_proba() - same as .transform()
• .sample() - sample new data from the fitted mixture model

Examples

On folders ./example_scipts and ./tutorials there are examples on how to use the code

Jupyter notebooks: 2d_Example 1d_Example

TODO

• Refactoring
• Docs
• Support other covariance types (right now only 'full' is supported, i.e., each component has its own general covariance matrix)