Gaussian Mixture Models (GMMs) for clustering and regression in Python.

Original repository: https://github.com/AlexanderFabisch/gmr

Changes made from the original repository:

• Implementation of a method gmm.condition_derivative( indices, x ) to compute the gradient of the conditional expectation.
• Addition of example scripts to test the computation of the gradient in 1D and 2D.
• Computation and plotting of the log-likelihood at each iteration during training.
• Interruptible training.

Estimate GMM from samples and sample from GMM:

from gmr import GMM

gmm = GMM(n_components=3, random_state=random_state)
gmm.from_samples(X)
X_sampled = gmm.sample(100)

For more details, see:

help(gmr)

There is an implementation of Gaussian Mixture Models for clustering in scikit-learn as well. Regression could not be easily integrated in the interface of sklearn. That is the reason why I put the code in a separate repository.

Install from PyPI:

sudo pip install gmr

or from source:

sudo python setup.py install