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

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)

Guassian Mixture Regression Estimation Maximization:

from gmr import GMM

gmm = GMM(n_components=3, random_state=0)
gmm.from_samples(X)
Y_gmm = gmm.predict(np.array([0]), X_test[:, np.newaxis])

Guassian Mixture Regression Incremental Update:

from gmr import IGMM

igmm = IGMM(n=2, sig_init=1.1, T_nov=.1) for i in range(X.shape[0]): igmm.update(X[i,:]) Y_igmm = igmm.predict(np.array([0]), X_test[:, np.newaxis])

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.

from source:

sudo python setup.py install