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Gaussian Mixture Models in Python

Author: Jeremy Stober Contact: stober@gmail.com Version: 0.01

This is a standalone Pythonic implementation of Gaussian Mixture Models. Various initialization strategies are included along with a standard EM algorithm for determining the model parameters based on data.

Example code for the GMM and Normal classes can be found in the src/test_*.py files. The GMM and the underlying Normal class both support conditioning on data and marginalization for any subset of the variables. This makes this implementation ideal for experimenting with Gaussian Mixture Regression. For example, the following code learns the cosine function:

import numpy as np from gmm import GMM from plot_gmm import draw2dgmm from test_func import noisy_cosine import pylab as pl

x,y = noisy_cosine() data = np.vstack([x,y]).transpose() pl.scatter(data[:,0],data[:,1])

gmm = GMM(dim = 2, ncomps = 2, data = data, method = "kmeans") draw2dgmm(gmm)

nx = np.arange(0,2 * np.pi, 0.1) ny = [] for i in nx:

ngmm = gmm.condition([0],[i]) ny.append(ngmm.mean())

pl.plot(nx,ny,color='red') pl.show()