Fit mixture of Gaussian

gaussianMixture.py

from sklearn import mixture
%pylab

def fit_mixture(data, ncomp=2, doplot=False):
    clf = mixture.GMM(n_components=ncomp, covariance_type='full')
    clf.fit(data)
    ml = clf.means_
    wl = clf.weights_
    cl = clf.covars_
    ms = [m[0] for m in ml]
    cs = [numpy.sqrt(c[0][0]) for c in cl]
    ws = [w for w in wl]
    ind = argmax(ws)
    vmin = ms[ind] - cs[ind]
    vmax = ms[ind] + cs[ind]
    if doplot == True:
        histo = hist(data, 200, normed=True)
        for w, m, c in zip(ws, ms, cs):
            plot(histo[1],w*matplotlib.mlab.normpdf(histo[1],m,np.sqrt(c)), linewidth=3)
    return vmin, vmax