#++++++++++++++++++++++++
# Learn the model with EM
#++++++++++++++++++++++++
# List of learned mixtures lgm[i] is a mixture with i+1 components
lgm = []
kmax = 6
bics = N.zeros(kmax)
em = EM()
for i in range(kmax):
lgm.append(GM(d, i+1, mode))
gmm = GMM(lgm[i], 'kmean')
em.train(data, gmm, maxiter = 30, thresh = 1e-10)
bics[i] = gmm.bic(data)
print "Original model has %d clusters, bics says %d" % (k, N.argmax(bics)+1)
#+++++++++++++++
# Draw the model
#+++++++++++++++
import pylab as P
P.subplot(3, 2, 1)
for k in range(kmax):
P.subplot(3, 2, k+1)
level = 0.9
P.plot(data[:, 0], data[:, 1], '.', label = '_nolegend_')
# h keeps the handles of the plot, so that you can modify
#++++++++++++++++++++++++
lgm = []
kmax = 6
bics = N.zeros(kmax)
for i in range(kmax):
# Init the model with an empty Gaussian Mixture, and create a Gaussian
# Mixture Model from it
lgm.append(GM(d, i+1, mode))
gmm = GMM(lgm[i], 'kmean')
# The actual EM, with likelihood computation. The threshold
# is compared to the (linearly appromixated) derivative of the likelihood
em = EM()
em.train(data, gmm, maxiter = 30, thresh = 1e-10)
bics[i] = gmm.bic(data)
print "Original model has %d clusters, bics says %d" % (k, N.argmax(bics)+1)
#+++++++++++++++
# Draw the model
#+++++++++++++++
import pylab as P
P.subplot(3, 2, 1)
for k in range(kmax):
P.subplot(3, 2, k+1)
# Level is the confidence level for confidence ellipsoids: 1.0 means that
# all points will be (almost surely) inside the ellipsoid
level = 0.8
if not d == 1:
