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import numpy as N
from numpy.random import seed
from scipy.sandbox.pyem import GM, GMM, EM
import copy
seed(2)
k = 4
d = 2
mode = 'diag'
nframes = 1e3
#+++++++++++++++++++++++++++++++++++++++++++
# Create an artificial GMM model, samples it
#+++++++++++++++++++++++++++++++++++++++++++
w, mu, va = GM.gen_param(d, k, mode, spread = 1.0)
gm = GM.fromvalues(w, mu, va)
# Sample nframes frames from the model
data = gm.sample(nframes)
#++++++++++++++++++++++++
# 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
# its parameters like label or color
h = lgm[k].plot(level = level)
[i.set_color('r') for i in h]
h[0].set_label('EM confidence ellipsoides')
h = gm.plot(level = level)
[i.set_color('g') for i in h]
h[0].set_label('Real confidence ellipsoides')
P.legend(loc = 0)
# depending on your configuration, you may have to call P.show()
# to actually display the figure
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