#+++++++++++++++++++++++++++++++++++++++++++
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)
#++++++++++++++++++++++++
# Learn the model with EM
#++++++++++++++++++++++++
# Init the model
lgm = GM(d, k, mode)
gmm = GMM(lgm, 'kmean')
gmm.init(data)
# Keep a copy for drawing later
gm0 = copy.copy(lgm)
# The actual EM, with likelihood computation. The threshold
# is compared to the (linearly appromixated) derivative of the likelihood
em = EM()
like = em.train(data, gmm, maxiter=30, thresh=1e-8)
#+++++++++++++++
# Draw the model
#+++++++++++++++
import pylab as P
P.subplot(2, 1, 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:
P.plot(data[:, 0], data[:, 1], '.', label='_nolegend_')
k = 2 d = 2 mode = 'diag' nframes = 1e3 #+++++++++++++++++++++++++++++++++++++++++++ # Create an artificial GM model, samples it #+++++++++++++++++++++++++++++++++++++++++++ w, mu, va = GM.gen_param(d, k, mode, spread = 1.5) gm = GM.fromvalues(w, mu, va) # Sample nframes frames from the model data = gm.sample(nframes) #++++++++++++++++++++++++ # Learn the model with EM #++++++++++++++++++++++++ # Create a Model from a Gaussian mixture with kmean initialization lgm = GM(d, k, mode) gmm = GMM(lgm, 'kmean') # The actual EM, with likelihood computation. The threshold # is compared to the (linearly appromixated) derivative of the likelihood em = EM() like = em.train(data, gmm, maxiter = 30, thresh = 1e-8) # The computed parameters are in gmm.gm, which is the same than lgm # (remember, python does not copy most objects by default). You can for example # plot lgm against gm to compare
#++++++++++++++++++++++++
# Learn the model with EM
#++++++++++++++++++++++++
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
