def _create_model(self, d, k, mode, nframes, emiter):
#+++++++++++++++++++++++++++++++++++++++++++++++++
# Generate a model with k components, d dimensions
#+++++++++++++++++++++++++++++++++++++++++++++++++
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)
#++++++++++++++++++++++++++++++++++++++++++
# Approximate the models with classical EM
#++++++++++++++++++++++++++++++++++++++++++
# Init the model
lgm = GM(d, k, mode)
gmm = GMM(lgm, 'kmean')
gmm.init(data, niter=KM_ITER)
self.gm0 = copy.copy(gmm.gm)
# The actual EM, with likelihood computation
for i in range(emiter):
g, tgd = gmm.compute_responsabilities(data)
gmm.update_em(data, g)
self.data = data
self.gm = lgm
def old_em(data, w, mu, va, niter):
from scikits.learn.machine.em import EM as OEM, GMM as OGMM, GM as OGM
k = w.size
k = mu.shape[0]
lgm = OGM(d, k)
gmm = OGMM(lgm)
gmm.gm.w = w.copy()
gmm.gm.mu = mu.copy()
gmm.gm.va = va.copy()
for i in range(niter):
g, tgd = gmm.compute_responsabilities(data)
gmm.update_em(data, g)
return gmm.gm
def _test_common(self, d, k, mode):
dic = load_dataset('%s_%dd_%dk.mat' % (mode, d, k))
gm = GM.fromvalues(dic['w0'], dic['mu0'], dic['va0'])
gmm = GMM(gm, 'test')
a, na = gmm.compute_responsabilities(dic['data'])
la, nla = gmm.compute_log_responsabilities(dic['data'])
ta = N.log(a)
tna = N.log(na)
if not N.all(N.isfinite(ta)):
print "precision problem for %s, %dd, %dk, test need fixing" % (mode, d, k)
else:
assert_array_almost_equal(ta, la, DEF_DEC)
if not N.all(N.isfinite(tna)):
print "precision problem for %s, %dd, %dk, test need fixing" % (mode, d, k)
else:
assert_array_almost_equal(tna, nla, DEF_DEC)
