def generate_dataset(d, k, mode, nframes):
"""Generate a dataset useful for EM anf GMM testing.
returns:
data : ndarray
data from the true model.
tgm : GM
the true model (randomly generated)
gm0 : GM
the initial model
gm : GM
the trained model
"""
# Generate a model
w, mu, va = GM.gen_param(d, k, mode, spread = 2.0)
tgm = GM.fromvalues(w, mu, va)
# Generate data from the model
data = tgm.sample(nframes)
# Run EM on the model, by running the initialization separetely.
gmm = GMM(GM(d, k, mode), 'test')
gmm.init_random(data)
gm0 = copy.copy(gmm.gm)
gmm = GMM(copy.copy(gmm.gm), 'test')
em = EM()
em.train(data, gmm)
return data, tgm, gm0, 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)
def generate_dataset(d, k, mode, nframes):
"""Generate a dataset useful for EM anf GMM testing.
returns:
data : ndarray
data from the true model.
tgm : GM
the true model (randomly generated)
gm0 : GM
the initial model
gm : GM
the trained model
"""
# Generate a model
w, mu, va = GM.gen_param(d, k, mode, spread=2.0)
tgm = GM.fromvalues(w, mu, va)
# Generate data from the model
data = tgm.sample(nframes)
# Run EM on the model, by running the initialization separetely.
gmm = GMM(GM(d, k, mode), 'test')
gmm.init_random(data)
gm0 = copy.copy(gmm.gm)
gmm = GMM(copy.copy(gmm.gm), 'test')
em = EM()
em.train(data, gmm)
return data, tgm, gm0, gmm.gm
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.sufficient_statistics(data)
gmm.update_em(data, g)
self.data = data
self.gm = lgm
