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 _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 test_conf_ellip(self):
"""Only test whether the call succeed. To check wether the result is
OK, you have to plot the results."""
d = 3
k = 3
w, mu, va = GM.gen_param(d, k)
gm = GM.fromvalues(w, mu, va)
gm.conf_ellipses()
def test_1d_bogus(self):
"""Check that functions which do not make sense for 1d fail nicely."""
d = 1
k = 2
w, mu, va = GM.gen_param(d, k)
gm = GM.fromvalues(w, mu, va)
try:
gm.conf_ellipses()
raise AssertionError("This should not work !")
except ValueError, e:
print "Ok, conf_ellipses failed as expected (with msg: " + str(e) + ")"
def test_get_va(self):
"""Test _get_va for diag and full mode."""
d = 3
k = 2
ld = 2
dim = [0, 2]
w, mu, va = GM.gen_param(d, k, 'full')
va = N.arange(d*d*k).reshape(d*k, d)
gm = GM.fromvalues(w, mu, va)
tva = N.empty(ld * ld * k)
for i in range(k * ld * ld):
tva[i] = dim[i%ld] + (i % 4)/ ld * dim[1] * d + d*d * (i / (ld*ld))
tva = tva.reshape(ld * k, ld)
sva = gm._get_va(dim)
assert N.all(sva == tva)
def _create_model_and_run_em(self, d, k, mode, nframes):
#+++++++++++++++++++++++++++++++++++++++++++++++++
# 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')
em = EM()
lk = em.train(data, gmm)
def _create_model_and_run_em(self, d, k, mode, nframes):
#+++++++++++++++++++++++++++++++++++++++++++++++++
# 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')
em = EM()
lk = em.train(data, gmm)
