def _do_mstep(self, stats, params, covars_prior=1e-2, **kwargs):
super(GMMHMM, self)._do_mstep(stats, params)
# All that is left to do is to apply covars_prior to the
# parameters updated in _accumulate_sufficient_statistics.
for state, g in enumerate(self.gmms):
norm = stats['norm'][state]
if 'w' in params:
g.weights = normalize(norm)
if 'm' in params:
g.means = stats['means'][state] / norm[:, np.newaxis]
if 'c' in params:
if g.cvtype == 'tied':
g.covars = ((stats['covars'][state]
+ covars_prior * np.eye(g.n_features))
/ norm.sum())
else:
cvnorm = np.copy(norm)
shape = np.ones(g._covars.ndim)
shape[0] = np.shape(g._covars)[0]
cvnorm.shape = shape
if g.cvtype == 'spherical' or g.cvtype == 'diag':
g.covars = (stats['covars'][state]
+ covars_prior) / cvnorm
elif g.cvtype == 'full':
eye = np.eye(g.n_features)
g.covars = ((stats['covars'][state]
+ covars_prior * eye[np.newaxis, :, :])
/ cvnorm)
def _init(self, obs, params='ste'):
super(MultinomialHMM, self)._init(obs, params=params)
if 'e' in params:
emissionprob = normalize(np.random.rand(self.n_components,
self.n_symbols), 1)
self.emissionprob = emissionprob
def _do_mstep(self, stats, params, **kwargs):
# Based on Huang, Acero, Hon, "Spoken Language Processing",
# p. 443 - 445
if 's' in params:
self.startprob = normalize(
np.maximum(self.startprob_prior - 1.0 + stats['start'], 1e-20))
if 't' in params:
A = self.transmat_prior - 1.0 + stats['trans']
s = np.sum(A,1)
# print(s)
self.transmat = (A.T/s).T
def test_normalize_with_axis_3D(self):
A = np.random.rand(10, 4, 5) + 1.0
for axis in range(3):
Anorm = gmm.normalize(A, axis)
self.assertTrue(np.all(gmm.almost_equal(Anorm.sum(axis), 1.0)))
def test_normalize_2D(self):
A = np.random.rand(10, 4) + 1.0
Anorm = gmm.normalize(A)
self.assertAlmostEqual(Anorm.sum(), 1.0)
def test_normalize_with_axis_3D(self):
A = np.random.rand(10, 4, 5) + 1.0
for axis in range(3):
Anorm = gmm.normalize(A, axis)
self.assertTrue(np.all(gmm.almost_equal(Anorm.sum(axis), 1.0)))
def test_normalize_2D(self):
A = np.random.rand(10, 4) + 1.0
Anorm = gmm.normalize(A)
self.assertAlmostEqual(Anorm.sum(), 1.0)
