def test_logsum_3D():
"""
Test also on a 3D matrix
"""
A = np.random.rand(2, 2, 2) + 1.0
for axis in range(3):
Asum = gmm.logsum(A, axis)
assert_array_almost_equal(np.exp(Asum), np.sum(np.exp(A), axis))
def _accumulate_sufficient_statistics(self, stats, obs, framelogprob,
posteriors, fwdlattice, bwdlattice,
params):
"""
Slight modification of the fit function, just to give us
a reference to the posteriors, so we can modify the observations
(impute them) in the M step.
"""
# keep posteriors
self.curr_posteriors = posteriors
# regulat fit, except M step modified (see below)
#super(GMMHMM_impute,self)._accumulate_sufficient_statistics(stats,
# obs, framelogprob,
# posteriors, fwdlattice, bwdlattice,
# params)
# from _BaseHMM
stats['nobs'] += 1
if 's' in params:
stats['start'] += posteriors[0]
if 't' in params:
for t in xrange(len(framelogprob)):
zeta = (fwdlattice[t - 1][:, np.newaxis] + self._log_transmat +
framelogprob[t] + bwdlattice[t])
stats['trans'] += np.exp(zeta - logsum(zeta))
# from GMMHMM
for state, g in enumerate(self.gmms):
gmm_logprob, gmm_posteriors = g.eval(obs)
# don't learn from missing data!
gmm_posteriors[
self.impute_p1:self.impute_p2, :] = 1. / self.n_states
#posteriors2 = posteriors.copy()
#posteriors2[self.impute_p1:self.impute_p2,:] = 1. / self.n_states
gmm_posteriors *= posteriors[:, state][:, np.newaxis]
subops = np.concatenate(
[obs[:self.impute_p1, :], obs[self.impute_p2:, :]], axis=0)
subposts = np.concatenate([
gmm_posteriors[:self.impute_p1, :],
gmm_posteriors[self.impute_p2:, :]
],
axis=0)
tmpgmm = GMM(g.n_states, g.n_dim, cvtype=g.cvtype)
norm = tmpgmm._do_mstep(subops, subposts, params)
# ******************************
stats['norm'][state] += norm
if 'm' in params:
stats['means'][state] += tmpgmm.means * norm[:, np.newaxis]
if 'c' in params:
if tmpgmm.cvtype == 'tied':
stats['covars'][state] += tmpgmm._covars * norm.sum()
else:
cvnorm = np.copy(norm)
shape = np.ones(tmpgmm._covars.ndim)
shape[0] = np.shape(tmpgmm._covars)[0]
cvnorm.shape = shape
stats['covars'][state] += tmpgmm._covars * cvnorm
def _accumulate_sufficient_statistics(self, stats, obs, framelogprob,
posteriors, fwdlattice, bwdlattice,
params):
"""
Slight modification of the fit function, just to give us
a reference to the posteriors, so we can modify the observations
(impute them) in the M step.
"""
# keep posteriors
self.curr_posteriors = posteriors
# regulat fit, except M step modified (see below)
#super(GMMHMM_impute,self)._accumulate_sufficient_statistics(stats,
# obs, framelogprob,
# posteriors, fwdlattice, bwdlattice,
# params)
# from _BaseHMM
stats['nobs'] += 1
if 's' in params:
stats['start'] += posteriors[0]
if 't' in params:
for t in xrange(len(framelogprob)):
zeta = (fwdlattice[t-1][:,np.newaxis] + self._log_transmat
+ framelogprob[t] + bwdlattice[t])
stats['trans'] += np.exp(zeta - logsum(zeta))
# from GMMHMM
for state,g in enumerate(self.gmms):
gmm_logprob, gmm_posteriors = g.eval(obs)
# don't learn from missing data!
gmm_posteriors[self.impute_p1:self.impute_p2,:] = 1. / self.n_states
#posteriors2 = posteriors.copy()
#posteriors2[self.impute_p1:self.impute_p2,:] = 1. / self.n_states
gmm_posteriors *= posteriors[:,state][:,np.newaxis]
subops = np.concatenate([obs[:self.impute_p1,:],
obs[self.impute_p2:,:]],axis=0)
subposts = np.concatenate([gmm_posteriors[:self.impute_p1,:],
gmm_posteriors[self.impute_p2:,:]],axis=0)
tmpgmm = GMM(g.n_states, g.n_dim, cvtype=g.cvtype)
norm = tmpgmm._do_mstep(subops,subposts,params)
# ******************************
stats['norm'][state] += norm
if 'm' in params:
stats['means'][state] += tmpgmm.means * norm[:,np.newaxis]
if 'c' in params:
if tmpgmm.cvtype == 'tied':
stats['covars'][state] += tmpgmm._covars * norm.sum()
else:
cvnorm = np.copy(norm)
shape = np.ones(tmpgmm._covars.ndim)
shape[0] = np.shape(tmpgmm._covars)[0]
cvnorm.shape = shape
stats['covars'][state] += tmpgmm._covars * cvnorm
def test_logsum_1D():
A = np.random.rand(2) + 1.0
for axis in range(1):
Asum = gmm.logsum(A, axis)
assert_array_almost_equal(np.exp(Asum), np.sum(np.exp(A), axis))
def test_logsum_with_axis_3D(self):
A = np.random.rand(10, 4, 5) + 1.0
for axis in range(3):
Asum = gmm.logsum(A, axis)
assert_array_almost_equal(np.exp(Asum), np.sum(np.exp(A), axis))
def test_logsum_2D(self):
A = np.random.rand(10, 4) + 1.0
Asum = gmm.logsum(A)
self.assertAlmostEqual(np.exp(Asum), np.sum(np.exp(A)))