def _accumulate_sufficient_statistics(self, stats, obs, framelogprob,
posteriors, fwdlattice, bwdlattice,
params):
super(GMMHMM,
self)._accumulate_sufficient_statistics(stats, obs, framelogprob,
posteriors, fwdlattice,
bwdlattice, params)
for state, g in enumerate(self.gmms_):
_, lgmm_posteriors = g.score_samples(obs)
lgmm_posteriors += np.log(posteriors[:, state][:, np.newaxis] +
np.finfo(np.float).eps)
gmm_posteriors = np.exp(lgmm_posteriors)
tmp_gmm = GaussianMixture(g.n_components,
covariance_type=g.covariance_type)
n_features = g.means_.shape[1]
tmp_gmm._set_covars(
distribute_covar_matrix_to_match_covariance_type(
np.eye(n_features), g.covariance_type, g.n_components))
norm = tmp_gmm._do_mstep(obs, gmm_posteriors, params)
if np.any(np.isnan(tmp_gmm.covars_)):
raise ValueError
stats['norm'][state] += norm
if 'm' in params:
stats['means'][state] += tmp_gmm.means_ * norm[:, np.newaxis]
if 'c' in params:
if tmp_gmm.covariance_type == 'tied':
stats['covars'][state] += tmp_gmm.covars_ * norm.sum()
else:
cvnorm = np.copy(norm)
shape = np.ones(tmp_gmm.covars_.ndim)
shape[0] = np.shape(tmp_gmm.covars_)[0]
cvnorm.shape = shape
stats['covars'][state] += tmp_gmm.covars_ * cvnorm
