def _reestimate(self,stats,observations):
'''
Required extension of _reestimate.
Adds a re-estimation of the model parameter 'B'.
'''
# re-estimate A, pi
new_model = _BaseHMM._reestimate(self,stats,observations) #@UndefinedVariable
# re-estimate the discrete probability of the observable symbols
B_new = self._reestimateB(observations,stats['gamma'])
new_model['B'] = B_new
return new_model
def _reestimate(self, stats, observations):
'''
Required extension of _reestimate.
Adds a re-estimation of the model parameter 'B'.
'''
# re-estimate A, pi
new_model = _BaseHMM._reestimate(self, stats,
observations) #@UndefinedVariable
# re-estimate the discrete probability of the observable symbols
B_new = self._reestimateB(observations, stats['gamma'])
new_model['B'] = B_new
return new_model
def _reestimate(self,stats,observations): ''' Required extension of _reestimate. Adds a re-estimation of the mixture parameters 'w', 'means', 'covars'. ''' # re-estimate A, pi new_model = _BaseHMM._reestimate(self,stats,observations) #@UndefinedVariable # re-estimate the continuous probability parameters of the mixtures w_new, means_new, covars_new = self._reestimateMixtures(observations,stats['gamma_mix']) new_model['w'] = w_new new_model['means'] = means_new new_model['covars'] = covars_new return new_model
def _reestimate(self, stats, observations):
'''
Required extension of _reestimate.
Adds a re-estimation of the mixture parameters 'w', 'means', 'covars'.
'''
# re-estimate A, pi
new_model = _BaseHMM._reestimate(self, stats,
observations) #@UndefinedVariable
# re-estimate the continuous probability parameters of the mixtures
w_new, means_new, covars_new = self._reestimateMixtures(
observations, stats['gamma_mix'])
new_model['w'] = w_new
new_model['means'] = means_new
new_model['covars'] = covars_new
return new_model