def _reestimate(self, stats):
new_model = _BaseHMM._reestimate(self, stats)
new_model['LFMparams'] = self._reestimateLFMparams(stats['gammas'])
if self.verbose:
print "CURRENT MODEL PARAMETERS"
print "PI"
print repr(new_model['pi'])
print "A"
print repr(new_model['A'])
print "EMISSION PARAMS: "
print repr(new_model['LFMparams'])
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 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
def _reestimate(self, stats):
new_model = _BaseHMM._reestimate(self, stats)
new_model['ICMparams'] = self._reestimateICMparams(stats['gammas'])
return new_model
