class _ConcentrationResamplingMixin(object):
# NOTE: because all rows share the same concentration parameter, we can't
# use CategoricalAndConcentration; gotta use GammaCompoundDirichlet directly
def __init__(self,num_states,alpha_a_0,alpha_b_0,**kwargs):
self.alpha_obj = GammaCompoundDirichlet(num_states,alpha_a_0,alpha_b_0)
super(_ConcentrationResamplingMixin,self).__init__(
num_states=num_states,alpha=self.alpha,**kwargs)
@property
def alpha(self):
return self.alpha_obj.concentration
@alpha.setter
def alpha(self,alpha):
if alpha is not None:
self.alpha_obj.concentration = alpha # a no-op when called internally
for d in self._row_distns:
d.alpha_0 = alpha
def resample(self,stateseqs=[],trans_counts=None):
trans_counts = self._count_transitions(stateseqs) if trans_counts is None \
else trans_counts
self._resample_alpha(trans_counts)
return super(_ConcentrationResamplingMixin,self).resample(
stateseqs=stateseqs,trans_counts=trans_counts)
def _resample_alpha(self,trans_counts):
self.alpha_obj.resample(trans_counts)
self.alpha = self.alpha_obj.concentration
def meanfieldupdate(self,*args,**kwargs):
raise NotImplementedError # TODO
class _ConcentrationResamplingMixin(object):
# NOTE: because all rows share the same concentration parameter, we can't
# use CategoricalAndConcentration; gotta use GammaCompoundDirichlet directly
def __init__(self,num_states,alpha_a_0,alpha_b_0,**kwargs):
self.alpha_obj = GammaCompoundDirichlet(num_states,alpha_a_0,alpha_b_0)
super(_ConcentrationResamplingMixin,self).__init__(
num_states=num_states,alpha=self.alpha,**kwargs)
@property
def alpha(self):
return self.alpha_obj.concentration
@alpha.setter
def alpha(self,alpha):
if alpha is not None:
self.alpha_obj.concentration = alpha # a no-op when called internally
for d in self._row_distns:
d.alpha_0 = alpha
def resample(self,stateseqs=[],trans_counts=None):
trans_counts = self._count_transitions(stateseqs) if trans_counts is None \
else trans_counts
self._resample_alpha(trans_counts)
return super(_ConcentrationResamplingMixin,self).resample(
stateseqs=stateseqs,trans_counts=trans_counts)
def _resample_alpha(self,trans_counts):
self.alpha_obj.resample(trans_counts)
self.alpha = self.alpha_obj.concentration
def meanfieldupdate(self,*args,**kwargs):
raise NotImplementedError # TODO
def __init__(self,
num_states,
gamma_a_0,
gamma_b_0,
alpha_a_0,
alpha_b_0,
beta=None,
trans_matrix=None,
**kwargs):
if num_states is None:
assert beta is not None or trans_matrix is not None
self.N = len(beta) if beta is not None else trans_matrix.shape[0]
else:
self.N = num_states
self.beta_obj = MultinomialAndConcentration(a_0=gamma_a_0,
b_0=gamma_b_0,
K=self.N,
weights=beta)
self.alpha_obj = GammaCompoundDirichlet(self.N, alpha_a_0, alpha_b_0)
# NOTE: we don't want to call WeakLimitHDPHMMTransitions.__init__
# because it sets beta_obj in a different way
_HMMTransitionsBase.__init__(self,
num_states=self.N,
alphav=self.alpha * self.beta,
trans_matrix=trans_matrix,
**kwargs)
def __init__(self,num_states,alpha_a_0,alpha_b_0,**kwargs):
self.alpha_obj = GammaCompoundDirichlet(num_states,alpha_a_0,alpha_b_0)
super(_ConcentrationResamplingMixin,self).__init__(
num_states=num_states,alpha=self.alpha,**kwargs)
def __init__(self,num_states,alpha_a_0,alpha_b_0,**kwargs):
self.alpha_obj = GammaCompoundDirichlet(num_states,alpha_a_0,alpha_b_0)
super(_ConcentrationResamplingMixin,self).__init__(
num_states=num_states,alpha=self.alpha,**kwargs)
