def i_logp(self, index):
"""
Evaluates the log-probability of the Markov blanket of
a stochastic owning a particular index.
"""
all_relevant_stochastics = set()
p,i = self.stochastic_indices[index]
try:
return p.logp + logp_of_set(p.extended_children)
except ZeroProbability:
return -Inf
def get_logp_plus_loglike(self):
return logp_of_set(self.variables_with_logp)
def _get_logp_plus_loglike(self):
sum = logp_of_set(self.markov_blanket)
if self.verbose>1:
print '\t' + self._id + ' Current log-likelihood plus current log-probability', sum
return sum
def _get_loglike(self):
# Fetch log-probability (as sum of childrens' log probability)
sum = logp_of_set(self.children)
if self.verbose>1:
print '\t' + self._id + ' Current log-likelihood ', sum
return sum
def get_logp_plus_loglike(self):
return logp_of_set(self.variables_with_logp)
def _get_logp_plus_loglike(self):
sum = logp_of_set(self.markov_blanket)
if self.verbose>1:
print '\t' + self._id + ' Current log-likelihood plus current log-probability', sum
return sum
def _get_loglike(self):
# Fetch log-probability (as sum of childrens' log probability)
sum = logp_of_set(self.children)
if self.verbose>1:
print '\t' + self._id + ' Current log-likelihood ', sum
return sum