def p_object_given_feature(self,feature, priors): probability_object_is_feature_given_object = self.table_to_function() try: object_given_features=dist.bayesEvidence(priors, probability_object_is_feature_given_object, feature) except: print "feature: ",feature, " not found, will return prior" object_given_features = priors return object_given_features
def getNextValues(self, state, inp): """ @param state: Distribution over states of the subject machine, represented as a C{dist.Dist} object @param inp: A pair C{(o, i)} of the observation (output) and input of the subject machine on this time step. """ (o, i) = inp if self.model.sensorDisplayFun: self.model.sensorDisplayFun(o) sGo = dist.bayesEvidence(state, self.model.observationDistribution, o) if self.verbose: print "after obs", o, sGo dSPrime = dist.totalProbability(sGo, self.model.transitionDistribution(i)) if self.verbose: print "after trans", i, dSPrime if self.model.beliefDisplayFun: self.model.beliefDisplayFun(dSPrime) return (dSPrime, dSPrime)
def getNextValues(self, state, inp): """ @param state: Distribution over states of the subject machine, represented as a C{dist.Dist} object @param inp: A pair C{(o, i)} of the observation (output) and input of the subject machine on this time step. """ (o, i) = inp if self.model.sensorDisplayFun: self.model.sensorDisplayFun(o) sGo = dist.bayesEvidence(state, self.model.observationDistribution, o) if self.verbose: print 'after obs', o, sGo dSPrime = dist.totalProbability(sGo, self.model.transitionDistribution(i)) if self.verbose: print 'after trans', i, dSPrime if self.model.beliefDisplayFun: self.model.beliefDisplayFun(dSPrime) return (dSPrime, dSPrime)