def perform_ve_inference(self, query_variable, evidence_variables):
"""Calculates the probability distribution P(query_variable|evidence_variables) using variable elimination.
Assumes that we have only one query variable.
Keyword arguments:
query_variable -- The name of the query variable (as called in the network).
evidence_variables -- A dictionary containing variable names as keys and observed values as values.
Returns:
distribution -- A dictionary containing the values of the query variable as keys and the probabilities as values.
"""
known_variables = list(evidence_variables.keys())
known_variables.append(query_variable)
hidden_variables = []
for _,var in enumerate(self.network.V):
if var not in known_variables:
hidden_variables.append(var)
variables, dependency_levels = self.__find_dependency_levels(known_variables, hidden_variables)
#we don't want the query variable to be considered as known
#in the factor creation process
known_variables.remove(query_variable)
factors = self.__create_factors(evidence_variables, variables, dependency_levels)
current_factor = Factor()
for i in xrange(len(hidden_variables)-1, -1, -1):
current_factor = self.__multiply_factors(factors, current_factor, dependency_levels, i)
current_factor.sum_out(hidden_variables[i])
current_factor = self.__multiply_factors(factors, current_factor, dependency_levels, -1)
alpha = sum(numpy.array(current_factor.probabilities))
distribution = dict()
for i,value in enumerate(current_factor.values):
distribution[value[0]] = current_factor.probabilities[i] / alpha
return distribution
