def test_max_calibrate_sepset_belief(self): belief_propagation = BeliefPropagation(self.junction_tree) belief_propagation.max_calibrate() sepset_belief = belief_propagation.get_sepset_beliefs() phi1 = Factor(['A', 'B'], [2, 3], range(6)) phi2 = Factor(['B', 'C'], [3, 2], range(6)) phi3 = Factor(['C', 'D'], [2, 2], range(4)) b_B = (phi1 * (phi3.maximize(['D'], inplace=False) * phi2).maximize(['C'], inplace=False)).maximize(['A'], inplace=False) b_C = (phi2 * (phi1.maximize(['A'], inplace=False) * phi3.maximize(['D'], inplace=False))).maximize(['B'], inplace=False) np_test.assert_array_almost_equal(sepset_belief[frozenset((('A', 'B'), ('B', 'C')))].values, b_B.values) np_test.assert_array_almost_equal(sepset_belief[frozenset((('B', 'C'), ('C', 'D')))].values, b_C.values)
def test_max_calibrate_sepset_belief(self): belief_propagation = BeliefPropagation(self.junction_tree) belief_propagation.max_calibrate() sepset_belief = belief_propagation.get_sepset_beliefs() phi1 = Factor(['A', 'B'], [2, 3], range(6)) phi2 = Factor(['B', 'C'], [3, 2], range(6)) phi3 = Factor(['C', 'D'], [2, 2], range(4)) b_B = (phi1 * (phi3.maximize(['D'], inplace=False) * phi2).maximize( ['C'], inplace=False)).maximize(['A'], inplace=False) b_C = (phi2 * (phi1.maximize(['A'], inplace=False) * phi3.maximize(['D'], inplace=False))).maximize( ['B'], inplace=False) np_test.assert_array_almost_equal( sepset_belief[frozenset((('A', 'B'), ('B', 'C')))].values, b_B.values) np_test.assert_array_almost_equal( sepset_belief[frozenset((('B', 'C'), ('C', 'D')))].values, b_C.values)
def test_max_calibrate_sepset_belief(self): belief_propagation = BeliefPropagation(self.junction_tree) belief_propagation.max_calibrate() sepset_belief = belief_propagation.get_sepset_beliefs() phi1 = DiscreteFactor(["A", "B"], [2, 3], range(6)) phi2 = DiscreteFactor(["B", "C"], [3, 2], range(6)) phi3 = DiscreteFactor(["C", "D"], [2, 2], range(4)) b_B = (phi1 * (phi3.maximize(["D"], inplace=False) * phi2).maximize( ["C"], inplace=False)).maximize(["A"], inplace=False) b_C = (phi2 * (phi1.maximize(["A"], inplace=False) * phi3.maximize(["D"], inplace=False))).maximize( ["B"], inplace=False) np_test.assert_array_almost_equal( sepset_belief[frozenset((("A", "B"), ("B", "C")))].values, b_B.values) np_test.assert_array_almost_equal( sepset_belief[frozenset((("B", "C"), ("C", "D")))].values, b_C.values)
cpd_var4 = TabularCPD(....) cpd_var5 = TabularCPD(....) model.add_cpds(..........) # Apply propagation belief_propagation = BeliefPropagation(model) # To calibrate the clique tree, use calibrate() method belief_propagation.calibrate() # To get cluster (or clique) beliefs use the corresponding getters belief_propagation.get_clique_beliefs() # To get the sepset beliefs use the corresponding getters belief_propagation.get_sepset_beliefs() >> # Query variables not in the same cluster belief_propagation.query(variables=['no_of_people'], evidence={'location':1, 'quality':1}) >> # Can apply MAP_Query - next belief_propagation.map_query(variables=['no_of_people'], evidence={'location':1, 'quality':1}) " {'no_of_people': 0} " -4- " MAP - Maximize A Posterior Probability "