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)
def test_max_calibrate_clique_belief(self): belief_propagation = BeliefPropagation(self.junction_tree) belief_propagation.max_calibrate() clique_belief = belief_propagation.get_clique_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_A_B = phi1 * (phi3.maximize(['D'], inplace=False) * phi2).maximize( ['C'], inplace=False) b_B_C = phi2 * (phi1.maximize(['A'], inplace=False) * phi3.maximize(['D'], inplace=False)) b_C_D = phi3 * (phi1.maximize(['A'], inplace=False) * phi2).maximize( ['B'], inplace=False) np_test.assert_array_almost_equal(clique_belief[('A', 'B')].values, b_A_B.values) np_test.assert_array_almost_equal(clique_belief[('B', 'C')].values, b_B_C.values) np_test.assert_array_almost_equal(clique_belief[('C', 'D')].values, b_C_D.values)