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 "
