def test_find_MAP():
print '-' * 80
G = MarkovModel()
G.add_nodes_from(['x1', 'x2', 'x3'])
G.add_edges_from([('x1', 'x2'), ('x1', 'x3')])
phi = [
DiscreteFactor(['x2', 'x1'],
cardinality=[2, 2],
values=np.array([[1.0 / 1, 1.0 / 2], [1.0 / 3,
1.0 / 4]])),
DiscreteFactor(['x3', 'x1'],
cardinality=[2, 2],
values=np.array([[1.0 / 1, 1.0 / 2], [1.0 / 3,
1.0 / 4]]))
]
# DiscreteFactor(['x1'], cardinality=[2],
# values=np.array([2,2]))]
G.add_factors(*phi)
print "nodes:", G.nodes()
bp = BeliefPropagation(G)
bp.max_calibrate()
# bp.calibrate()
clique_beliefs = bp.get_clique_beliefs()
print clique_beliefs
print clique_beliefs[('x1', 'x2')]
print clique_beliefs[('x1', 'x3')]
# print 'partition function should be', np.sum(clique_beliefs[('x1', 'x3')].values)
phi_query = bp._query(['x1', 'x2', 'x3'], operation='maximize')
# phi_query = bp._query(['x1', 'x2', 'x3'], operation='marginalize')
print phi_query
sleep(52)
def find_MAP_val(G):
'''
Inputs:
- G: MarkovModel
'''
bp = BeliefPropagation(G)
bp.max_calibrate()
clique_beliefs = bp.get_clique_beliefs()
map_val = np.max(clique_beliefs.values()[0].values)
return map_val
def find_MAP_state(G):
'''
Inputs:
- G: MarkovModel
'''
bp = BeliefPropagation(G)
bp.max_calibrate()
clique_beliefs = bp.get_clique_beliefs()
phi_query = bp._query(G.nodes(), operation='maximize')
print phi_query
return phi_query
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 = Factor(['A', 'B'], [2, 3], range(6))
phi2 = Factor(['B', 'C'], [3, 2], range(6))
phi3 = Factor(['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)
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_clique_belief(self):
belief_propagation = BeliefPropagation(self.junction_tree)
belief_propagation.max_calibrate()
clique_belief = belief_propagation.get_clique_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_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)
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)