def test_get_cardinality_check_cardinality(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = DiscreteFactor(['a', 'b'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1)
self.assertRaises(ValueError,
self.graph.get_cardinality,
check_cardinality=True)
phi2 = DiscreteFactor(['a', 'c'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi2)
self.assertRaises(ValueError,
self.graph.get_cardinality,
check_cardinality=True)
phi3 = DiscreteFactor(['c', 'd'], [2, 2], np.random.rand(4))
self.graph.add_factors(phi3)
self.assertDictEqual(
self.graph.get_cardinality(check_cardinality=True), {
'd': 2,
'c': 2,
'b': 2,
'a': 1
})
def test_remove_factors(self):
self.graph.add_edges_from([[('a', 'b'), ('b', 'c')]])
phi1 = DiscreteFactor(['a', 'b'], [2, 2], np.random.rand(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], np.random.rand(4))
self.graph.add_factors(phi1, phi2)
self.graph.remove_factors(phi1)
six.assertCountEqual(self, self.graph.factors, [phi2])
def setUp(self):
self.factor1 = DiscreteFactor(['a', 'b'], [2, 2], np.random.rand(4))
self.factor2 = DiscreteFactor(['b', 'c'], [2, 2], np.random.rand(4))
self.factor3 = DiscreteFactor(['d', 'e'], [2, 2], np.random.rand(4))
self.factor4 = DiscreteFactor(['e', 'f'], [2, 2], np.random.rand(4))
self.factor5 = DiscreteFactor(['a', 'b', 'e'], [2, 2, 2],
np.random.rand(8))
self.graph1 = JunctionTree()
self.graph1.add_edge(('a', 'b'), ('b', 'c'))
self.graph1.add_factors(self.factor1, self.factor2)
self.graph2 = JunctionTree()
self.graph2.add_nodes_from([('a', 'b'), ('b', 'c'), ('d', 'e')])
self.graph2.add_edge(('a', 'b'), ('b', 'c'))
self.graph2.add_factors(self.factor1, self.factor2, self.factor3)
self.graph3 = JunctionTree()
self.graph3.add_edges_from([(('a', 'b'), ('b', 'c')),
(('d', 'e'), ('e', 'f'))])
self.graph3.add_factors(self.factor1, self.factor2, self.factor3,
self.factor4)
self.graph4 = JunctionTree()
self.graph4.add_edges_from([(('a', 'b', 'e'), ('b', 'c')),
(('a', 'b', 'e'), ('e', 'f')),
(('d', 'e'), ('e', 'f'))])
self.graph4.add_factors(self.factor5, self.factor2, self.factor3,
self.factor4)
def test_remove_multiple_factors(self):
self.graph.add_nodes_from(['a', 'b', 'c'])
phi1 = DiscreteFactor(['a', 'b'], [2, 2], range(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], range(4))
self.graph.add_factors(phi1, phi2)
self.graph.remove_factors(phi1, phi2)
six.assertCountEqual(self, self.graph.factors, [])
def test_partition_function(self):
self.graph.add_nodes_from(['a', 'b', 'c'])
phi1 = DiscreteFactor(['a', 'b'], [2, 2], range(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], range(4))
self.graph.add_factors(phi1, phi2)
self.graph.add_edges_from([('a', 'b'), ('b', 'c')])
self.assertEqual(self.graph.get_partition_function(), 22.0)
def test_copy_with_factors(self):
self.graph.add_edges_from([[('a', 'b'), ('b', 'c')]])
phi1 = DiscreteFactor(['a', 'b'], [2, 2], np.random.rand(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], np.random.rand(4))
self.graph.add_factors(phi1, phi2)
graph_copy = self.graph.copy()
self.assertIsInstance(graph_copy, JunctionTree)
self.assertIsNot(self.graph, graph_copy)
self.assertEqual(hf.recursive_sorted(self.graph.nodes()),
hf.recursive_sorted(graph_copy.nodes()))
self.assertEqual(hf.recursive_sorted(self.graph.edges()),
hf.recursive_sorted(graph_copy.edges()))
self.assertTrue(graph_copy.check_model())
self.assertEqual(self.graph.get_factors(), graph_copy.get_factors())
self.graph.remove_factors(phi1, phi2)
self.assertTrue(phi1 not in self.graph.factors
and phi2 not in self.graph.factors)
self.assertTrue(phi1 in graph_copy.factors
and phi2 in graph_copy.factors)
self.graph.add_factors(phi1, phi2)
self.graph.factors[0] = DiscreteFactor(['a', 'b'], [2, 2],
np.random.rand(4))
self.assertNotEqual(self.graph.get_factors()[0],
graph_copy.get_factors()[0])
self.assertNotEqual(self.graph.factors, graph_copy.factors)
def test_get_factors(self):
self.graph.add_nodes_from(['a', 'b', 'c'])
phi1 = DiscreteFactor(['a', 'b'], [2, 2], range(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], range(4))
six.assertCountEqual(self, self.graph.get_factors(), [])
self.graph.add_factors(phi1, phi2)
six.assertCountEqual(self, self.graph.get_factors(), [phi1, phi2])
def setUp(self):
self.junction_tree = JunctionTree([(('A', 'B'), ('B', 'C')),
(('B', 'C'), ('C', 'D'))])
phi1 = DiscreteFactor(['A', 'B'], [2, 3], range(6))
phi2 = DiscreteFactor(['B', 'C'], [3, 2], range(6))
phi3 = DiscreteFactor(['C', 'D'], [2, 2], range(4))
self.junction_tree.add_factors(phi1, phi2, phi3)
self.bayesian_model = BayesianModel([('A', 'J'), ('R', 'J'),
('J', 'Q'), ('J', 'L'),
('G', 'L')])
cpd_a = TabularCPD('A', 2, values=[[0.2], [0.8]])
cpd_r = TabularCPD('R', 2, values=[[0.4], [0.6]])
cpd_j = TabularCPD('J',
2,
values=[[0.9, 0.6, 0.7, 0.1], [0.1, 0.4, 0.3, 0.9]],
evidence=['A', 'R'],
evidence_card=[2, 2])
cpd_q = TabularCPD('Q',
2,
values=[[0.9, 0.2], [0.1, 0.8]],
evidence=['J'],
evidence_card=[2])
cpd_l = TabularCPD('L',
2,
values=[[0.9, 0.45, 0.8, 0.1],
[0.1, 0.55, 0.2, 0.9]],
evidence=['J', 'G'],
evidence_card=[2, 2])
cpd_g = TabularCPD('G', 2, values=[[0.6], [0.4]])
self.bayesian_model.add_cpds(cpd_a, cpd_g, cpd_j, cpd_l, cpd_q, cpd_r)
def test_get_cardinality(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
self.assertDictEqual(self.graph.get_cardinality(), {})
phi1 = DiscreteFactor(['a', 'b'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1)
self.assertDictEqual(self.graph.get_cardinality(), {'a': 1, 'b': 2})
self.graph.remove_factors(phi1)
self.assertDictEqual(self.graph.get_cardinality(), {})
phi1 = DiscreteFactor(['a', 'b'], [2, 2], np.random.rand(4))
phi2 = DiscreteFactor(['c', 'd'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1, phi2)
self.assertDictEqual(self.graph.get_cardinality(), {
'd': 2,
'a': 2,
'b': 2,
'c': 1
})
phi3 = DiscreteFactor(['d', 'a'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi3)
self.assertDictEqual(self.graph.get_cardinality(), {
'd': 1,
'c': 1,
'b': 2,
'a': 2
})
self.graph.remove_factors(phi1, phi2, phi3)
self.assertDictEqual(self.graph.get_cardinality(), {})
def test_add_multiple_factors(self):
phi1 = DiscreteFactor(['a', 'b'], [2, 2], np.random.rand(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], np.random.rand(4))
self.graph.add_edges_from([('a', phi1), ('b', phi1), ('b', phi2),
('c', phi2)])
self.graph.add_factors(phi1, phi2)
six.assertCountEqual(self, self.graph.factors, [phi1, phi2])
def setUp(self):
# A test Bayesian model
diff_cpd = TabularCPD('diff', 2, [[0.6], [0.4]])
intel_cpd = TabularCPD('intel', 2, [[0.7], [0.3]])
grade_cpd = TabularCPD('grade',
3,
[[0.3, 0.05, 0.9, 0.5], [0.4, 0.25, 0.08, 0.3],
[0.3, 0.7, 0.02, 0.2]],
evidence=['diff', 'intel'],
evidence_card=[2, 2])
self.bayesian_model = BayesianModel()
self.bayesian_model.add_nodes_from(['diff', 'intel', 'grade'])
self.bayesian_model.add_edges_from([('diff', 'grade'),
('intel', 'grade')])
self.bayesian_model.add_cpds(diff_cpd, intel_cpd, grade_cpd)
# A test Markov model
self.markov_model = MarkovModel([('A', 'B'), ('C', 'B'), ('B', 'D')])
factor_ab = DiscreteFactor(['A', 'B'], [2, 3], [1, 2, 3, 4, 5, 6])
factor_cb = DiscreteFactor(['C', 'B'], [4, 3],
[3, 1, 4, 5, 7, 8, 1, 3, 10, 4, 5, 6])
factor_bd = DiscreteFactor(['B', 'D'], [3, 2], [5, 7, 2, 1, 9, 3])
self.markov_model.add_factors(factor_ab, factor_cb, factor_bd)
self.gibbs = GibbsSampling(self.bayesian_model)
def test_get_variable_nodes(self):
phi1 = DiscreteFactor(['a', 'b'], [2, 2], np.random.rand(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], np.random.rand(4))
self.graph.add_edges_from([('a', phi1), ('b', phi1), ('b', phi2),
('c', phi2)])
self.graph.add_factors(phi1, phi2)
six.assertCountEqual(self, self.graph.get_variable_nodes(),
['a', 'b', 'c'])
def test_get_factors(self):
self.graph.add_edges_from([[('a', 'b'), ('b', 'c')]])
phi1 = DiscreteFactor(['a', 'b'], [2, 2], np.random.rand(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], np.random.rand(4))
six.assertCountEqual(self, self.graph.get_factors(), [])
self.graph.add_factors(phi1, phi2)
self.assertEqual(self.graph.get_factors(node=('b', 'a')), phi1)
self.assertEqual(self.graph.get_factors(node=('b', 'c')), phi2)
six.assertCountEqual(self, self.graph.get_factors(), [phi1, phi2])
def test_check_model1(self):
self.graph.add_edges_from([(('a', 'b'), ('a', 'c')),
(('a', 'c'), ('a', 'd'))])
phi1 = DiscreteFactor(['a', 'b'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1)
self.assertRaises(ValueError, self.graph.check_model)
phi2 = DiscreteFactor(['a', 'c'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi2)
self.assertRaises(ValueError, self.graph.check_model)
def test_cardinality_mismatch_raises_error(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
factor_list = [
DiscreteFactor(edge, [2, 2], np.random.rand(4))
for edge in self.graph.edges()
]
self.graph.add_factors(*factor_list)
self.graph.add_factors(
DiscreteFactor(['a', 'b'], [2, 3], np.random.rand(6)))
self.assertRaises(ValueError, self.graph.triangulate)
def test_check_model(self):
self.graph.add_edges_from([(('a', 'b'), ('a', 'c'))])
phi1 = DiscreteFactor(['a', 'b'], [1, 2], np.random.rand(2))
phi2 = DiscreteFactor(['a', 'c'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi1, phi2)
self.assertTrue(self.graph.check_model())
self.graph.remove_factors(phi2)
phi2 = DiscreteFactor(['a', 'c'], [1, 2], np.random.rand(2))
self.graph.add_factors(phi2)
self.assertTrue(self.graph.check_model())
def test_triangulation_h6_create_new(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = DiscreteFactor(['a', 'b'], [2, 3], np.random.rand(6))
phi2 = DiscreteFactor(['b', 'c'], [3, 4], np.random.rand(12))
phi3 = DiscreteFactor(['c', 'd'], [4, 5], np.random.rand(20))
phi4 = DiscreteFactor(['d', 'a'], [5, 2], np.random.random(10))
self.graph.add_factors(phi1, phi2, phi3, phi4)
H = self.graph.triangulate(heuristic='H6', inplace=True)
self.assertListEqual(
hf.recursive_sorted(H.edges()),
[['a', 'b'], ['a', 'd'], ['b', 'c'], ['b', 'd'], ['c', 'd']])
def test_check_model3(self):
self.graph.add_nodes_from(['a', 'b', 'c'])
phi1 = DiscreteFactor(['a', 'b'], [2, 2], np.random.rand(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], np.random.rand(4))
phi3 = DiscreteFactor(['a', 'c'], [2, 2], np.random.rand(4))
self.graph.add_nodes_from([phi1, phi2])
self.graph.add_edges_from([('a', phi1), ('b', phi1), ('b', phi2),
('c', phi2)])
self.graph.add_factors(phi1, phi2, phi3)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi3)
self.assertTrue(self.graph.check_model())
def test_to_junction_tree(self):
phi1 = DiscreteFactor(['a', 'b'], [2, 2], np.random.rand(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], np.random.rand(4))
self.graph.add_edges_from([('a', phi1), ('b', phi1), ('b', phi2),
('c', phi2)])
self.graph.add_factors(phi1, phi2)
jt = self.graph.to_junction_tree()
self.assertIsInstance(jt, JunctionTree)
self.assertListEqual(hf.recursive_sorted(jt.nodes()),
[['a', 'b'], ['b', 'c']])
self.assertEqual(len(jt.edges()), 1)
def test_bayesian_model(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = DiscreteFactor(['a', 'b'], [2, 3], np.random.rand(6))
phi2 = DiscreteFactor(['b', 'c'], [3, 4], np.random.rand(12))
phi3 = DiscreteFactor(['c', 'd'], [4, 5], np.random.rand(20))
phi4 = DiscreteFactor(['d', 'a'], [5, 2], np.random.random(10))
self.graph.add_factors(phi1, phi2, phi3, phi4)
bm = self.graph.to_bayesian_model()
self.assertIsInstance(bm, BayesianModel)
self.assertListEqual(sorted(bm.nodes()), ['a', 'b', 'c', 'd'])
self.assertTrue(nx.is_chordal(bm.to_undirected()))
def test_junction_tree(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = DiscreteFactor(['a', 'b'], [2, 3], np.random.rand(6))
phi2 = DiscreteFactor(['b', 'c'], [3, 4], np.random.rand(12))
phi3 = DiscreteFactor(['c', 'd'], [4, 5], np.random.rand(20))
phi4 = DiscreteFactor(['d', 'a'], [5, 2], np.random.random(10))
self.graph.add_factors(phi1, phi2, phi3, phi4)
junction_tree = self.graph.to_junction_tree()
self.assertListEqual(hf.recursive_sorted(junction_tree.nodes()),
[['a', 'b', 'd'], ['b', 'c', 'd']])
self.assertEqual(len(junction_tree.edges()), 1)
def test_to_markov_model(self):
phi1 = DiscreteFactor(['a', 'b'], [2, 2], np.random.rand(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], np.random.rand(4))
self.graph.add_edges_from([('a', phi1), ('b', phi1), ('b', phi2),
('c', phi2)])
self.graph.add_factors(phi1, phi2)
mm = self.graph.to_markov_model()
self.assertIsInstance(mm, MarkovModel)
self.assertListEqual(sorted(mm.nodes()), ['a', 'b', 'c'])
self.assertListEqual(hf.recursive_sorted(mm.edges()),
[['a', 'b'], ['b', 'c']])
self.assertListEqual(sorted(mm.get_factors(), key=lambda x: x.scope()),
[phi1, phi2])
def test_check_model(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = DiscreteFactor(['a', 'b'], [1, 2], np.random.rand(2))
phi2 = DiscreteFactor(['c', 'b'], [3, 2], np.random.rand(6))
phi3 = DiscreteFactor(['c', 'd'], [3, 4], np.random.rand(12))
phi4 = DiscreteFactor(['d', 'a'], [4, 1], np.random.rand(4))
self.graph.add_factors(phi1, phi2, phi3, phi4)
self.assertTrue(self.graph.check_model())
self.graph.remove_factors(phi1, phi4)
phi1 = DiscreteFactor(['a', 'b'], [4, 2], np.random.rand(8))
self.graph.add_factors(phi1)
self.assertTrue(self.graph.check_model())
def test_copy_with_factorchanges(self):
self.graph.add_edges_from([[('a', 'b'), ('b', 'c')]])
phi1 = DiscreteFactor(['a', 'b'], [2, 2], np.random.rand(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], np.random.rand(4))
self.graph.add_factors(phi1, phi2)
graph_copy = self.graph.copy()
self.graph.factors[0].reduce([('a', 0)])
self.assertNotEqual(self.graph.factors[0].scope(),
graph_copy.factors[0].scope())
self.assertNotEqual(self.graph, graph_copy)
self.graph.factors[1].marginalize(['b'])
self.assertNotEqual(self.graph.factors[1].scope(),
graph_copy.factors[1].scope())
self.assertNotEqual(self.graph, graph_copy)
def test_get_factors(self):
phi1 = DiscreteFactor(['a', 'b'], [2, 2], np.random.rand(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], np.random.rand(4))
self.graph.add_edges_from([('a', phi1), ('b', phi1), ('b', phi2),
('c', phi2)])
six.assertCountEqual(self, self.graph.get_factors(), [])
self.graph.add_factors(phi1, phi2)
self.assertEqual(self.graph.get_factors(node=phi1), phi1)
self.assertEqual(self.graph.get_factors(node=phi2), phi2)
six.assertCountEqual(self, self.graph.get_factors(), [phi1, phi2])
self.graph.remove_factors(phi1)
self.assertRaises(ValueError, self.graph.get_factors, node=phi1)
def test_check_model(self):
self.graph.add_nodes_from(['a', 'b', 'c'])
phi1 = DiscreteFactor(['a', 'b'], [2, 2], np.random.rand(4))
phi2 = DiscreteFactor(['b', 'c'], [2, 2], np.random.rand(4))
self.graph.add_nodes_from([phi1, phi2])
self.graph.add_edges_from([('a', phi1), ('b', phi1), ('b', phi2),
('c', phi2)])
self.graph.add_factors(phi1, phi2)
self.assertTrue(self.graph.check_model())
self.graph.remove_factors(phi1)
self.graph.remove_node(phi1)
phi1 = DiscreteFactor(['a', 'b'], [4, 2], np.random.rand(8))
self.graph.add_factors(phi1)
self.graph.add_edges_from([('a', phi1)])
self.assertTrue(self.graph.check_model())
def test_factor_graph(self):
phi1 = DiscreteFactor(['Alice', 'Bob'], [3, 2], np.random.rand(6))
phi2 = DiscreteFactor(['Bob', 'Charles'], [2, 2], np.random.rand(4))
self.graph.add_edges_from([('Alice', 'Bob'), ('Bob', 'Charles')])
self.graph.add_factors(phi1, phi2)
factor_graph = self.graph.to_factor_graph()
self.assertIsInstance(factor_graph, FactorGraph)
self.assertListEqual(
sorted(factor_graph.nodes()),
['Alice', 'Bob', 'Charles', 'phi_Alice_Bob', 'phi_Bob_Charles'])
self.assertListEqual(
hf.recursive_sorted(factor_graph.edges()),
[['Alice', 'phi_Alice_Bob'], ['Bob', 'phi_Alice_Bob'],
['Bob', 'phi_Bob_Charles'], ['Charles', 'phi_Bob_Charles']])
self.assertListEqual(factor_graph.get_factors(), [phi1, phi2])
def __init__(self, intersection_set_variables, cluster_potential):
"""
Initialization of the current cluster
"""
# The variables with which the cluster is made of.
self.cluster_variables = frozenset(cluster_potential.scope())
# The cluster potentials must be specified before only.
self.cluster_potential = copy.deepcopy(cluster_potential)
# Generate intersection sets for this cluster; S(c)
self.intersection_sets_for_cluster_c = [intersect.intersection(self.cluster_variables)
for intersect in intersection_set_variables
if intersect.intersection(self.cluster_variables)]
# Initialize messages from this cluster to its respective intersection sets
# \lambda_{c \rightarrow \s} = 0
self.message_from_cluster = {}
for intersection in self.intersection_sets_for_cluster_c:
# Present variable. It can be a node or an edge too. (that is ['A'] or ['A', 'C'] too)
present_variables = list(intersection)
# Present variables cardinality
present_variables_card = cluster_potential.get_cardinality(present_variables)
present_variables_card = [present_variables_card[var] for var in present_variables]
# We need to create a new factor whose messages are blank
self.message_from_cluster[intersection] = \
DiscreteFactor(present_variables, present_variables_card, np.zeros(np.prod(present_variables_card)))
def test_check_model1(self):
self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
('d', 'a')])
phi1 = DiscreteFactor(['a', 'b'], [1, 2], np.random.rand(2))
phi2 = DiscreteFactor(['b', 'c'], [3, 3], np.random.rand(9))
self.graph.add_factors(phi1, phi2)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi2)
phi3 = DiscreteFactor(['c', 'a'], [4, 4], np.random.rand(16))
self.graph.add_factors(phi3)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi3)
phi2 = DiscreteFactor(['b', 'c'], [2, 3], np.random.rand(6))
phi3 = DiscreteFactor(['c', 'd'], [3, 4], np.random.rand(12))
phi4 = DiscreteFactor(['d', 'a'], [4, 3], np.random.rand(12))
self.graph.add_factors(phi2, phi3, phi4)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi2, phi3, phi4)
phi2 = DiscreteFactor(['a', 'b'], [1, 3], np.random.rand(3))
self.graph.add_factors(phi1, phi2)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi2)
def test_check_model2(self):
self.graph.add_edges_from([(('a', 'b'), ('a', 'c')),
(('a', 'c'), ('a', 'd'))])
phi1 = DiscreteFactor(['a', 'b'], [1, 2], np.random.rand(2))
phi2 = DiscreteFactor(['a', 'c'], [3, 3], np.random.rand(9))
phi3 = DiscreteFactor(['a', 'd'], [4, 4], np.random.rand(16))
self.graph.add_factors(phi1, phi2, phi3)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi2)
phi2 = DiscreteFactor(['a', 'c'], [1, 3], np.random.rand(3))
self.graph.add_factors(phi2)
self.assertRaises(ValueError, self.graph.check_model)
self.graph.remove_factors(phi3)
phi3 = DiscreteFactor(['a', 'd'], [1, 4], np.random.rand(4))
self.graph.add_factors(phi3)
self.assertTrue(self.graph.check_model())