Python models.MarkovModel类代码示例

class TestInferenceBase(unittest.TestCase):
    def setUp(self):
        self.bayesian = BayesianModel([('a', 'b'), ('b', 'c'), ('c', 'd'), ('d', 'e')])
        a_cpd = TabularCPD('a', 2, [[0.4, 0.6]])
        b_cpd = TabularCPD('b', 2, [[0.2, 0.4], [0.3, 0.4]], evidence='a', evidence_card=[2])
        c_cpd = TabularCPD('c', 2, [[0.1, 0.2], [0.3, 0.4]], evidence='b', evidence_card=[2])
        d_cpd = TabularCPD('d', 2, [[0.4, 0.3], [0.2, 0.1]], evidence='c', evidence_card=[2])
        e_cpd = TabularCPD('e', 2, [[0.3, 0.2], [0.4, 0.1]], evidence='d', evidence_card=[2])
        self.bayesian.add_cpd([a_cpd, b_cpd, c_cpd, d_cpd, e_cpd])

        self.markov = MarkovModel([('a', 'b'), ('b', 'd'), ('a', 'c'), ('c', 'd')])
        factor_1 = Factor(['a', 'b'], [2, 2], np.array([100, 1, 1, 100]))
        factor_2 = Factor(['a', 'c'], [2, 2], np.array([40, 30, 100, 20]))
        factor_3 = Factor(['b', 'd'], [2, 2], np.array([1, 100, 100, 1]))
        factor_4 = Factor(['c', 'd'], [2, 2], np.array([60, 60, 40, 40]))
        self.markov.add_factors(factor_1, factor_2, factor_3, factor_4)

    def test_bayesian_inference_init(self):
        infer_bayesian = Inference(self.bayesian)
        self.assertEqual(set(infer_bayesian.variables), {'a', 'b', 'c', 'd', 'e'})
        self.assertEqual(infer_bayesian.cardinality, {'a': 2, 'b': 2, 'c': 2, 'd': 2, 'e': 2})
        # self.assertEqual(infer_bayesian.factors, {'a': [self.bayesian.get_cpd('a').to_factor(),
        #                                                 self.bayesian.get_cpd('b').to_factor()],
        #                                           'b': [self.bayesian.get_cpd('b').to_factor(),
        #                                                 self.bayesian.get_cpd('c').to_factor()],
        #                                           'c': [self.bayesian.get_cpd('c').to_factor(),
        #                                                 self.bayesian.get_cpd('d').to_factor()],
        #                                           'd': [self.bayesian.get_cpd('d').to_factor(),
        #                                                 self.bayesian.get_cpd('e').to_factor()],
        #                                           'e': [self.bayesian.get_cpd('e').to_factor()]})

    def test_markov_inference_init(self):
        infer_markov = Inference(self.markov)
        self.assertEqual(set(infer_markov.variables), {'a', 'b', 'c', 'd'})
        self.assertEqual(infer_markov.cardinality, {'a': 2, 'b': 2, 'c': 2, 'd': 2})

def test_markovmodel():
    """
        >>> from ibeis.algo.hots.pgm_ext import *  # NOQA
    """
    from pgmpy.models import MarkovModel
    from pgmpy.factors import Factor
    markovmodel = MarkovModel([('A', 'B'), ('B', 'C'), ('C', 'D'), ('D', 'A')])
    factor_a_b = Factor(variables=['A', 'B'], cardinality=[2, 2], values=[100, 5, 5, 100])
    factor_b_c = Factor(variables=['B', 'C'], cardinality=[2, 2], values=[100, 3, 2, 4])
    factor_c_d = Factor(variables=['C', 'D'], cardinality=[2, 2], values=[3, 5, 1, 6])
    factor_d_a = Factor(variables=['D', 'A'], cardinality=[2, 2], values=[6, 2, 56, 2])
    markovmodel.add_factors(factor_a_b, factor_b_c, factor_c_d, factor_d_a)

    pgm_viz.show_markov_model(markovmodel)
    pgm_viz.show_junction_tree(markovmodel)

    def setUp(self):
        self.maxDiff = None
        edges = [['family-out', 'dog-out'],
                 ['bowel-problem', 'dog-out'],
                 ['family-out', 'light-on'],
                 ['dog-out', 'hear-bark']]
        cpds = {'bowel-problem': np.array([[0.01],
                                           [0.99]]),
                'dog-out': np.array([[0.99, 0.01, 0.97, 0.03],
                                     [0.9, 0.1, 0.3, 0.7]]),
                'family-out': np.array([[0.15],
                                        [0.85]]),
                'hear-bark': np.array([[0.7, 0.3],
                                       [0.01, 0.99]]),
                'light-on': np.array([[0.6, 0.4],
                                      [0.05, 0.95]])}
        states = {'bowel-problem': ['true', 'false'],
                  'dog-out': ['true', 'false'],
                  'family-out': ['true', 'false'],
                  'hear-bark': ['true', 'false'],
                  'light-on': ['true', 'false']}
        parents = {'bowel-problem': [],
                   'dog-out': ['bowel-problem', 'family-out'],
                   'family-out': [],
                   'hear-bark': ['dog-out'],
                   'light-on': ['family-out']}

        self.bayesmodel = BayesianModel(edges)

        tabular_cpds = []
        for var, values in cpds.items():
            cpd = TabularCPD(var, len(states[var]), values,
                             evidence=parents[var],
                             evidence_card=[len(states[evidence_var])
                                            for evidence_var in parents[var]])
            tabular_cpds.append(cpd)
        self.bayesmodel.add_cpds(*tabular_cpds)
        self.bayeswriter = UAIWriter(self.bayesmodel)

        edges = {('var_0', 'var_1'), ('var_0', 'var_2'), ('var_1', 'var_2')}
        self.markovmodel = MarkovModel(edges)
        tables = [(['var_0', 'var_1'],
                   ['4.000', '2.400', '1.000', '0.000']),
                  (['var_0', 'var_1', 'var_2'],
                   ['2.2500', '3.2500', '3.7500', '0.0000', '0.0000', '10.0000',
                    '1.8750', '4.0000', '3.3330', '2.0000', '2.0000', '3.4000'])]
        domain = {'var_1': '2', 'var_2': '3', 'var_0': '2'}
        factors = []
        for table in tables:
            variables = table[0]
            cardinality = [int(domain[var]) for var in variables]
            values = list(map(float, table[1]))
            factor = DiscreteFactor(variables, cardinality, values)
            factors.append(factor)
        self.markovmodel.add_factors(*factors)
        self.markovwriter = UAIWriter(self.markovmodel)

 def __init__(self, adj_mat=None, struct=None):
     DEBUG = False
     self.G = MarkovModel()
     self.n_nodes = adj_mat.shape[0]
     if DEBUG: print 'struct', struct
     if struct == 'complete':
         self._complete_graph(adj_mat)
     if struct == 'nodes':
         self._nodes_only(adj_mat)
     if struct is None:
         self._import_adj(adj_mat)
     self._ising_factors(Wf=5, Wi=5, f_type='mixed')
     if DEBUG: print 'generate_init', self.G, self.G.nodes()

    def to_markov_model(self):
        """
        Converts bayesian model to markov model. The markov model created would
        be the moral graph of the bayesian model.

        Examples
        --------
        >>> from pgmpy.models import BayesianModel
        >>> G = BayesianModel([('diff', 'grade'), ('intel', 'grade'),
        ...                    ('intel', 'SAT'), ('grade', 'letter')])
        >>> mm = G.to_markov_model()
        >>> mm.nodes()
        ['diff', 'grade', 'intel', 'SAT', 'letter']
        >>> mm.edges()
        [('diff', 'intel'), ('diff', 'grade'), ('intel', 'grade'),
        ('intel', 'SAT'), ('grade', 'letter')]
        """
        from pgmpy.models import MarkovModel
        moral_graph = self.moralize()
        mm = MarkovModel(moral_graph.edges())
        mm.add_factors(*[cpd.to_factor() for cpd in self.cpds])

        return mm

    def setUp(self):
        self.bayesian = BayesianModel([('a', 'b'), ('b', 'c'), ('c', 'd'), ('d', 'e')])
        a_cpd = TabularCPD('a', 2, [[0.4, 0.6]])
        b_cpd = TabularCPD('b', 2, [[0.2, 0.4], [0.3, 0.4]], evidence='a', evidence_card=[2])
        c_cpd = TabularCPD('c', 2, [[0.1, 0.2], [0.3, 0.4]], evidence='b', evidence_card=[2])
        d_cpd = TabularCPD('d', 2, [[0.4, 0.3], [0.2, 0.1]], evidence='c', evidence_card=[2])
        e_cpd = TabularCPD('e', 2, [[0.3, 0.2], [0.4, 0.1]], evidence='d', evidence_card=[2])
        self.bayesian.add_cpd([a_cpd, b_cpd, c_cpd, d_cpd, e_cpd])

        self.markov = MarkovModel([('a', 'b'), ('b', 'd'), ('a', 'c'), ('c', 'd')])
        factor_1 = Factor(['a', 'b'], [2, 2], np.array([100, 1, 1, 100]))
        factor_2 = Factor(['a', 'c'], [2, 2], np.array([40, 30, 100, 20]))
        factor_3 = Factor(['b', 'd'], [2, 2], np.array([1, 100, 100, 1]))
        factor_4 = Factor(['c', 'd'], [2, 2], np.array([60, 60, 40, 40]))
        self.markov.add_factors(factor_1, factor_2, factor_3, factor_4)

    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 = Factor(['A', 'B'], [2, 3], [1, 2, 3, 4, 5, 6])
        factor_cb = Factor(['C', 'B'], [4, 3], [3, 1, 4, 5, 7, 8, 1, 3, 10, 4, 5, 6])
        factor_bd = Factor(['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 setUp(self):
        # It is just a moralised version of the above Bayesian network so all the results are same. Only factors
        # are under consideration for inference so this should be fine.
        self.markov_model = MarkovModel([('A', 'J'), ('R', 'J'), ('J', 'Q'), ('J', 'L'),
                                         ('G', 'L'), ('A', 'R'), ('J', 'G')])

        factor_a = TabularCPD('A', 2, values=[[0.2], [0.8]]).to_factor()
        factor_r = TabularCPD('R', 2, values=[[0.4], [0.6]]).to_factor()
        factor_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]).to_factor()
        factor_q = TabularCPD('Q', 2, values=[[0.9, 0.2], [0.1, 0.8]],
                              evidence=['J'], evidence_card=[2]).to_factor()
        factor_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]).to_factor()
        factor_g = TabularCPD('G', 2, [[0.6], [0.4]]).to_factor()

        self.markov_model.add_factors(factor_a, factor_r, factor_j, factor_q, factor_l, factor_g)
        self.markov_inference = VariableElimination(self.markov_model)

    def to_markov_model(self):
        """
        Converts the factor graph into markov model.

        A markov model contains nodes as random variables and edge between
        two nodes imply interaction between them.

        Examples
        --------
        >>> from pgmpy.models import FactorGraph
        >>> from pgmpy.factors import Factor
        >>> G = FactorGraph()
        >>> G.add_nodes_from(['a', 'b', 'c'])
        >>> phi1 = Factor(['a', 'b'], [2, 2], np.random.rand(4))
        >>> phi2 = Factor(['b', 'c'], [2, 2], np.random.rand(4))
        >>> G.add_factors(phi1, phi2)
        >>> G.add_nodes_from([phi1, phi2])
        >>> G.add_edges_from([('a', phi1), ('b', phi1),
        ...                   ('b', phi2), ('c', phi2)])
        >>> mm = G.to_markov_model()
        """
        from pgmpy.models import MarkovModel
        mm = MarkovModel()

        variable_nodes = self.get_variable_nodes()

        if len(set(self.nodes()) - set(variable_nodes)) != len(self.factors):
            raise ValueError('Factors not associated with all the factor nodes.')

        mm.add_nodes_from(variable_nodes)
        for factor in self.factors:
            scope = factor.scope()
            mm.add_edges_from(itertools.combinations(scope, 2))
            mm.add_factors(factor)

        return mm

class TestUndirectedGraphTriangulation(unittest.TestCase):
    def setUp(self):
        self.graph = MarkovModel()

    def test_check_clique(self):
        self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'a')])
        self.assertTrue(self.graph.check_clique(['a', 'b', 'c']))

    def test_is_triangulated(self):
        self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'a')])
        self.assertTrue(self.graph.is_triangulated())

    def test_triangulation_h1_inplace(self):
        self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
                                   ('d', 'a')])
        phi1 = Factor(['a', 'b'], [2, 3], np.random.rand(6))
        phi2 = Factor(['b', 'c'], [3, 4], np.random.rand(12))
        phi3 = Factor(['c', 'd'], [4, 5], np.random.rand(20))
        phi4 = Factor(['d', 'a'], [5, 2], np.random.random(10))
        self.graph.add_factors(phi1, phi2, phi3, phi4)
        self.graph.triangulate(heuristic='H1', inplace=True)
        self.assertTrue(self.graph.is_triangulated())
        self.assertListEqual(hf.recursive_sorted(self.graph.edges()),
                             [['a', 'b'], ['a', 'c'], ['a', 'd'],
                              ['b', 'c'], ['c', 'd']])

    def test_triangulation_h2_inplace(self):
        self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
                                   ('d', 'a')])
        phi1 = Factor(['a', 'b'], [2, 3], np.random.rand(6))
        phi2 = Factor(['b', 'c'], [3, 4], np.random.rand(12))
        phi3 = Factor(['c', 'd'], [4, 5], np.random.rand(20))
        phi4 = Factor(['d', 'a'], [5, 2], np.random.random(10))
        self.graph.add_factors(phi1, phi2, phi3, phi4)
        self.graph.triangulate(heuristic='H2', inplace=True)
        self.assertTrue(self.graph.is_triangulated())
        self.assertListEqual(hf.recursive_sorted(self.graph.edges()),
                             [['a', 'b'], ['a', 'c'], ['a', 'd'],
                              ['b', 'c'], ['c', 'd']])

    def test_triangulation_h3_inplace(self):
        self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
                                   ('d', 'a')])
        phi1 = Factor(['a', 'b'], [2, 3], np.random.rand(6))
        phi2 = Factor(['b', 'c'], [3, 4], np.random.rand(12))
        phi3 = Factor(['c', 'd'], [4, 5], np.random.rand(20))
        phi4 = Factor(['d', 'a'], [5, 2], np.random.random(10))
        self.graph.add_factors(phi1, phi2, phi3, phi4)
        self.graph.triangulate(heuristic='H3', inplace=True)
        self.assertTrue(self.graph.is_triangulated())
        self.assertListEqual(hf.recursive_sorted(self.graph.edges()),
                             [['a', 'b'], ['a', 'd'], ['b', 'c'],
                              ['b', 'd'], ['c', 'd']])

    def test_triangulation_h4_inplace(self):
        self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
                                   ('d', 'a')])
        phi1 = Factor(['a', 'b'], [2, 3], np.random.rand(6))
        phi2 = Factor(['b', 'c'], [3, 4], np.random.rand(12))
        phi3 = Factor(['c', 'd'], [4, 5], np.random.rand(20))
        phi4 = Factor(['d', 'a'], [5, 2], np.random.random(10))
        self.graph.add_factors(phi1, phi2, phi3, phi4)
        self.graph.triangulate(heuristic='H4', inplace=True)
        self.assertTrue(self.graph.is_triangulated())
        self.assertListEqual(hf.recursive_sorted(self.graph.edges()),
                             [['a', 'b'], ['a', 'd'], ['b', 'c'],
                              ['b', 'd'], ['c', 'd']])

    def test_triangulation_h5_inplace(self):
        self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
                                   ('d', 'a')])
        phi1 = Factor(['a', 'b'], [2, 3], np.random.rand(6))
        phi2 = Factor(['b', 'c'], [3, 4], np.random.rand(12))
        phi3 = Factor(['c', 'd'], [4, 5], np.random.rand(20))
        phi4 = Factor(['d', 'a'], [5, 2], np.random.random(10))
        self.graph.add_factors(phi1, phi2, phi3, phi4)
        self.graph.triangulate(heuristic='H4', inplace=True)
        self.assertTrue(self.graph.is_triangulated())
        self.assertListEqual(hf.recursive_sorted(self.graph.edges()),
                             [['a', 'b'], ['a', 'd'], ['b', 'c'],
                              ['b', 'd'], ['c', 'd']])

    def test_triangulation_h6_inplace(self):
        self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
                                   ('d', 'a')])
        phi1 = Factor(['a', 'b'], [2, 3], np.random.rand(6))
        phi2 = Factor(['b', 'c'], [3, 4], np.random.rand(12))
        phi3 = Factor(['c', 'd'], [4, 5], np.random.rand(20))
        phi4 = Factor(['d', 'a'], [5, 2], np.random.random(10))
        self.graph.add_factors(phi1, phi2, phi3, phi4)
        self.graph.triangulate(heuristic='H4', inplace=True)
        self.assertTrue(self.graph.is_triangulated())
        self.assertListEqual(hf.recursive_sorted(self.graph.edges()),
                             [['a', 'b'], ['a', 'd'], ['b', 'c'],
                              ['b', 'd'], ['c', 'd']])

    def test_cardinality_mismatch_raises_error(self):
        self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
                                   ('d', 'a')])
        factor_list = [Factor(edge, [2, 2], np.random.rand(4)) for edge in
#.........这里部分代码省略.........

class TestUndirectedGraphFactorOperations(unittest.TestCase):
    def setUp(self):
        self.graph = MarkovModel()

    def test_add_factor_raises_error(self):
        self.graph.add_edges_from([('Alice', 'Bob'), ('Bob', 'Charles'),
                                   ('Charles', 'Debbie'), ('Debbie', 'Alice')])
        factor = Factor(['Alice', 'Bob', 'John'], [2, 2, 2], np.random.rand(8))
        self.assertRaises(ValueError, self.graph.add_factors, factor)

    def test_add_single_factor(self):
        self.graph.add_nodes_from(['a', 'b', 'c'])
        phi = Factor(['a', 'b'], [2, 2], range(4))
        self.graph.add_factors(phi)
        six.assertCountEqual(self, self.graph.factors, [phi])

    def test_add_multiple_factors(self):
        self.graph.add_nodes_from(['a', 'b', 'c'])
        phi1 = Factor(['a', 'b'], [2, 2], range(4))
        phi2 = Factor(['b', 'c'], [2, 2], range(4))
        self.graph.add_factors(phi1, phi2)
        six.assertCountEqual(self, self.graph.factors, [phi1, phi2])

    def test_get_factors(self):
        self.graph.add_nodes_from(['a', 'b', 'c'])
        phi1 = Factor(['a', 'b'], [2, 2], range(4))
        phi2 = Factor(['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 test_remove_single_factor(self):
        self.graph.add_nodes_from(['a', 'b', 'c'])
        phi1 = Factor(['a', 'b'], [2, 2], range(4))
        phi2 = Factor(['b', 'c'], [2, 2], range(4))
        self.graph.add_factors(phi1, phi2)
        self.graph.remove_factors(phi1)
        six.assertCountEqual(self, self.graph.factors, [phi2])

    def test_remove_multiple_factors(self):
        self.graph.add_nodes_from(['a', 'b', 'c'])
        phi1 = Factor(['a', 'b'], [2, 2], range(4))
        phi2 = Factor(['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 = Factor(['a', 'b'], [2, 2], range(4))
        phi2 = Factor(['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_partition_function_raises_error(self):
        self.graph.add_nodes_from(['a', 'b', 'c', 'd'])
        phi1 = Factor(['a', 'b'], [2, 2], range(4))
        phi2 = Factor(['b', 'c'], [2, 2], range(4))
        self.graph.add_factors(phi1, phi2)
        self.assertRaises(ValueError,
                          self.graph.get_partition_function)

    def tearDown(self):
        del self.graph

 def test_class_init_with_data_nonstring(self):
     self.g = MarkovModel([(1, 2), (2, 3)])

 def test_class_init_with_data_string(self):
     self.g = MarkovModel([('a', 'b'), ('b', 'c')])
     self.assertListEqual(sorted(self.g.nodes()), ['a', 'b', 'c'])
     self.assertListEqual(hf.recursive_sorted(self.g.edges()),
                          [['a', 'b'], ['b', 'c']])

class TestInferenceBase(unittest.TestCase):

    def setUp(self):
        self.bayesian = BayesianModel([('a', 'b'), ('b', 'c'), ('c', 'd'), ('d', 'e')])
        a_cpd = TabularCPD('a', 2, [[0.4, 0.6]])
        b_cpd = TabularCPD('b', 2, [[0.2, 0.4], [0.8, 0.6]], evidence=['a'],
                           evidence_card=[2])
        c_cpd = TabularCPD('c', 2, [[0.1, 0.2], [0.9, 0.8]], evidence=['b'],
                           evidence_card=[2])
        d_cpd = TabularCPD('d', 2, [[0.4, 0.3], [0.6, 0.7]], evidence=['c'],
                           evidence_card=[2])
        e_cpd = TabularCPD('e', 2, [[0.3, 0.2], [0.7, 0.8]], evidence=['d'],
                           evidence_card=[2])
        self.bayesian.add_cpds(a_cpd, b_cpd, c_cpd, d_cpd, e_cpd)

        self.markov = MarkovModel([('a', 'b'), ('b', 'd'), ('a', 'c'), ('c', 'd')])
        factor_1 = DiscreteFactor(['a', 'b'], [2, 2], np.array([100, 1, 1, 100]))
        factor_2 = DiscreteFactor(['a', 'c'], [2, 2], np.array([40, 30, 100, 20]))
        factor_3 = DiscreteFactor(['b', 'd'], [2, 2], np.array([1, 100, 100, 1]))
        factor_4 = DiscreteFactor(['c', 'd'], [2, 2], np.array([60, 60, 40, 40]))
        self.markov.add_factors(factor_1, factor_2, factor_3, factor_4)

    def test_bayesian_inference_init(self):
        infer_bayesian = Inference(self.bayesian)
        self.assertEqual(set(infer_bayesian.variables), {'a', 'b', 'c', 'd', 'e'})
        self.assertEqual(infer_bayesian.cardinality, {'a': 2, 'b': 2, 'c': 2,
                                                      'd': 2, 'e': 2})
        self.assertIsInstance(infer_bayesian.factors, defaultdict)
        self.assertEqual(set(infer_bayesian.factors['a']),
                         set([self.bayesian.get_cpds('a').to_factor(),
                              self.bayesian.get_cpds('b').to_factor()]))
        self.assertEqual(set(infer_bayesian.factors['b']),
                         set([self.bayesian.get_cpds('b').to_factor(),
                              self.bayesian.get_cpds('c').to_factor()]))
        self.assertEqual(set(infer_bayesian.factors['c']),
                         set([self.bayesian.get_cpds('c').to_factor(),
                              self.bayesian.get_cpds('d').to_factor()]))
        self.assertEqual(set(infer_bayesian.factors['d']),
                         set([self.bayesian.get_cpds('d').to_factor(),
                              self.bayesian.get_cpds('e').to_factor()]))
        self.assertEqual(set(infer_bayesian.factors['e']),
                         set([self.bayesian.get_cpds('e').to_factor()]))

    def test_markov_inference_init(self):
        infer_markov = Inference(self.markov)
        self.assertEqual(set(infer_markov.variables), {'a', 'b', 'c', 'd'})
        self.assertEqual(infer_markov.cardinality, {'a': 2, 'b': 2, 'c': 2, 'd': 2})
        self.assertEqual(infer_markov.factors, {'a': [DiscreteFactor(['a', 'b'], [2, 2],
                                                                     np.array([100, 1, 1, 100])),
                                                      DiscreteFactor(['a', 'c'], [2, 2],
                                                                     np.array([40, 30, 100, 20]))],
                                                'b': [DiscreteFactor(['a', 'b'], [2, 2],
                                                                     np.array([100, 1, 1, 100])),
                                                      DiscreteFactor(['b', 'd'], [2, 2],
                                                                     np.array([1, 100, 100, 1]))],
                                                'c': [DiscreteFactor(['a', 'c'], [2, 2],
                                                                     np.array([40, 30, 100, 20])),
                                                      DiscreteFactor(['c', 'd'], [2, 2],
                                                                     np.array([60, 60, 40, 40]))],
                                                'd': [DiscreteFactor(['b', 'd'], [2, 2],
                                                                     np.array([1, 100, 100, 1])),
                                                      DiscreteFactor(['c', 'd'], [2, 2],
                                                                     np.array([60, 60, 40, 40]))]})

class TestGibbsSampling(unittest.TestCase):
    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 = Factor(['A', 'B'], [2, 3], [1, 2, 3, 4, 5, 6])
        factor_cb = Factor(['C', 'B'], [4, 3], [3, 1, 4, 5, 7, 8, 1, 3, 10, 4, 5, 6])
        factor_bd = Factor(['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 tearDown(self):
        del self.bayesian_model
        del self.markov_model

    @patch('pgmpy.inference.Sampling.GibbsSampling._get_kernel_from_bayesian_model', autospec=True)
    @patch('pgmpy.models.MarkovChain.__init__', autospec=True)
    def test_init_bayesian_model(self, init, get_kernel):
        model = MagicMock(spec_set=BayesianModel)
        gibbs = GibbsSampling(model)
        init.assert_called_once_with(gibbs)
        get_kernel.assert_called_once_with(gibbs, model)

    @patch('pgmpy.inference.Sampling.GibbsSampling._get_kernel_from_markov_model', autospec=True)
    def test_init_markov_model(self, get_kernel):
        model = MagicMock(spec_set=MarkovModel)
        gibbs = GibbsSampling(model)
        get_kernel.assert_called_once_with(gibbs, model)

    def test_get_kernel_from_bayesian_model(self):
        gibbs = GibbsSampling()
        gibbs._get_kernel_from_bayesian_model(self.bayesian_model)
        self.assertListEqual(list(gibbs.variables), self.bayesian_model.nodes())
        self.assertDictEqual(gibbs.cardinalities, {'diff': 2, 'intel': 2, 'grade': 3})

    def test_get_kernel_from_markov_model(self):
        gibbs = GibbsSampling()
        gibbs._get_kernel_from_markov_model(self.markov_model)
        self.assertListEqual(list(gibbs.variables), self.markov_model.nodes())
        self.assertDictEqual(gibbs.cardinalities, {'A': 2, 'B': 3, 'C': 4, 'D': 2})

    def test_sample(self):
        start_state = [State('diff', 0), State('intel', 0), State('grade', 0)]
        sample = self.gibbs.sample(start_state, 2)
        self.assertEquals(len(sample), 2)
        self.assertEquals(len(sample.columns), 3)
        self.assertIn('diff', sample.columns)
        self.assertIn('intel', sample.columns)
        self.assertIn('grade', sample.columns)
        self.assertTrue(set(sample['diff']).issubset({0, 1}))
        self.assertTrue(set(sample['intel']).issubset({0, 1}))
        self.assertTrue(set(sample['grade']).issubset({0, 1, 2}))


    @patch("pgmpy.inference.Sampling.GibbsSampling.random_state", autospec=True)
    def test_sample_less_arg(self, random_state):
        self.gibbs.state = None
        random_state.return_value = [State('diff', 0), State('intel', 0), State('grade', 0)]
        sample = self.gibbs.sample(size=2)
        random_state.assert_called_once_with(self.gibbs)
        self.assertEqual(len(sample), 2)


    def test_generate_sample(self):
        start_state = [State('diff', 0), State('intel', 0), State('grade', 0)]
        gen = self.gibbs.generate_sample(start_state, 2)
        samples = [sample for sample in gen]
        self.assertEqual(len(samples), 2)
        self.assertEqual({samples[0][0].var, samples[0][1].var, samples[0][2].var}, {'diff', 'intel', 'grade'})
        self.assertEqual({samples[1][0].var, samples[1][1].var, samples[1][2].var}, {'diff', 'intel', 'grade'})


    @patch("pgmpy.inference.Sampling.GibbsSampling.random_state", autospec=True)
    def test_generate_sample_less_arg(self, random_state):
        self.gibbs.state = None
        gen = self.gibbs.generate_sample(size=2)
        samples = [sample for sample in gen]
        random_state.assert_called_once_with(self.gibbs)
        self.assertEqual(len(samples), 2)