Python MarkovModel.edges方法代码示例

# 需要导入模块: from pgmpy.models import MarkovModel [as 别名]
# 或者: from pgmpy.models.MarkovModel import edges [as 别名]
class generate(object):
    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 get_model(self):
        return self.G

    def _complete_graph(self, adj_mat):
        """
        generate the complete graph over len(adj_mat)
        """

        self._nodes_only(adj_mat)
        for i in range(self.n_nodes):
            self.G.add_edges_from([(i, j)
                                   for j in range(self.n_nodes)])

    def _import_adj(self, adj_mat):
        """
        add nodes and edges to graph
        adj_mat - square matrix, numpy array like
        """
        DEBUG = False
        assert (adj_mat is not None), "can't import empty adj mat"
        # add nodes
        self._nodes_only(adj_mat)
        # add edges
        for i in range(self.n_nodes):
            edges_list = ([(i, j)
                           for j in range(self.n_nodes)
                           if adj_mat[i][j]])
            if DEBUG: print edges_list
            self.G.add_edges_from(edges_list)
            if DEBUG: print len(self.G)

    def _nodes_only(self, adj_mat):
        """
        add nodes to graph
        adj_mat - aquare matrix, numpy array like
        """
        global DEBUG
        assert (adj_mat is not None), "can't import empty adj mat"
        assert (self.n_nodes == adj_mat.shape[1]), "adj_mat is not sqaure"
        self.G.add_nodes_from([i for i in range(self.n_nodes)])
        if DEBUG: print '_nodes_only', [i for i in range(self.n_nodes)]
        if DEBUG: print '_nodes_only print G', self.G.nodes()
        assert (self.n_nodes == len(self.G)), "graph size is incosistent with adj_mat"

    def _ising_factors(self, Wf=1, Wi=1, f_type='mixed'):
        """
        Add ising-like factors to model graph
        cardinality is the number of possible values
        in our case we have boolean nodes, thus cardinality = 2
        Wf   =  \theta_i     = ~U[-Wf, Wf] 
        type =  'mixed'      = ~U[-Wi,Wi]
                'attractive' = ~U[0,Wi]
        """
        self._field_factors(Wf)
        self._interact_factors(Wi, f_type)

    def _field_factors(self, w, states=2):
        """
            this function assigns factor for single node
            currently states=2 for ising model generation
        """

        for i in self.G.nodes():
            phi_i = Factor([i], [states], self._wf(w, states))
            self.G.add_factors(phi_i)

    def _interact_factors(self, w, f_type, states=2):
        """
            this function assigns factor for two interacting nodes
            currently states=2 for ising model generation
        """

        for e in self.G.edges():
            # if DEBUG: print 'interact_factors edges,states, values',e,[e[0],
            #  e[1]],len(e)*[states], self._wi(w, f_type, states)
            phi_ij = Factor([e[0], e[1]], [states] * len(e), self._wi(w, f_type, states))
            self.G.add_factors(phi_ij)

    def _wf(self, w, k):
        """
            generate field factor
        """
        # if DEBUG: print 'w',type(w),w
        return np.random.uniform(low=-1 * w, high=w, size=k)

#.........这里部分代码省略.........

# 需要导入模块: from pgmpy.models import MarkovModel [as 别名]
# 或者: from pgmpy.models.MarkovModel import edges [as 别名]
import numpy as np
import pandas as pd
from pgmpy.models import MarkovModel
from pgmpy.estimators import MaximumLikelihoodEstimator
# Generating random data
raw_data = np.random.randint(low=0, high=2, size=(1000, 2))
data = pd.DataFrame(raw_data, columns=['X', 'Y'])
model = MarkovModel()
model.fit(data, estimator=MaximumLikelihoodEstimator)
model.get_factors()
model.nodes()
model.edges()

# 需要导入模块: from pgmpy.models import MarkovModel [as 别名]
# 或者: from pgmpy.models.MarkovModel import edges [as 别名]
class TestMarkovModelMethods(unittest.TestCase):
    def setUp(self):
        self.graph = MarkovModel()

    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 = Factor(['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 = Factor(['a', 'b'], [2, 2], np.random.rand(4))
        phi2 = Factor(['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 = Factor(['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_get_cardinality_check_cardinality(self):

        self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
                                   ('d', 'a')])

        phi1 = Factor(['a', 'b'], [1, 2], np.random.rand(2))
        self.graph.add_factors(phi1)
        self.assertRaises(ValueError, self.graph.get_cardinality, check_cardinality=True)

        phi2 = Factor(['a', 'c'], [1, 2], np.random.rand(2))
        self.graph.add_factors(phi2)
        self.assertRaises(ValueError, self.graph.get_cardinality, check_cardinality=True)

        phi3 = Factor(['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_check_model(self):

        self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
                                   ('d', 'a')])
        phi1 = Factor(['a', 'b'], [1, 2], np.random.rand(2))
        phi2 = Factor(['c', 'b'], [3, 2], np.random.rand(6))
        phi3 = Factor(['c', 'd'], [3, 4], np.random.rand(12))
        phi4 = Factor(['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 = Factor(['a', 'b'], [4, 2], np.random.rand(8))
        self.graph.add_factors(phi1)
        self.assertTrue(self.graph.check_model())

    def test_check_model1(self):
    
        self.graph.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'd'),
                                   ('d', 'a')])

        phi1 = Factor(['a', 'b'], [1, 2], np.random.rand(2))

        phi2 = Factor(['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 = Factor(['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 = Factor(['b', 'c'], [2, 3], np.random.rand(6))
        phi3 = Factor(['c', 'd'], [3, 4], np.random.rand(12))
        phi4 = Factor(['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 = Factor(['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'), ('b', 'c'), ('c', 'd'),
                                   ('d', 'a')])

        phi1 = Factor(['a', 'c'], [1, 2], np.random.rand(2))
#.........这里部分代码省略.........

# 需要导入模块: from pgmpy.models import MarkovModel [as 别名]
# 或者: from pgmpy.models.MarkovModel import edges [as 别名]
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.........

# 需要导入模块: from pgmpy.models import MarkovModel [as 别名]
# 或者: from pgmpy.models.MarkovModel import edges [as 别名]
class TestMarkovModelCreation(unittest.TestCase):
    def setUp(self):
        self.graph = MarkovModel()

    def test_class_init_without_data(self):
        self.assertIsInstance(self.graph, MarkovModel)

    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']])

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

    def test_add_node_string(self):
        self.graph.add_node('a')
        self.assertListEqual(self.graph.nodes(), ['a'])

    def test_add_node_nonstring(self):
        self.graph.add_node(1)

    def test_add_nodes_from_string(self):
        self.graph.add_nodes_from(['a', 'b', 'c', 'd'])
        self.assertListEqual(sorted(self.graph.nodes()), ['a', 'b', 'c', 'd'])

    def test_add_nodes_from_non_string(self):
        self.graph.add_nodes_from([1, 2, 3, 4])

    def test_add_edge_string(self):
        self.graph.add_edge('d', 'e')
        self.assertListEqual(sorted(self.graph.nodes()), ['d', 'e'])
        self.assertListEqual(hf.recursive_sorted(self.graph.edges()),
                             [['d', 'e']])
        self.graph.add_nodes_from(['a', 'b', 'c'])
        self.graph.add_edge('a', 'b')
        self.assertListEqual(hf.recursive_sorted(self.graph.edges()),
                             [['a', 'b'], ['d', 'e']])

    def test_add_edge_nonstring(self):
        self.graph.add_edge(1, 2)

    def test_add_edge_selfloop(self):
        self.assertRaises(ValueError, self.graph.add_edge, 'a', 'a')

    def test_add_edges_from_string(self):
        self.graph.add_edges_from([('a', 'b'), ('b', 'c')])
        self.assertListEqual(sorted(self.graph.nodes()), ['a', 'b', 'c'])
        self.assertListEqual(hf.recursive_sorted(self.graph.edges()),
                             [['a', 'b'], ['b', 'c']])
        self.graph.add_nodes_from(['d', 'e', 'f'])
        self.graph.add_edges_from([('d', 'e'), ('e', 'f')])
        self.assertListEqual(sorted(self.graph.nodes()),
                             ['a', 'b', 'c', 'd', 'e', 'f'])
        self.assertListEqual(hf.recursive_sorted(self.graph.edges()),
                             hf.recursive_sorted([('a', 'b'), ('b', 'c'),
                                                  ('d', 'e'), ('e', 'f')]))

    def test_add_edges_from_nonstring(self):
        self.graph.add_edges_from([(1, 2), (2, 3)])

    def test_add_edges_from_self_loop(self):
        self.assertRaises(ValueError, self.graph.add_edges_from,
                          [('a', 'a')])

    def test_number_of_neighbors(self):
        self.graph.add_edges_from([('a', 'b'), ('b', 'c')])
        self.assertEqual(len(self.graph.neighbors('b')), 2)

    def tearDown(self):
        del self.graph

# 需要导入模块: from pgmpy.models import MarkovModel [as 别名]
# 或者: from pgmpy.models.MarkovModel import edges [as 别名]
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.is_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 = 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)
        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 = 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)
        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 = 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)
        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 = 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)
        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 = 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)
        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 = 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)
        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 = [DiscreteFactor(edge, [2, 2], np.random.rand(4)) for edge in
#.........这里部分代码省略.........