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Python MarkovModel.is_clique方法代码示例

本文整理汇总了Python中pgmpy.models.MarkovModel.is_clique方法的典型用法代码示例。如果您正苦于以下问题:Python MarkovModel.is_clique方法的具体用法?Python MarkovModel.is_clique怎么用?Python MarkovModel.is_clique使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在pgmpy.models.MarkovModel的用法示例。


在下文中一共展示了MarkovModel.is_clique方法的1个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: TestUndirectedGraphTriangulation

# 需要导入模块: from pgmpy.models import MarkovModel [as 别名]
# 或者: from pgmpy.models.MarkovModel import is_clique [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.........
开发者ID:MariosRichards,项目名称:BES_analysis_code,代码行数:103,代码来源:test_MarkovModel.py


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