class TestFactorMethods(unittest.TestCase): def setUp(self): self.phi = Factor(['x1', 'x2', 'x3'], [2, 2, 2], np.random.uniform(5, 10, size=8)) self.phi1 = Factor(['x1', 'x2', 'x3'], [2, 3, 2], range(12)) # def test_pos_dist(self): # self.assertTrue(self.phi.is_pos_dist()) # self.assertFalse(self.phi1.is_pos_dist()) def test_scope(self): self.assertListEqual(self.phi.scope(), ['x1', 'x2', 'x3']) self.assertListEqual(self.phi1.scope(), ['x1', 'x2', 'x3']) def test_assignment(self): self.assertListEqual(self.phi.assignment([0]), [['x1_0', 'x2_0', 'x3_0']]) self.assertListEqual(self.phi.assignment([4, 5, 6]), [['x1_1', 'x2_0', 'x3_0'], ['x1_1', 'x2_0', 'x3_1'], ['x1_1', 'x2_1', 'x3_0']]) self.assertListEqual(self.phi1.assignment(np.array([4, 5, 6])), [['x1_0', 'x2_2', 'x3_0'], ['x1_0', 'x2_2', 'x3_1'], ['x1_1', 'x2_0', 'x3_0']]) def test_assignment_indexerror(self): self.assertRaises(IndexError, self.phi.assignment, [10]) self.assertRaises(IndexError, self.phi.assignment, [1, 3, 10, 5]) self.assertRaises(IndexError, self.phi.assignment, np.array([1, 3, 10, 5])) # def test_get_variables(self): # self.assertListEqual(sorted(self.phi.get_variables()), ['x1', 'x2', 'x3']) # self.assertListEqual(sorted(self.phi1.get_variables()), ['x1', 'x2', 'x3']) # def test_get_value(self): # self.assertAlmostEqual(self.phi1.get_value({'x1': 1, 'x2': 2, 'x3': 0, 'x4': 2, 'x5': 1}), 5.0) # self.assertAlmostEqual(self.phi1.get_value([1, 1, 1]), 9.0) def test_get_cardinality(self): self.assertEqual(self.phi.get_cardinality('x1'), 2) self.assertEqual(self.phi.get_cardinality('x2'), 2) self.assertEqual(self.phi.get_cardinality('x3'), 2) def test_get_cardinality_scopeerror(self): self.assertRaises(exceptions.ScopeError, self.phi.get_cardinality, 'x4') def test_marginalize(self): self.phi1.marginalize('x1') np_test.assert_array_equal(self.phi1.values, np.array([6, 8, 10, 12, 14, 16])) self.phi1.marginalize(['x2']) np_test.assert_array_equal(self.phi1.values, np.array([30, 36])) self.phi1.marginalize('x3') np_test.assert_array_equal(self.phi1.values, np.array([66])) def test_marginalize_scopeerror(self): self.assertRaises(exceptions.ScopeError, self.phi.marginalize, 'x4') self.assertRaises(exceptions.ScopeError, self.phi.marginalize, ['x4']) self.phi.marginalize('x1') self.assertRaises(exceptions.ScopeError, self.phi.marginalize, 'x1') def test_normalize(self): self.phi1.normalize() np_test.assert_almost_equal(self.phi1.values, np.array( [0, 0.01515152, 0.03030303, 0.04545455, 0.06060606, 0.07575758, 0.09090909, 0.10606061, 0.12121212, 0.13636364, 0.15151515, 0.16666667])) # def test_maximize(self): # self.phi1.maximize(['x1']) # max_val_indices = {'x1':1} # data = [max_val_indices] * 6 # #print(self.phi1) # np_test.assert_array_equal(self.phi1.values, np.array([6,7,8,9,10,11])) # self.assertEqual(self.phi1.data, sorted(data, key=lambda t: sorted(t.keys()))) def test_reduce(self): self.phi1.reduce(['x1_0', 'x2_0']) np_test.assert_array_equal(self.phi1.values, np.array([0, 1])) def test_reduce_typeerror(self): self.assertRaises(TypeError, self.phi1.reduce, 'x10') self.assertRaises(TypeError, self.phi1.reduce, ['x10']) def test_reduce_scopeerror(self): self.assertRaises(exceptions.ScopeError, self.phi1.reduce, 'x4_1') def test_reduce_sizeerror(self): self.assertRaises(exceptions.SizeError, self.phi1.reduce, 'x3_5') def test_identity_factor(self): identity_factor = self.phi.identity_factor() self.assertEquals(list(identity_factor.variables), ['x1', 'x2', 'x3']) np_test.assert_array_equal(identity_factor.cardinality, [2, 2, 2]) np_test.assert_array_equal(identity_factor.values, np.ones(8)) def test__str(self): from pgmpy import factors phi = factors.Factor(['x1'], [2], np.ones(2)) self.assertEqual(repr(phi.__str__()), "'x1\\t\\tphi(x1)\\n------------------------\\nx1_0\\t\\t1.0\\nx1_1\\t\\t1.0\\n'") def test_factor_product(self): from pgmpy import factors phi = factors.Factor(['x1', 'x2'], [2, 2], range(4)) phi1 = factors.Factor(['x3', 'x4'], [2, 2], range(4)) factor_product = factors.factor_product(phi, phi1) np_test.assert_array_equal(factor_product.values, np.array([0, 0, 0, 0, 0, 1, 2, 3, 0, 2, 4, 6, 0, 3, 6, 9])) self.assertEqual(factor_product.variables, OrderedDict([ ('x1', ['x1_0', 'x1_1']), ('x2', ['x2_0', 'x2_1']), ('x3', ['x3_0', 'x3_1']), ('x4', ['x4_0', 'x4_1'])] )) phi = factors.Factor(['x1', 'x2'], [3, 2], range(6)) phi1 = factors.Factor(['x2', 'x3'], [2, 2], range(4)) factor_product = factors.factor_product(phi, phi1) np_test.assert_array_equal(factor_product.values, np.array([0, 0, 2, 3, 0, 2, 6, 9, 0, 4, 10, 15])) self.assertEqual(factor_product.variables, OrderedDict( [('x1', ['x1_0', 'x1_1', 'x1_2']), ('x2', ['x2_0', 'x2_1']), ('x3', ['x3_0', 'x3_1'])])) def test_factor_product2(self): from pgmpy import factors phi = factors.Factor(['x1', 'x2'], [2, 2], range(4)) phi1 = factors.Factor(['x3', 'x4'], [2, 2], range(4)) factor_product = phi.product(phi1) np_test.assert_array_equal(factor_product.values, np.array([0, 0, 0, 0, 0, 1, 2, 3, 0, 2, 4, 6, 0, 3, 6, 9])) self.assertEqual(factor_product.variables, OrderedDict([ ('x1', ['x1_0', 'x1_1']), ('x2', ['x2_0', 'x2_1']), ('x3', ['x3_0', 'x3_1']), ('x4', ['x4_0', 'x4_1'])] )) phi = factors.Factor(['x1', 'x2'], [3, 2], range(6)) phi1 = factors.Factor(['x2', 'x3'], [2, 2], range(4)) factor_product = phi.product(phi1) np_test.assert_array_equal(factor_product.values, np.array([0, 0, 2, 3, 0, 2, 6, 9, 0, 4, 10, 15])) self.assertEqual(factor_product.variables, OrderedDict( [('x1', ['x1_0', 'x1_1', 'x1_2']), ('x2', ['x2_0', 'x2_1']), ('x3', ['x3_0', 'x3_1'])])) def test_factor_product_non_factor_arg(self): from pgmpy import factors self.assertRaises(TypeError, factors.factor_product, 1, 2) def test_factor_mul(self): from pgmpy import factors phi = factors.Factor(['x1', 'x2'], [2, 2], range(4)) phi1 = factors.Factor(['x3', 'x4'], [2, 2], range(4)) factor_product = phi * phi1 np_test.assert_array_equal(factor_product.values, np.array([0, 0, 0, 0, 0, 1, 2, 3, 0, 2, 4, 6, 0, 3, 6, 9])) self.assertEqual(factor_product.variables, OrderedDict([ ('x1', ['x1_0', 'x1_1']), ('x2', ['x2_0', 'x2_1']), ('x3', ['x3_0', 'x3_1']), ('x4', ['x4_0', 'x4_1'])] )) def test_factor_divide(self): from pgmpy import factors phi1 = factors.Factor(['x1', 'x2'], [2, 2], [1, 2, 2, 4]) phi2 = factors.Factor(['x1'], [2], [1, 2]) factor_divide = phi1.divide(phi2) phi3 = factors.Factor(['x1', 'x2'], [2, 2], [1, 2, 1, 2]) self.assertEqual(phi3, factor_divide) def test_factor_divide_invalid(self): from pgmpy import factors phi1 = factors.Factor(['x1', 'x2', ], [2, 2], [1, 2, 3, 4]) phi2 = factors.Factor(['x1'], [2], [0, 2]) factor_divide = phi1.divide(phi2) np_test.assert_array_equal(factor_divide.values, np.array([0, 0, 1.5, 2])) def test_factor_divide_no_common_scope(self): from pgmpy import factors phi1 = factors.Factor(['x1', 'x2', ], [2, 2], [1, 2, 3, 4]) phi2 = factors.Factor(['x3'], [2], [0, 2]) self.assertRaises(ValueError, factors.factor_divide, phi1, phi2) def test_factor_divide_non_factor_arg(self): from pgmpy import factors self.assertRaises(TypeError, factors.factor_divide, 1, 1) # def test_sum_values(self): # self.assertEqual(self.phi1.sum_values(), 66) def test_eq(self): self.assertFalse(self.phi == self.phi1) self.assertTrue(self.phi == self.phi) self.assertTrue(self.phi1 == self.phi1) def tearDown(self): del self.phi del self.phi1
class TestFactorMethods(unittest.TestCase): def setUp(self): self.phi = Factor(['x1', 'x2', 'x3'], [2, 2, 2], np.random.uniform(5, 10, size=8)) self.phi1 = Factor(['x1', 'x2', 'x3'], [2, 3, 2], range(12)) def test_scope(self): self.assertListEqual(self.phi.scope(), ['x1', 'x2', 'x3']) self.assertListEqual(self.phi1.scope(), ['x1', 'x2', 'x3']) def test_assignment(self): self.assertListEqual( self.phi.assignment([0]), [[State('x1', 0), State('x2', 0), State('x3', 0)]]) self.assertListEqual( self.phi.assignment([4, 5, 6]), [[State('x1', 1), State( 'x2', 0), State('x3', 0)], [State('x1', 1), State('x2', 0), State('x3', 1)], [State('x1', 1), State('x2', 1), State('x3', 0)]]) self.assertListEqual( self.phi1.assignment(np.array([4, 5, 6])), [[State('x1', 0), State( 'x2', 2), State('x3', 0)], [State('x1', 0), State('x2', 2), State('x3', 1)], [State('x1', 1), State('x2', 0), State('x3', 0)]]) def test_assignment_indexerror(self): self.assertRaises(IndexError, self.phi.assignment, [10]) self.assertRaises(IndexError, self.phi.assignment, [1, 3, 10, 5]) self.assertRaises(IndexError, self.phi.assignment, np.array([1, 3, 10, 5])) def test_get_cardinality(self): self.assertEqual(self.phi.get_cardinality('x1'), 2) self.assertEqual(self.phi.get_cardinality('x2'), 2) self.assertEqual(self.phi.get_cardinality('x3'), 2) def test_get_cardinality_scopeerror(self): self.assertRaises(exceptions.ScopeError, self.phi.get_cardinality, 'x4') def test_marginalize(self): self.phi1.marginalize('x1') np_test.assert_array_equal(self.phi1.values, np.array([6, 8, 10, 12, 14, 16])) self.phi1.marginalize(['x2']) np_test.assert_array_equal(self.phi1.values, np.array([30, 36])) self.phi1.marginalize('x3') np_test.assert_array_equal(self.phi1.values, np.array([66])) def test_marginalize_scopeerror(self): self.assertRaises(exceptions.ScopeError, self.phi.marginalize, 'x4') self.assertRaises(exceptions.ScopeError, self.phi.marginalize, ['x4']) self.phi.marginalize('x1') self.assertRaises(exceptions.ScopeError, self.phi.marginalize, 'x1') def test_normalize(self): self.phi1.normalize() np_test.assert_almost_equal( self.phi1.values, np.array([ 0, 0.01515152, 0.03030303, 0.04545455, 0.06060606, 0.07575758, 0.09090909, 0.10606061, 0.12121212, 0.13636364, 0.15151515, 0.16666667 ])) def test_reduce(self): self.phi1.reduce([('x1', 0), ('x2', 0)]) np_test.assert_array_equal(self.phi1.values, np.array([0, 1])) def test_reduce1(self): self.phi1.reduce([('x2', 0), ('x1', 0)]) np_test.assert_array_equal(self.phi1.values, np.array([0, 1])) @unittest.skip def test_complete_reduce(self): self.phi1.reduce([('x1', 0), ('x2', 0), ('x3', 1)]) np_test.assert_array_equal(self.phi1.values, np.array([0])) np_test.assert_array_equal(self.phi1.cardinality, np.array([])) np_test.assert_array_equal(self.phi1.variables, OrderedDict()) def test_reduce_typeerror(self): self.assertRaises(ValueError, self.phi1.reduce, 'x10') self.assertRaises(ValueError, self.phi1.reduce, ['x10']) def test_reduce_scopeerror(self): self.assertRaises(ValueError, self.phi1.reduce, ('x4', 1)) def test_reduce_sizeerror(self): self.assertRaises(IndexError, self.phi1.reduce, ('x3', 5)) def test_identity_factor(self): identity_factor = self.phi.identity_factor() self.assertEquals(list(identity_factor.variables), ['x1', 'x2', 'x3']) np_test.assert_array_equal(identity_factor.cardinality, [2, 2, 2]) np_test.assert_array_equal(identity_factor.values, np.ones(8)) def test_factor_product(self): phi = Factor(['x1', 'x2'], [2, 2], range(4)) phi1 = Factor(['x3', 'x4'], [2, 2], range(4)) prod = factor_product(phi, phi1) np_test.assert_array_equal( prod.values, np.array([0, 0, 0, 0, 0, 1, 2, 3, 0, 2, 4, 6, 0, 3, 6, 9])) self.assertEqual( prod.variables, OrderedDict([('x1', [State('x1', 0), State('x1', 1)]), ('x2', [State('x2', 0), State('x2', 1)]), ('x3', [State('x3', 0), State('x3', 1)]), ('x4', [State('x4', 0), State('x4', 1)])])) phi = Factor(['x1', 'x2'], [3, 2], range(6)) phi1 = Factor(['x2', 'x3'], [2, 2], range(4)) prod = factor_product(phi, phi1) np_test.assert_array_equal( prod.values, np.array([0, 0, 2, 3, 0, 2, 6, 9, 0, 4, 10, 15])) self.assertEqual( prod.variables, OrderedDict([('x1', [State('x1', 0), State('x1', 1), State('x1', 2)]), ('x2', [State('x2', 0), State('x2', 1)]), ('x3', [State('x3', 0), State('x3', 1)])])) def test_factor_product2(self): from pgmpy import factors phi = factors.Factor(['x1', 'x2'], [2, 2], range(4)) phi1 = factors.Factor(['x3', 'x4'], [2, 2], range(4)) prod = phi.product(phi1) np_test.assert_array_equal( prod.values, np.array([0, 0, 0, 0, 0, 1, 2, 3, 0, 2, 4, 6, 0, 3, 6, 9])) self.assertEqual( prod.variables, OrderedDict([('x1', [State('x1', 0), State('x1', 1)]), ('x2', [State('x2', 0), State('x2', 1)]), ('x3', [State('x3', 0), State('x3', 1)]), ('x4', [State('x4', 0), State('x4', 1)])])) phi = Factor(['x1', 'x2'], [3, 2], range(6)) phi1 = Factor(['x2', 'x3'], [2, 2], range(4)) prod = phi.product(phi1) np_test.assert_array_equal( prod.values, np.array([0, 0, 2, 3, 0, 2, 6, 9, 0, 4, 10, 15])) self.assertEqual( prod.variables, OrderedDict([('x1', [State('x1', 0), State('x1', 1), State('x1', 2)]), ('x2', [State('x2', 0), State('x2', 1)]), ('x3', [State('x3', 0), State('x3', 1)])])) def test_factor_product_non_factor_arg(self): self.assertRaises(TypeError, factor_product, 1, 2) def test_factor_mul(self): phi = Factor(['x1', 'x2'], [2, 2], range(4)) phi1 = Factor(['x3', 'x4'], [2, 2], range(4)) prod = phi * phi1 np_test.assert_array_equal( prod.values, np.array([0, 0, 0, 0, 0, 1, 2, 3, 0, 2, 4, 6, 0, 3, 6, 9])) self.assertEqual( prod.variables, OrderedDict([('x1', [State('x1', 0), State('x1', 1)]), ('x2', [State('x2', 0), State('x2', 1)]), ('x3', [State('x3', 0), State('x3', 1)]), ('x4', [State('x4', 0), State('x4', 1)])])) def test_factor_divide(self): phi1 = Factor(['x1', 'x2'], [2, 2], [1, 2, 2, 4]) phi2 = Factor(['x1'], [2], [1, 2]) div = phi1.divide(phi2) phi3 = Factor(['x1', 'x2'], [2, 2], [1, 2, 1, 2]) self.assertEqual(phi3, div) def test_factor_divide_truediv(self): phi1 = Factor(['x1', 'x2'], [2, 2], [1, 2, 2, 4]) phi2 = Factor(['x1'], [2], [1, 2]) div = phi1 / phi2 phi3 = Factor(['x1', 'x2'], [2, 2], [1, 2, 1, 2]) self.assertEqual(phi3, div) # def test_factor_divide_dividebyzero(self): # phi1 = Factor(['x1', 'x2'], [2, 2], [1, 2, 3, 4]) # phi2 = Factor(['x1'], [2], [0, 2]) # self.assertRaises(FloatingPointError, factor_divide, phi1, phi2) # def test_factor_divide_invalidvalue(self): phi1 = Factor(['x1', 'x2'], [3, 2], [0.5, 0.2, 0, 0, 0.3, 0.45]) phi2 = Factor(['x1'], [3], [0.8, 0, 0.6]) div = phi1.divide(phi2) np_test.assert_array_equal(div.values, np.array([0.625, 0.25, 0, 0, 0.5, 0.75])) def test_factor_divide_no_common_scope(self): phi1 = Factor(['x1', 'x2'], [2, 2], [1, 2, 3, 4]) phi2 = Factor(['x3'], [2], [0, 2]) self.assertRaises(ValueError, factor_divide, phi1, phi2) def test_factor_divide_non_factor_arg(self): self.assertRaises(TypeError, factor_divide, 1, 1) def test_eq(self): self.assertFalse(self.phi == self.phi1) self.assertTrue(self.phi == self.phi) self.assertTrue(self.phi1 == self.phi1) def test_eq1(self): phi1 = Factor(['x1', 'x2', 'x3'], [2, 4, 3], range(24)) phi2 = Factor(['x2', 'x1', 'x3'], [4, 2, 3], [ 0, 1, 2, 12, 13, 14, 3, 4, 5, 15, 16, 17, 6, 7, 8, 18, 19, 20, 9, 10, 11, 21, 22, 23 ]) self.assertTrue(phi1, phi2) def test_index_for_assignment(self): for i, j in enumerate( itertools.product( *[range(2), range(3), range(2)])): self.assertEqual(self.phi1._index_for_assignment(j), i) def test_maximize1(self): self.phi1.maximize('x1') self.assertEqual(self.phi1, Factor(['x2', 'x3'], [3, 2], [6, 7, 8, 9, 10, 11])) self.phi1.maximize('x2') self.assertEqual(self.phi1, Factor(['x3'], [2], [10, 11])) def test_maximize2(self): self.phi1.maximize(['x1', 'x2']) self.assertEqual(self.phi1, Factor(['x3'], [2], [10, 11])) def test_maximize3(self): self.phi2 = Factor(['x1', 'x2', 'x3'], [3, 2, 2], [ 0.25, 0.35, 0.08, 0.16, 0.05, 0.07, 0.00, 0.00, 0.15, 0.21, 0.08, 0.18 ]) self.phi2.maximize('x2') self.assertEqual( self.phi2, Factor(['x1', 'x3'], [3, 2], [0.25, 0.35, 0.05, 0.07, 0.15, 0.21])) def tearDown(self): del self.phi del self.phi1
class TestFactorMethods(unittest.TestCase): def setUp(self): self.phi = Factor(['x1', 'x2', 'x3'], [2, 2, 2], np.random.uniform(5, 10, size=8)) self.phi1 = Factor(['x1', 'x2', 'x3'], [2, 3, 2], range(12)) def test_scope(self): self.assertListEqual(self.phi.scope(), ['x1', 'x2', 'x3']) self.assertListEqual(self.phi1.scope(), ['x1', 'x2', 'x3']) def test_assignment(self): self.assertListEqual(self.phi.assignment([0]), [[State('x1', 0), State('x2', 0), State('x3', 0)]]) self.assertListEqual(self.phi.assignment([4, 5, 6]), [[State('x1', 1), State('x2', 0), State('x3', 0)], [State('x1', 1), State('x2', 0), State('x3', 1)], [State('x1', 1), State('x2', 1), State('x3', 0)]]) self.assertListEqual(self.phi1.assignment(np.array([4, 5, 6])), [[State('x1', 0), State('x2', 2), State('x3', 0)], [State('x1', 0), State('x2', 2), State('x3', 1)], [State('x1', 1), State('x2', 0), State('x3', 0)]]) def test_assignment_indexerror(self): self.assertRaises(IndexError, self.phi.assignment, [10]) self.assertRaises(IndexError, self.phi.assignment, [1, 3, 10, 5]) self.assertRaises(IndexError, self.phi.assignment, np.array([1, 3, 10, 5])) def test_get_cardinality(self): self.assertEqual(self.phi.get_cardinality('x1'), 2) self.assertEqual(self.phi.get_cardinality('x2'), 2) self.assertEqual(self.phi.get_cardinality('x3'), 2) def test_get_cardinality_scopeerror(self): self.assertRaises(exceptions.ScopeError, self.phi.get_cardinality, 'x4') def test_marginalize(self): self.phi1.marginalize('x1') np_test.assert_array_equal(self.phi1.values, np.array([6, 8, 10, 12, 14, 16])) self.phi1.marginalize(['x2']) np_test.assert_array_equal(self.phi1.values, np.array([30, 36])) self.phi1.marginalize('x3') np_test.assert_array_equal(self.phi1.values, np.array([66])) def test_marginalize_scopeerror(self): self.assertRaises(exceptions.ScopeError, self.phi.marginalize, 'x4') self.assertRaises(exceptions.ScopeError, self.phi.marginalize, ['x4']) self.phi.marginalize('x1') self.assertRaises(exceptions.ScopeError, self.phi.marginalize, 'x1') def test_normalize(self): self.phi1.normalize() np_test.assert_almost_equal(self.phi1.values, np.array( [0, 0.01515152, 0.03030303, 0.04545455, 0.06060606, 0.07575758, 0.09090909, 0.10606061, 0.12121212, 0.13636364, 0.15151515, 0.16666667])) def test_reduce(self): self.phi1.reduce([('x1', 0), ('x2', 0)]) np_test.assert_array_equal(self.phi1.values, np.array([0, 1])) def test_reduce1(self): self.phi1.reduce([('x2', 0), ('x1', 0)]) np_test.assert_array_equal(self.phi1.values, np.array([0, 1])) @unittest.skip def test_complete_reduce(self): self.phi1.reduce([('x1', 0), ('x2', 0), ('x3', 1)]) np_test.assert_array_equal(self.phi1.values, np.array([0])) np_test.assert_array_equal(self.phi1.cardinality, np.array([])) np_test.assert_array_equal(self.phi1.variables, OrderedDict()) def test_reduce_typeerror(self): self.assertRaises(ValueError, self.phi1.reduce, 'x10') self.assertRaises(ValueError, self.phi1.reduce, ['x10']) def test_reduce_scopeerror(self): self.assertRaises(ValueError, self.phi1.reduce, ('x4', 1)) def test_reduce_sizeerror(self): self.assertRaises(IndexError, self.phi1.reduce, ('x3', 5)) def test_identity_factor(self): identity_factor = self.phi.identity_factor() self.assertEquals(list(identity_factor.variables), ['x1', 'x2', 'x3']) np_test.assert_array_equal(identity_factor.cardinality, [2, 2, 2]) np_test.assert_array_equal(identity_factor.values, np.ones(8)) def test_factor_product(self): phi = Factor(['x1', 'x2'], [2, 2], range(4)) phi1 = Factor(['x3', 'x4'], [2, 2], range(4)) prod = factor_product(phi, phi1) np_test.assert_array_equal(prod.values, np.array([0, 0, 0, 0, 0, 1, 2, 3, 0, 2, 4, 6, 0, 3, 6, 9])) self.assertEqual(prod.variables, OrderedDict([ ('x1', [State('x1', 0), State('x1', 1)]), ('x2', [State('x2', 0), State('x2', 1)]), ('x3', [State('x3', 0), State('x3', 1)]), ('x4', [State('x4', 0), State('x4', 1)])] )) phi = Factor(['x1', 'x2'], [3, 2], range(6)) phi1 = Factor(['x2', 'x3'], [2, 2], range(4)) prod = factor_product(phi, phi1) np_test.assert_array_equal(prod.values, np.array([0, 0, 2, 3, 0, 2, 6, 9, 0, 4, 10, 15])) self.assertEqual(prod.variables, OrderedDict( [('x1', [State('x1', 0), State('x1', 1), State('x1', 2)]), ('x2', [State('x2', 0), State('x2', 1)]), ('x3', [State('x3', 0), State('x3', 1)])])) def test_factor_product2(self): from pgmpy import factors phi = factors.Factor(['x1', 'x2'], [2, 2], range(4)) phi1 = factors.Factor(['x3', 'x4'], [2, 2], range(4)) prod = phi.product(phi1) np_test.assert_array_equal(prod.values, np.array([0, 0, 0, 0, 0, 1, 2, 3, 0, 2, 4, 6, 0, 3, 6, 9])) self.assertEqual(prod.variables, OrderedDict([ ('x1', [State('x1', 0), State('x1', 1)]), ('x2', [State('x2', 0), State('x2', 1)]), ('x3', [State('x3', 0), State('x3', 1)]), ('x4', [State('x4', 0), State('x4', 1)])] )) phi = Factor(['x1', 'x2'], [3, 2], range(6)) phi1 = Factor(['x2', 'x3'], [2, 2], range(4)) prod = phi.product(phi1) np_test.assert_array_equal(prod.values, np.array([0, 0, 2, 3, 0, 2, 6, 9, 0, 4, 10, 15])) self.assertEqual(prod.variables, OrderedDict( [('x1', [State('x1', 0), State('x1', 1), State('x1', 2)]), ('x2', [State('x2', 0), State('x2', 1)]), ('x3', [State('x3', 0), State('x3', 1)])])) def test_factor_product_non_factor_arg(self): self.assertRaises(TypeError, factor_product, 1, 2) def test_factor_mul(self): phi = Factor(['x1', 'x2'], [2, 2], range(4)) phi1 = Factor(['x3', 'x4'], [2, 2], range(4)) prod = phi * phi1 np_test.assert_array_equal(prod.values, np.array([0, 0, 0, 0, 0, 1, 2, 3, 0, 2, 4, 6, 0, 3, 6, 9])) self.assertEqual(prod.variables, OrderedDict([ ('x1', [State('x1', 0), State('x1', 1)]), ('x2', [State('x2', 0), State('x2', 1)]), ('x3', [State('x3', 0), State('x3', 1)]), ('x4', [State('x4', 0), State('x4', 1)])] )) def test_factor_divide(self): phi1 = Factor(['x1', 'x2'], [2, 2], [1, 2, 2, 4]) phi2 = Factor(['x1'], [2], [1, 2]) div = phi1.divide(phi2) phi3 = Factor(['x1', 'x2'], [2, 2], [1, 2, 1, 2]) self.assertEqual(phi3, div) def test_factor_divide_truediv(self): phi1 = Factor(['x1', 'x2'], [2, 2], [1, 2, 2, 4]) phi2 = Factor(['x1'], [2], [1, 2]) div = phi1 / phi2 phi3 = Factor(['x1', 'x2'], [2, 2], [1, 2, 1, 2]) self.assertEqual(phi3, div) # def test_factor_divide_dividebyzero(self): # phi1 = Factor(['x1', 'x2'], [2, 2], [1, 2, 3, 4]) # phi2 = Factor(['x1'], [2], [0, 2]) # self.assertRaises(FloatingPointError, factor_divide, phi1, phi2) # def test_factor_divide_invalidvalue(self): phi1 = Factor(['x1', 'x2'], [3, 2], [0.5, 0.2, 0, 0, 0.3, 0.45]) phi2 = Factor(['x1'], [3], [0.8, 0, 0.6]) div = phi1.divide(phi2) np_test.assert_array_equal(div.values, np.array([0.625, 0.25, 0, 0, 0.5, 0.75])) def test_factor_divide_no_common_scope(self): phi1 = Factor(['x1', 'x2'], [2, 2], [1, 2, 3, 4]) phi2 = Factor(['x3'], [2], [0, 2]) self.assertRaises(ValueError, factor_divide, phi1, phi2) def test_factor_divide_non_factor_arg(self): self.assertRaises(TypeError, factor_divide, 1, 1) def test_eq(self): self.assertFalse(self.phi == self.phi1) self.assertTrue(self.phi == self.phi) self.assertTrue(self.phi1 == self.phi1) def test_eq1(self): phi1 = Factor(['x1', 'x2', 'x3'], [2, 4, 3], range(24)) phi2 = Factor(['x2', 'x1', 'x3'], [4, 2, 3], [0, 1, 2, 12, 13, 14, 3, 4, 5, 15, 16, 17, 6, 7, 8, 18, 19, 20, 9, 10, 11, 21, 22, 23]) self.assertTrue(phi1, phi2) def test_index_for_assignment(self): for i, j in enumerate(itertools.product(*[range(2), range(3), range(2)])): self.assertEqual(self.phi1._index_for_assignment(j), i) def test_maximize1(self): self.phi1.maximize('x1') self.assertEqual(self.phi1, Factor(['x2', 'x3'], [3, 2], [6, 7, 8, 9, 10, 11])) self.phi1.maximize('x2') self.assertEqual(self.phi1, Factor(['x3'], [2], [10, 11])) def test_maximize2(self): self.phi1.maximize(['x1', 'x2']) self.assertEqual(self.phi1, Factor(['x3'], [2], [10, 11])) def test_maximize3(self): self.phi2 = Factor(['x1', 'x2', 'x3'], [3, 2, 2], [0.25, 0.35, 0.08, 0.16, 0.05, 0.07, 0.00, 0.00, 0.15, 0.21, 0.08, 0.18]) self.phi2.maximize('x2') self.assertEqual(self.phi2, Factor(['x1', 'x3'], [3, 2], [0.25, 0.35, 0.05, 0.07, 0.15, 0.21])) def tearDown(self): del self.phi del self.phi1
class TestFactorMethods(unittest.TestCase): def setUp(self): self.phi = Factor(['x1', 'x2', 'x3'], [2, 2, 2], np.random.uniform(5, 10, size=8)) self.phi1 = Factor(['x1', 'x2', 'x3'], [2, 3, 2], range(12)) self.phi2 = Factor([('x1', 0), ('x2', 0), ('x3', 0)], [2, 3, 2], range(12)) # This larger factor (phi3) caused a bug in reduce card3 = [3, 3, 3, 2, 2, 2, 2, 2, 2] self.phi3 = Factor(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'], card3, np.arange(np.prod(card3), dtype=np.float)) def test_scope(self): self.assertListEqual(self.phi.scope(), ['x1', 'x2', 'x3']) self.assertListEqual(self.phi1.scope(), ['x1', 'x2', 'x3']) def test_assignment(self): self.assertListEqual(self.phi.assignment([0]), [[('x1', 0), ('x2', 0), ('x3', 0)]]) self.assertListEqual(self.phi.assignment([4, 5, 6]), [[('x1', 1), ('x2', 0), ('x3', 0)], [('x1', 1), ('x2', 0), ('x3', 1)], [('x1', 1), ('x2', 1), ('x3', 0)]]) self.assertListEqual(self.phi1.assignment(np.array([4, 5, 6])), [[('x1', 0), ('x2', 2), ('x3', 0)], [('x1', 0), ('x2', 2), ('x3', 1)], [('x1', 1), ('x2', 0), ('x3', 0)]]) def test_assignment_indexerror(self): self.assertRaises(IndexError, self.phi.assignment, [10]) self.assertRaises(IndexError, self.phi.assignment, [1, 3, 10, 5]) self.assertRaises(IndexError, self.phi.assignment, np.array([1, 3, 10, 5])) def test_get_cardinality(self): self.assertEqual(self.phi.get_cardinality(['x1']), {'x1': 2}) self.assertEqual(self.phi.get_cardinality(['x2']), {'x2': 2}) self.assertEqual(self.phi.get_cardinality(['x3']), {'x3': 2}) self.assertEqual(self.phi.get_cardinality(['x1', 'x2']), {'x1': 2, 'x2': 2}) self.assertEqual(self.phi.get_cardinality(['x1', 'x3']), {'x1': 2, 'x3': 2}) self.assertEqual(self.phi.get_cardinality(['x1', 'x2', 'x3']), {'x1': 2, 'x2': 2, 'x3': 2}) def test_get_cardinality_scopeerror(self): self.assertRaises(ValueError, self.phi.get_cardinality, ['x4']) def test_get_cardinality_typeerror(self): self.assertRaises(TypeError, self.phi.get_cardinality, 'x1') def test_marginalize(self): self.phi1.marginalize(['x1']) np_test.assert_array_equal(self.phi1.values, np.array([[6, 8], [10, 12], [14, 16]])) self.phi1.marginalize(['x2']) np_test.assert_array_equal(self.phi1.values, np.array([30, 36])) self.phi1.marginalize(['x3']) np_test.assert_array_equal(self.phi1.values, np.array(66)) def test_marginalize_scopeerror(self): self.assertRaises(ValueError, self.phi.marginalize, ['x4']) self.assertRaises(ValueError, self.phi.marginalize, ['x4']) self.phi.marginalize(['x1']) self.assertRaises(ValueError, self.phi.marginalize, ['x1']) def test_marginalize_typeerror(self): self.assertRaises(TypeError, self.phi.marginalize, 'x1') def test_marginalize_shape(self): values = ['A', 'D', 'F', 'H'] phi3_max = self.phi3.marginalize(values, inplace=False) # Previously a sorting error caused these to be different np_test.assert_array_equal(phi3_max.values.shape, phi3_max.cardinality) def test_normalize(self): self.phi1.normalize() np_test.assert_almost_equal(self.phi1.values, np.array([[[0, 0.01515152], [0.03030303, 0.04545455], [0.06060606, 0.07575758]], [[0.09090909, 0.10606061], [0.12121212, 0.13636364], [0.15151515, 0.16666667]]])) def test_reduce(self): self.phi1.reduce([('x1', 0), ('x2', 0)]) np_test.assert_array_equal(self.phi1.values, np.array([0, 1])) def test_reduce1(self): self.phi1.reduce([('x2', 0), ('x1', 0)]) np_test.assert_array_equal(self.phi1.values, np.array([0, 1])) def test_reduce_shape(self): values = [('A', 0), ('D', 0), ('F', 0), ('H', 1)] phi3_reduced = self.phi3.reduce(values, inplace=False) # Previously a sorting error caused these to be different np_test.assert_array_equal(phi3_reduced.values.shape, phi3_reduced.cardinality) @unittest.skip def test_complete_reduce(self): self.phi1.reduce([('x1', 0), ('x2', 0), ('x3', 1)]) np_test.assert_array_equal(self.phi1.values, np.array([1])) np_test.assert_array_equal(self.phi1.cardinality, np.array([])) np_test.assert_array_equal(self.phi1.variables, OrderedDict()) def test_reduce_typeerror(self): self.assertRaises(TypeError, self.phi1.reduce, 'x10') self.assertRaises(TypeError, self.phi1.reduce, ['x10']) self.assertRaises(TypeError, self.phi1.reduce, [('x1', 'x2')]) self.assertRaises(TypeError, self.phi1.reduce, [(0, 'x1')]) self.assertRaises(TypeError, self.phi1.reduce, [(0.1, 'x1')]) self.assertRaises(TypeError, self.phi1.reduce, [(0.1, 0.1)]) self.assertRaises(TypeError, self.phi1.reduce, [('x1', 0.1)]) def test_reduce_scopeerror(self): self.assertRaises(ValueError, self.phi1.reduce, [('x4', 1)]) def test_reduce_sizeerror(self): self.assertRaises(IndexError, self.phi1.reduce, [('x3', 5)]) def test_identity_factor(self): identity_factor = self.phi.identity_factor() self.assertEqual(list(identity_factor.variables), ['x1', 'x2', 'x3']) np_test.assert_array_equal(identity_factor.cardinality, [2, 2, 2]) np_test.assert_array_equal(identity_factor.values, np.ones(8).reshape(2, 2, 2)) def test_factor_product(self): phi = Factor(['x1', 'x2'], [2, 2], range(4)) phi1 = Factor(['x3', 'x4'], [2, 2], range(4)) prod = factor_product(phi, phi1) expected_factor = Factor(['x1', 'x2', 'x3', 'x4'], [2, 2, 2, 2], [0, 0, 0, 0, 0, 1, 2, 3, 0, 2, 4, 6, 0, 3, 6, 9]) self.assertEqual(prod, expected_factor) self.assertEqual(sorted(prod.variables), ['x1', 'x2', 'x3', 'x4']) phi = Factor(['x1', 'x2'], [3, 2], range(6)) phi1 = Factor(['x2', 'x3'], [2, 2], range(4)) prod = factor_product(phi, phi1) expected_factor = Factor(['x1', 'x2', 'x3'], [3, 2, 2], [0, 0, 2, 3, 0, 2, 6, 9, 0, 4, 10, 15]) np_test.assert_almost_equal(prod.values, np.array([0, 0, 2, 3, 0, 2, 6, 9, 0, 4, 10, 15]).reshape(3, 2, 2)) self.assertEqual(sorted(prod.variables), ['x1', 'x2', 'x3']) def test_factor_product2(self): from pgmpy import factors phi = factors.Factor(['x1', 'x2'], [2, 2], range(4)) phi1 = factors.Factor(['x3', 'x4'], [2, 2], range(4)) prod = phi.product(phi1, inplace=False) expected_factor = Factor(['x1', 'x2', 'x3', 'x4'], [2, 2, 2, 2], [0, 0, 0, 0, 0, 1, 2, 3, 0, 2, 4, 6, 0, 3, 6, 9]) self.assertEqual(prod, expected_factor) self.assertEqual(sorted(prod.variables), ['x1', 'x2', 'x3', 'x4']) phi = Factor(['x1', 'x2'], [3, 2], range(6)) phi1 = Factor(['x2', 'x3'], [2, 2], range(4)) prod = phi.product(phi1, inplace=False) expected_factor = Factor(['x1', 'x2', 'x3'], [3, 2, 2], [0, 0, 2, 3, 0, 2, 6, 9, 0, 4, 10, 15]) self.assertEqual(prod, expected_factor) self.assertEqual(sorted(prod.variables), ['x1', 'x2', 'x3']) def test_factor_product_non_factor_arg(self): self.assertRaises(TypeError, factor_product, 1, 2) def test_factor_mul(self): phi = Factor(['x1', 'x2'], [2, 2], range(4)) phi1 = Factor(['x3', 'x4'], [2, 2], range(4)) prod = phi * phi1 sorted_vars = ['x1', 'x2', 'x3', 'x4'] for axis in range(prod.values.ndim): exchange_index = prod.variables.index(sorted_vars[axis]) prod.variables[axis], prod.variables[exchange_index] = prod.variables[exchange_index], prod.variables[axis] prod.values = prod.values.swapaxes(axis, exchange_index) np_test.assert_almost_equal(prod.values.ravel(), np.array([0, 0, 0, 0, 0, 1, 2, 3, 0, 2, 4, 6, 0, 3, 6, 9])) self.assertEqual(prod.variables, ['x1', 'x2', 'x3', 'x4']) def test_factor_divide(self): phi1 = Factor(['x1', 'x2'], [2, 2], [1, 2, 2, 4]) phi2 = Factor(['x1'], [2], [1, 2]) div = phi1.divide(phi2, inplace=False) phi3 = Factor(['x1', 'x2'], [2, 2], [1, 2, 1, 2]) self.assertEqual(phi3, div) def test_factor_divide_truediv(self): phi1 = Factor(['x1', 'x2'], [2, 2], [1, 2, 2, 4]) phi2 = Factor(['x1'], [2], [1, 2]) div = phi1 / phi2 phi3 = Factor(['x1', 'x2'], [2, 2], [1, 2, 1, 2]) self.assertEqual(phi3, div) def test_factor_divide_invalid(self): phi1 = Factor(['x1', 'x2'], [2, 2], [1, 2, 3, 4]) phi2 = Factor(['x1'], [2], [0, 2]) div = phi1.divide(phi2, inplace=False) np_test.assert_array_equal(div.values.ravel(), np.array([np.inf, np.inf, 1.5, 2])) def test_factor_divide_no_common_scope(self): phi1 = Factor(['x1', 'x2'], [2, 2], [1, 2, 3, 4]) phi2 = Factor(['x3'], [2], [0, 2]) self.assertRaises(ValueError, factor_divide, phi1, phi2) def test_factor_divide_non_factor_arg(self): self.assertRaises(TypeError, factor_divide, 1, 1) def test_eq(self): self.assertFalse(self.phi == self.phi1) self.assertTrue(self.phi == self.phi) self.assertTrue(self.phi1 == self.phi1) def test_eq1(self): phi1 = Factor(['x1', 'x2', 'x3'], [2, 4, 3], range(24)) phi2 = Factor(['x2', 'x1', 'x3'], [4, 2, 3], [0, 1, 2, 12, 13, 14, 3, 4, 5, 15, 16, 17, 6, 7, 8, 18, 19, 20, 9, 10, 11, 21, 22, 23]) self.assertTrue(phi1, phi2) def test_maximize1(self): self.phi1.maximize(['x1']) self.assertEqual(self.phi1, Factor(['x2', 'x3'], [3, 2], [6, 7, 8, 9, 10, 11])) self.phi1.maximize(['x2']) self.assertEqual(self.phi1, Factor(['x3'], [2], [10, 11])) def test_maximize2(self): self.phi1.maximize(['x1', 'x2']) self.assertEqual(self.phi1, Factor(['x3'], [2], [10, 11])) def test_maximize3(self): self.phi2 = Factor(['x1', 'x2', 'x3'], [3, 2, 2], [0.25, 0.35, 0.08, 0.16, 0.05, 0.07, 0.00, 0.00, 0.15, 0.21, 0.08, 0.18]) self.phi2.maximize(['x2']) self.assertEqual(self.phi2, Factor(['x1', 'x3'], [3, 2], [0.25, 0.35, 0.05, 0.07, 0.15, 0.21])) def test_maximize_shape(self): values = ['A', 'D', 'F', 'H'] phi3_max = self.phi3.maximize(values, inplace=False) # Previously a sorting error caused these to be different np_test.assert_array_equal(phi3_max.values.shape, phi3_max.cardinality) def test_maximize_scopeerror(self): self.assertRaises(ValueError, self.phi.maximize, ['x10']) def test_maximize_typeerror(self): self.assertRaises(TypeError, self.phi.maximize, 'x1') def tearDown(self): del self.phi del self.phi1
class StateNameDecorator(unittest.TestCase): def setUp(self): self.sn2 = {'grade': ['A', 'B', 'F'], 'diff': ['high', 'low'], 'intel': ['poor', 'good', 'very good']} self.sn1 = {'speed': ['low', 'medium', 'high'], 'switch': ['on', 'off'], 'time': ['day', 'night']} self.phi1 = Factor(['speed', 'switch', 'time'], [3, 2, 2], np.ones(12)) self.phi2 = Factor(['speed', 'switch', 'time'], [3, 2, 2], np.ones(12), state_names=self.sn1) self.cpd1 = TabularCPD('grade', 3, [[0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.8, 0.8, 0.8, 0.8, 0.8, 0.8]], evidence=['diff', 'intel'], evidence_card=[2, 3]) self.cpd2 = TabularCPD('grade', 3, [[0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.8, 0.8, 0.8, 0.8, 0.8, 0.8]], evidence=['diff', 'intel'], evidence_card=[2, 3], state_names=self.sn2) student = BayesianModel([('diff', 'grade'), ('intel', 'grade')]) diff_cpd = TabularCPD('diff', 2, [[0.2, 0.8]]) intel_cpd = TabularCPD('intel', 2, [[0.3, 0.7]]) grade_cpd = TabularCPD('grade', 3, [[0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1], [0.8, 0.8, 0.8, 0.8]], evidence=['diff', 'intel'], evidence_card=[2, 2]) student.add_cpds(diff_cpd, intel_cpd, grade_cpd) self.model1 = VariableElimination(student) self.model2 = VariableElimination(student, state_names=self.sn2) def test_assignment_statename(self): req_op1 = [[('speed', 'low'), ('switch', 'on'), ('time', 'night')], [('speed', 'low'), ('switch', 'off'), ('time', 'day')]] req_op2 = [[('speed', 0), ('switch', 0), ('time', 1)], [('speed', 0), ('switch', 1), ('time', 0)]] self.assertEqual(self.phi1.assignment([1, 2]), req_op2) self.assertEqual(self.phi2.assignment([1, 2]), req_op1) def test_factor_reduce_statename(self): phi = Factor(['speed', 'switch', 'time'], [3, 2, 2], np.ones(12), state_names=self.sn1) phi.reduce([('speed', 'medium'), ('time', 'day')]) self.assertEqual(phi.variables, ['switch']) self.assertEqual(phi.cardinality, [2]) np_test.assert_array_equal(phi.values, np.array([1, 1])) phi = Factor(['speed', 'switch', 'time'], [3, 2, 2], np.ones(12), state_names=self.sn1) phi = phi.reduce([('speed', 'medium'), ('time', 'day')], inplace=False) self.assertEqual(phi.variables, ['switch']) self.assertEqual(phi.cardinality, [2]) np_test.assert_array_equal(phi.values, np.array([1, 1])) phi = Factor(['speed', 'switch', 'time'], [3, 2, 2], np.ones(12), state_names=self.sn1) phi.reduce([('speed', 1), ('time', 0)]) self.assertEqual(phi.variables, ['switch']) self.assertEqual(phi.cardinality, [2]) np_test.assert_array_equal(phi.values, np.array([1, 1])) phi = Factor(['speed', 'switch', 'time'], [3, 2, 2], np.ones(12), state_names=self.sn1) phi = phi.reduce([('speed', 1), ('time', 0)], inplace=False) self.assertEqual(phi.variables, ['switch']) self.assertEqual(phi.cardinality, [2]) np_test.assert_array_equal(phi.values, np.array([1, 1])) def test_reduce_cpd_statename(self): cpd = TabularCPD('grade', 3, [[0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.8, 0.8, 0.8, 0.8, 0.8, 0.8]], evidence=['diff', 'intel'], evidence_card=[2, 3], state_names=self.sn2) cpd.reduce([('diff', 'high')]) self.assertEqual(cpd.variable, 'grade') self.assertEqual(cpd.variables, ['grade', 'intel']) np_test.assert_array_equal(cpd.get_cpd(), np.array([[0.1, 0.1, 0.1], [0.1, 0.1, 0.1], [0.8, 0.8, 0.8]])) cpd = TabularCPD('grade', 3, [[0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.8, 0.8, 0.8, 0.8, 0.8, 0.8]], evidence=['diff', 'intel'], evidence_card=[2, 3], state_names=self.sn2) cpd.reduce([('diff', 0)]) self.assertEqual(cpd.variable, 'grade') self.assertEqual(cpd.variables, ['grade', 'intel']) np_test.assert_array_equal(cpd.get_cpd(), np.array([[0.1, 0.1, 0.1], [0.1, 0.1, 0.1], [0.8, 0.8, 0.8]])) cpd = TabularCPD('grade', 3, [[0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.8, 0.8, 0.8, 0.8, 0.8, 0.8]], evidence=['diff', 'intel'], evidence_card=[2, 3], state_names=self.sn2) cpd = cpd.reduce([('diff', 'high')], inplace=False) self.assertEqual(cpd.variable, 'grade') self.assertEqual(cpd.variables, ['grade', 'intel']) np_test.assert_array_equal(cpd.get_cpd(), np.array([[0.1, 0.1, 0.1], [0.1, 0.1, 0.1], [0.8, 0.8, 0.8]])) cpd = TabularCPD('grade', 3, [[0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.8, 0.8, 0.8, 0.8, 0.8, 0.8]], evidence=['diff', 'intel'], evidence_card=[2, 3], state_names=self.sn2) cpd = cpd.reduce([('diff', 0)], inplace=False) self.assertEqual(cpd.variable, 'grade') self.assertEqual(cpd.variables, ['grade', 'intel']) np_test.assert_array_equal(cpd.get_cpd(), np.array([[0.1, 0.1, 0.1], [0.1, 0.1, 0.1], [0.8, 0.8, 0.8]])) def test_inference_query_statename(self): inf_op1 = self.model2.query(['grade'], evidence={'intel': 'poor'}) inf_op2 = self.model2.query(['grade'], evidence={'intel': 0}) req_op = {'grade': Factor(['grade'], [3], np.array([0.1, 0.1, 0.8]))} self.assertEqual(inf_op1, inf_op2) self.assertEqual(inf_op1, req_op) self.assertEqual(inf_op1, req_op) inf_op1 = self.model2.map_query(['grade'], evidence={'intel': 'poor'}) inf_op2 = self.model2.map_query(['grade'], evidence={'intel': 0}) req_op = {'grade': 'F'} self.assertEqual(inf_op1, inf_op2) self.assertEqual(inf_op1, req_op) self.assertEqual(inf_op1, req_op)