def setUp(self):
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=['intel', 'diff'],
evidence_card=[3, 2])
def test_cpd_init(self):
cpd = TabularCPD('grade', 3, [[0.1, 0.1, 0.1]])
self.assertEqual(cpd.variable, 'grade')
self.assertEqual(cpd.variable_card, 3)
self.assertEqual(list(cpd.variables), ['grade'])
np_test.assert_array_equal(cpd.cardinality, np.array([3]))
np_test.assert_array_almost_equal(cpd.values, np.array([0.1, 0.1, 0.1]))
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=['intel', 'diff'], evidence_card=[3, 2])
self.assertEqual(cpd.variable, 'grade')
self.assertEqual(cpd.variable_card, 3)
np_test.assert_array_equal(cpd.cardinality, np.array([3, 2, 3]))
self.assertListEqual(list(cpd.variables), ['grade', 'diff', 'intel'])
np_test.assert_array_equal(cpd.values, np.array([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]).reshape(3, 2, 3))
cpd = TabularCPD('grade', 3, [[0.1, 0.1],
[0.1, 0.1],
[0.8, 0.8]],
evidence='evi1', evidence_card=2)
self.assertEqual(cpd.variable, 'grade')
self.assertEqual(cpd.variable_card, 3)
np_test.assert_array_equal(cpd.cardinality, np.array([3, 2]))
self.assertListEqual(list(cpd.variables), ['grade', 'evi1'])
np_test.assert_array_equal(cpd.values, np.array([0.1, 0.1,
0.1, 0.1,
0.8, 0.8]).reshape(3, 2))
def test_normalize(self):
cpd_un_normalized = TabularCPD(
'grade', 2, [[0.7, 0.2, 0.6, 0.2], [0.4, 0.4, 0.4, 0.8]],
['intel', 'diff'], [2, 2])
cpd_un_normalized.normalize()
np_test.assert_array_almost_equal(
cpd_un_normalized.values,
np.array([
0.63636364, 0.33333333, 0.6, 0.2, 0.36363636, 0.66666667, 0.4,
0.8
]))
def test__repr__(self):
grade_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=['intel', 'diff'], evidence_card=[3, 2])
intel_cpd = TabularCPD('intel', 3, [[0.5], [0.3], [0.2]])
diff_cpd = TabularCPD('grade', 3, [[0.1, 0.1], [0.1, 0.1], [0.8, 0.8]], evidence=['diff'], evidence_card=[2])
self.assertEqual(repr(grade_cpd), '<TabularCPD representing P(grade:3 | intel:3, diff:2) at {address}>'
.format(address=hex(id(grade_cpd))))
self.assertEqual(repr(intel_cpd), '<TabularCPD representing P(intel:3) at {address}>'
.format(address=hex(id(intel_cpd))))
self.assertEqual(repr(diff_cpd), '<TabularCPD representing P(grade:3 | diff:2) at {address}>'
.format(address=hex(id(diff_cpd))))
def test_normalize(self):
cpd_un_normalized = TabularCPD('grade', 2, [[0.7, 0.2, 0.6, 0.2], [0.4, 0.4, 0.4, 0.8]],
['intel', 'diff'], [2, 2])
cpd_un_normalized.normalize()
np_test.assert_array_almost_equal(cpd_un_normalized.values, np.array([0.63636364, 0.33333333, 0.6, 0.2,
0.36363636, 0.66666667, 0.4, 0.8]))
def setUp(self):
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=['intel', 'diff'], evidence_card=[3, 2])
class TestTabularCPDMethods(unittest.TestCase):
def setUp(self):
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=['intel', 'diff'], evidence_card=[3, 2])
def test_marginalize_1(self):
self.cpd.marginalize('diff')
self.assertEqual(self.cpd.variable, 'grade')
self.assertEqual(self.cpd.variable_card, 3)
self.assertListEqual(list(self.cpd.variables), ['grade', 'intel'])
np_test.assert_array_equal(self.cpd.cardinality, np.array([3, 3]))
np_test.assert_array_equal(self.cpd.values, np.array([0.2, 0.2, 0.2,
0.2, 0.2, 0.2,
1.6, 1.6, 1.6]))
def test_marginalize_2(self):
self.cpd.marginalize('grade')
self.assertEqual(self.cpd.variable, 'grade')
self.assertListEqual(list(self.cpd.variables), ['intel', 'diff'])
np_test.assert_array_equal(self.cpd.cardinality, np.array([3, 2]))
np_test.assert_array_equal(self.cpd.values, np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0]))
def test_normalize(self):
cpd_un_normalized = TabularCPD('grade', 2, [[0.7, 0.2, 0.6, 0.2], [0.4, 0.4, 0.4, 0.8]],
['intel', 'diff'], [2, 2])
cpd_un_normalized.normalize()
np_test.assert_array_almost_equal(cpd_un_normalized.values, np.array([0.63636364, 0.33333333, 0.6, 0.2,
0.36363636, 0.66666667, 0.4, 0.8]))
def test_reduce_1(self):
self.cpd.reduce('diff_0')
np_test.assert_array_equal(self.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_reduce_2(self):
self.cpd.reduce('intel_0')
np_test.assert_array_equal(self.cpd.get_cpd(), np.array([[0.1, 0.1],
[0.1, 0.1],
[0.8, 0.8]]))
def test_reduce_3(self):
self.cpd.reduce(['intel_0', 'diff_0'])
np_test.assert_array_equal(self.cpd.get_cpd(), np.array([[0.1],
[0.1],
[0.8]]))
def test_reduce_4(self):
self.cpd.reduce('grade_0')
np_test.assert_array_equal(self.cpd.get_cpd(), np.array([[0.1, 0.1, 0.1, 0.1, 0.1, 0.1]]))
def test_get_cpd(self):
np_test.assert_array_equal(self.cpd.get_cpd(), np.array([[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]]))
def tearDown(self):
del self.cpd
class TestTabularCPDMethods(unittest.TestCase):
def setUp(self):
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=['intel', 'diff'],
evidence_card=[3, 2])
def test_marginalize_1(self):
self.cpd.marginalize('diff')
self.assertEqual(self.cpd.variable, 'grade')
self.assertEqual(self.cpd.variable_card, 3)
self.assertListEqual(list(self.cpd.variables), ['grade', 'intel'])
np_test.assert_array_equal(self.cpd.cardinality, np.array([3, 3]))
np_test.assert_array_equal(
self.cpd.values,
np.array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.8, 0.8, 0.8]))
def test_marginalize_2(self):
self.cpd.marginalize('grade')
self.assertEqual(self.cpd.variable, 'grade')
self.assertListEqual(list(self.cpd.variables), ['diff', 'intel'])
np_test.assert_array_equal(self.cpd.cardinality, np.array([2, 3]))
np_test.assert_array_equal(self.cpd.values,
np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0]))
def test_normalize(self):
cpd_un_normalized = TabularCPD(
'grade', 2, [[0.7, 0.2, 0.6, 0.2], [0.4, 0.4, 0.4, 0.8]],
['intel', 'diff'], [2, 2])
cpd_un_normalized.normalize()
np_test.assert_array_almost_equal(
cpd_un_normalized.values,
np.array([
0.63636364, 0.33333333, 0.6, 0.2, 0.36363636, 0.66666667, 0.4,
0.8
]))
def test__repr__(self):
grade_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=['intel', 'diff'],
evidence_card=[3, 2])
intel_cpd = TabularCPD('intel', 3, [[0.5], [0.3], [0.2]])
diff_cpd = TabularCPD('grade',
3, [[0.1, 0.1], [0.1, 0.1], [0.8, 0.8]],
evidence=['diff'],
evidence_card=[2])
self.assertEqual(
repr(grade_cpd),
'<TabularCPD representing P(grade:3 | intel:3, diff:2) at {address}>'
.format(address=hex(id(grade_cpd))))
self.assertEqual(
repr(intel_cpd),
'<TabularCPD representing P(intel:3) at {address}>'.format(
address=hex(id(intel_cpd))))
self.assertEqual(
repr(diff_cpd),
'<TabularCPD representing P(grade:3 | diff:2) at {address}>'.
format(address=hex(id(diff_cpd))))
def test_reduce_1(self):
self.cpd.reduce(('diff', 0))
np_test.assert_array_equal(
self.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_reduce_2(self):
self.cpd.reduce(('intel', 0))
np_test.assert_array_equal(
self.cpd.get_cpd(), np.array([[0.1, 0.1], [0.1, 0.1], [0.8, 0.8]]))
def test_reduce_3(self):
self.cpd.reduce([('intel', 0), ('diff', 0)])
np_test.assert_array_equal(self.cpd.get_cpd(),
np.array([[0.1], [0.1], [0.8]]))
def test_reduce_4(self):
self.cpd.reduce(('grade', 0))
np_test.assert_array_equal(self.cpd.get_cpd(),
np.array([[1, 1, 1, 1, 1, 1]]))
def test_get_cpd(self):
np_test.assert_array_equal(
self.cpd.get_cpd(),
np.array([[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]]))
def tearDown(self):
del self.cpd
class TestTabularCPDMethods(unittest.TestCase):
def setUp(self):
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=['intel', 'diff'], evidence_card=[3, 2])
def test_marginalize_1(self):
self.cpd.marginalize(['diff'])
self.assertEqual(self.cpd.variable, 'grade')
self.assertEqual(self.cpd.variable_card, 3)
self.assertListEqual(list(self.cpd.variables), ['grade', 'intel'])
np_test.assert_array_equal(self.cpd.cardinality, np.array([3, 3]))
np_test.assert_array_equal(self.cpd.values.ravel(), np.array([0.1, 0.1, 0.1,
0.1, 0.1, 0.1,
0.8, 0.8, 0.8]))
def test_marginalize_2(self):
self.cpd.marginalize(['grade'])
self.assertEqual(self.cpd.variable, 'grade')
self.assertListEqual(list(self.cpd.variables), ['diff', 'intel'])
np_test.assert_array_equal(self.cpd.cardinality, np.array([2, 3]))
np_test.assert_array_equal(self.cpd.values.ravel(), np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0]))
def test_normalize(self):
cpd_un_normalized = TabularCPD('grade', 2, [[0.7, 0.2, 0.6, 0.2], [0.4, 0.4, 0.4, 0.8]],
['intel', 'diff'], [2, 2])
cpd_un_normalized.normalize()
np_test.assert_array_almost_equal(cpd_un_normalized.values.ravel(), np.array([0.63636364, 0.33333333, 0.6, 0.2,
0.36363636, 0.66666667, 0.4, 0.8]))
def test__repr__(self):
grade_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=['intel', 'diff'], evidence_card=[3, 2])
intel_cpd = TabularCPD('intel', 3, [[0.5], [0.3], [0.2]])
diff_cpd = TabularCPD('grade', 3, [[0.1, 0.1], [0.1, 0.1], [0.8, 0.8]], evidence=['diff'], evidence_card=[2])
self.assertEqual(repr(grade_cpd), '<TabularCPD representing P(grade:3 | intel:3, diff:2) at {address}>'
.format(address=hex(id(grade_cpd))))
self.assertEqual(repr(intel_cpd), '<TabularCPD representing P(intel:3) at {address}>'
.format(address=hex(id(intel_cpd))))
self.assertEqual(repr(diff_cpd), '<TabularCPD representing P(grade:3 | diff:2) at {address}>'
.format(address=hex(id(diff_cpd))))
def test_reduce_1(self):
self.cpd.reduce([('diff', 0)])
np_test.assert_array_equal(self.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_reduce_2(self):
self.cpd.reduce([('intel', 0)])
np_test.assert_array_equal(self.cpd.get_cpd(), np.array([[0.1, 0.1],
[0.1, 0.1],
[0.8, 0.8]]))
def test_reduce_3(self):
self.cpd.reduce([('intel', 0), ('diff', 0)])
np_test.assert_array_equal(self.cpd.get_cpd(), np.array([[0.1],
[0.1],
[0.8]]))
def test_reduce_4(self):
self.cpd.reduce([('grade', 0)])
np_test.assert_array_equal(self.cpd.get_cpd(), np.array([[1, 1, 1, 1, 1, 1]]))
def test_get_cpd(self):
np_test.assert_array_equal(self.cpd.get_cpd(), np.array([[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]]))
def tearDown(self):
del self.cpd