def setUp(self):
self.bayesian_model = BayesianModel([('A', 'J'), ('R', 'J'),
('J', 'Q'), ('J', 'L'),
('G', 'L')])
cpd_a = TabularCPD('A', 2, [[0.2], [0.8]])
cpd_r = TabularCPD('R', 2, [[0.4], [0.6]])
cpd_j = TabularCPD('J', 2,
[[0.9, 0.6, 0.7, 0.1], [0.1, 0.4, 0.3, 0.9]],
['R', 'A'], [2, 2])
cpd_q = TabularCPD('Q', 2, [[0.9, 0.2], [0.1, 0.8]], ['J'], [2])
cpd_l = TabularCPD('L', 2,
[[0.9, 0.45, 0.8, 0.1], [0.1, 0.55, 0.2, 0.9]],
['G', 'J'], [2, 2])
cpd_g = TabularCPD('G', 2, [[0.6], [0.4]])
self.bayesian_model.add_cpds(cpd_a, cpd_g, cpd_j, cpd_l, cpd_q, cpd_r)
self.sampling_inference = BayesianModelSampling(self.bayesian_model)
self.markov_model = MarkovModel()
def setUp(self):
self.bayesian_model = BayesianModel([('A', 'J'), ('R', 'J'), ('J', 'Q'),
('J', 'L'), ('G', 'L')])
cpd_a = TabularCPD('A', 2, [[0.2], [0.8]])
cpd_r = TabularCPD('R', 2, [[0.4], [0.6]])
cpd_j = TabularCPD('J', 2,
[[0.9, 0.6, 0.7, 0.1],
[0.1, 0.4, 0.3, 0.9]],
['R', 'A'], [2, 2])
cpd_q = TabularCPD('Q', 2,
[[0.9, 0.2],
[0.1, 0.8]],
['J'], [2])
cpd_l = TabularCPD('L', 2,
[[0.9, 0.45, 0.8, 0.1],
[0.1, 0.55, 0.2, 0.9]],
['G', 'J'], [2, 2])
cpd_g = TabularCPD('G', 2, [[0.6], [0.4]])
self.bayesian_model.add_cpds(cpd_a, cpd_g, cpd_j, cpd_l, cpd_q, cpd_r)
self.sampling_inference = BayesianModelSampling(self.bayesian_model)
self.markov_model = MarkovModel()
class TestBayesianModelSampling(unittest.TestCase):
def setUp(self):
self.bayesian_model = BayesianModel([('A', 'J'), ('R', 'J'), ('J', 'Q'),
('J', 'L'), ('G', 'L')])
cpd_a = TabularCPD('A', 2, [[0.2], [0.8]])
cpd_r = TabularCPD('R', 2, [[0.4], [0.6]])
cpd_j = TabularCPD('J', 2,
[[0.9, 0.6, 0.7, 0.1],
[0.1, 0.4, 0.3, 0.9]],
['R', 'A'], [2, 2])
cpd_q = TabularCPD('Q', 2,
[[0.9, 0.2],
[0.1, 0.8]],
['J'], [2])
cpd_l = TabularCPD('L', 2,
[[0.9, 0.45, 0.8, 0.1],
[0.1, 0.55, 0.2, 0.9]],
['G', 'J'], [2, 2])
cpd_g = TabularCPD('G', 2, [[0.6], [0.4]])
self.bayesian_model.add_cpds(cpd_a, cpd_g, cpd_j, cpd_l, cpd_q, cpd_r)
self.sampling_inference = BayesianModelSampling(self.bayesian_model)
self.markov_model = MarkovModel()
def test_init(self):
with self.assertRaises(TypeError):
BayesianModelSampling(self.markov_model)
def test_forward_sample(self):
sample = self.sampling_inference.forward_sample(25)
self.assertEquals(len(sample), 25)
self.assertEquals(len(sample.columns), 6)
self.assertIn('A', sample.columns)
self.assertIn('J', sample.columns)
self.assertIn('R', sample.columns)
self.assertIn('Q', sample.columns)
self.assertIn('G', sample.columns)
self.assertIn('L', sample.columns)
self.assertTrue(set(sample.A).issubset({State('A', 0), State('A', 1)}))
self.assertTrue(set(sample.J).issubset({State('J', 0), State('J', 1)}))
self.assertTrue(set(sample.R).issubset({State('R', 0), State('R', 1)}))
self.assertTrue(set(sample.Q).issubset({State('Q', 0), State('Q', 1)}))
self.assertTrue(set(sample.G).issubset({State('G', 0), State('G', 1)}))
self.assertTrue(set(sample.L).issubset({State('L', 0), State('L', 1)}))
def test_rejection_sample_basic(self):
sample = self.sampling_inference.rejection_sample([State('A', 1), State('J', 1), State('R', 1)], 25)
self.assertEquals(len(sample), 25)
self.assertEquals(len(sample.columns), 6)
self.assertIn('A', sample.columns)
self.assertIn('J', sample.columns)
self.assertIn('R', sample.columns)
self.assertIn('Q', sample.columns)
self.assertIn('G', sample.columns)
self.assertIn('L', sample.columns)
self.assertTrue(set(sample.A).issubset({State('A', 1)}))
self.assertTrue(set(sample.J).issubset({State('J', 1)}))
self.assertTrue(set(sample.R).issubset({State('R', 1)}))
self.assertTrue(set(sample.Q).issubset({State('Q', 0), State('Q', 1)}))
self.assertTrue(set(sample.G).issubset({State('G', 0), State('G', 1)}))
self.assertTrue(set(sample.L).issubset({State('L', 0), State('L', 1)}))
def test_likelihood_weighted_sample(self):
sample = self.sampling_inference.likelihood_weighted_sample([State('A', 0), State('J', 1), State('R', 0)], 25)
self.assertEquals(len(sample), 25)
self.assertEquals(len(sample.columns), 7)
self.assertIn('A', sample.columns)
self.assertIn('J', sample.columns)
self.assertIn('R', sample.columns)
self.assertIn('Q', sample.columns)
self.assertIn('G', sample.columns)
self.assertIn('L', sample.columns)
self.assertIn('_weight', sample.columns)
self.assertTrue(set(sample.A).issubset({State('A', 0), State('A', 1)}))
self.assertTrue(set(sample.J).issubset({State('J', 0), State('J', 1)}))
self.assertTrue(set(sample.R).issubset({State('R', 0), State('R', 1)}))
self.assertTrue(set(sample.Q).issubset({State('Q', 0), State('Q', 1)}))
self.assertTrue(set(sample.G).issubset({State('G', 0), State('G', 1)}))
self.assertTrue(set(sample.L).issubset({State('L', 0), State('L', 1)}))
def tearDown(self):
del self.sampling_inference
del self.bayesian_model
del self.markov_model
class TestBayesianModelSampling(unittest.TestCase):
def setUp(self):
self.bayesian_model = BayesianModel([('A', 'J'), ('R', 'J'),
('J', 'Q'), ('J', 'L'),
('G', 'L')])
cpd_a = TabularCPD('A', 2, [[0.2], [0.8]])
cpd_r = TabularCPD('R', 2, [[0.4], [0.6]])
cpd_j = TabularCPD('J', 2,
[[0.9, 0.6, 0.7, 0.1], [0.1, 0.4, 0.3, 0.9]],
['R', 'A'], [2, 2])
cpd_q = TabularCPD('Q', 2, [[0.9, 0.2], [0.1, 0.8]], ['J'], [2])
cpd_l = TabularCPD('L', 2,
[[0.9, 0.45, 0.8, 0.1], [0.1, 0.55, 0.2, 0.9]],
['G', 'J'], [2, 2])
cpd_g = TabularCPD('G', 2, [[0.6], [0.4]])
self.bayesian_model.add_cpds(cpd_a, cpd_g, cpd_j, cpd_l, cpd_q, cpd_r)
self.sampling_inference = BayesianModelSampling(self.bayesian_model)
self.markov_model = MarkovModel()
def test_init(self):
with self.assertRaises(TypeError):
BayesianModelSampling(self.markov_model)
def test_forward_sample(self):
sample = self.sampling_inference.forward_sample(25)
self.assertEquals(len(sample), 25)
self.assertEquals(len(sample.columns), 6)
self.assertIn('A', sample.columns)
self.assertIn('J', sample.columns)
self.assertIn('R', sample.columns)
self.assertIn('Q', sample.columns)
self.assertIn('G', sample.columns)
self.assertIn('L', sample.columns)
self.assertTrue(set(sample.A).issubset({State('A', 0), State('A', 1)}))
self.assertTrue(set(sample.J).issubset({State('J', 0), State('J', 1)}))
self.assertTrue(set(sample.R).issubset({State('R', 0), State('R', 1)}))
self.assertTrue(set(sample.Q).issubset({State('Q', 0), State('Q', 1)}))
self.assertTrue(set(sample.G).issubset({State('G', 0), State('G', 1)}))
self.assertTrue(set(sample.L).issubset({State('L', 0), State('L', 1)}))
def test_rejection_sample_basic(self):
sample = self.sampling_inference.rejection_sample(
[State('A', 1), State('J', 1),
State('R', 1)], 25)
self.assertEquals(len(sample), 25)
self.assertEquals(len(sample.columns), 6)
self.assertIn('A', sample.columns)
self.assertIn('J', sample.columns)
self.assertIn('R', sample.columns)
self.assertIn('Q', sample.columns)
self.assertIn('G', sample.columns)
self.assertIn('L', sample.columns)
self.assertTrue(set(sample.A).issubset({State('A', 1)}))
self.assertTrue(set(sample.J).issubset({State('J', 1)}))
self.assertTrue(set(sample.R).issubset({State('R', 1)}))
self.assertTrue(set(sample.Q).issubset({State('Q', 0), State('Q', 1)}))
self.assertTrue(set(sample.G).issubset({State('G', 0), State('G', 1)}))
self.assertTrue(set(sample.L).issubset({State('L', 0), State('L', 1)}))
def test_likelihood_weighted_sample(self):
sample = self.sampling_inference.likelihood_weighted_sample(
[State('A', 0), State('J', 1),
State('R', 0)], 25)
self.assertEquals(len(sample), 25)
self.assertEquals(len(sample.columns), 7)
self.assertIn('A', sample.columns)
self.assertIn('J', sample.columns)
self.assertIn('R', sample.columns)
self.assertIn('Q', sample.columns)
self.assertIn('G', sample.columns)
self.assertIn('L', sample.columns)
self.assertIn('_weight', sample.columns)
self.assertTrue(set(sample.A).issubset({State('A', 0), State('A', 1)}))
self.assertTrue(set(sample.J).issubset({State('J', 0), State('J', 1)}))
self.assertTrue(set(sample.R).issubset({State('R', 0), State('R', 1)}))
self.assertTrue(set(sample.Q).issubset({State('Q', 0), State('Q', 1)}))
self.assertTrue(set(sample.G).issubset({State('G', 0), State('G', 1)}))
self.assertTrue(set(sample.L).issubset({State('L', 0), State('L', 1)}))
def tearDown(self):
del self.sampling_inference
del self.bayesian_model
del self.markov_model
def test_init(self):
with self.assertRaises(TypeError):
BayesianModelSampling(self.markov_model)
