class TestDynamicBayesianNetworkMethods2(unittest.TestCase):
def setUp(self):
self.G = DynamicBayesianNetwork()
self.G.add_edges_from(
[(('D', 0), ('G', 0)), (('I', 0), ('G', 0)),
(('D', 0), ('D', 1)), (('I', 0), ('I', 1))])
"""
G.edges()
[(('I', 0), ('G', 0)), (('I', 0), ('I', 1)),
(('D', 1), ('G', 1)), (('D', 0), ('G', 0)),
(('D', 0), ('D', 1)), (('I', 1), ('G', 1))]
"""
def test_check_model(self):
grade_cpd = TabularCPD(('G', 0), 3, [[0.3, 0.05, 0.7, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.2, 0.2]], [('D', 0), ('I', 0)], [2, 2])
d_i_cpd = TabularCPD(('D', 1), 2, [[0.6, 0.3], [0.4, 0.7]], [('D', 0)], 2)
diff_cpd = TabularCPD(('D', 0), 2, [[0.6, 0.4]])
intel_cpd = TabularCPD(('I', 0), 2, [[0.7, 0.3]])
i_i_cpd = TabularCPD(('I', 1), 2, [[0.5, 0.4], [0.5, 0.6]], [('I', 0)], 2)
grade_1_cpd = TabularCPD(('G', 1), 3, [[0.3, 0.05, 0.8, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]], [('D', 1), ('I', 1)], [2, 2])
self.G.add_cpds(grade_cpd, d_i_cpd, i_i_cpd)
self.assertTrue(self.G.check_model())
self.G.remove_cpds(grade_cpd, d_i_cpd, i_i_cpd)
self.G.add_cpds(grade_1_cpd, diff_cpd, intel_cpd)
self.assertTrue(self.G.check_model())
def test_check_model1(self):
diff_cpd = TabularCPD(('D', 0), 3, [[0.3, 0.05, 0.7, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.2, 0.2]], [('G', 0), ('I', 0)], [2, 2])
self.G.add_cpds(diff_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(diff_cpd)
grade_cpd = TabularCPD(('G', 0), 2, [[0.6, 0.3], [0.4, 0.7]], [('D', 0)], 2)
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
diff_cpd = TabularCPD(('D', 0), 2, [[0.6, 0.3], [0.4, 0.7]], [('D', 1)], 2)
self.G.add_cpds(diff_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(diff_cpd)
grade_cpd = TabularCPD(('G', 0), 3, [[0.3, 0.05, 0.8, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]], [('D', 1), ('I', 1)], [2, 2])
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
grade_cpd = TabularCPD(('G', 1), 3, [[0.3, 0.05, 0.8, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]], [('D', 0), ('I', 0)], [2, 2])
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
grade_cpd = TabularCPD(('G', 0), 2, [[0.6, 0.3], [0.4, 0.7]], [('D', 1)], 2)
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
def test_check_model2(self):
grade_cpd = TabularCPD(('G', 0), 3, [[0.9, 0.05, 0.7, 0.5],
[0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.2, 0.2]], [('D', 0), ('I', 0)], [2, 2])
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
d_i_cpd = TabularCPD(('D', 1), 2, [[0.1, 0.3], [0.4, 0.7]], [('D', 0)], 2)
self.G.add_cpds(d_i_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(d_i_cpd)
diff_cpd = TabularCPD(('D', 0), 2, [[0.7, 0.4]])
self.G.add_cpds(diff_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(diff_cpd)
intel_cpd = TabularCPD(('I', 0), 2, [[1.7, 0.3]])
self.G.add_cpds(intel_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(intel_cpd)
i_i_cpd = TabularCPD(('I', 1), 2, [[0.9, 0.4], [0.5, 0.6]], [('I', 0)], 2)
self.G.add_cpds(i_i_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(i_i_cpd)
grade_1_cpd = TabularCPD(('G', 1), 3, [[0.3, 0.05, 0.8, 0.5],
[0.4, 0.5, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]], [('D', 1), ('I', 1)], [2, 2])
self.G.add_cpds(grade_1_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_1_cpd)
def tearDown(self):
del self.G
0.6, 0.1, 0.0, 0.1
],
[
0.2, 0.1, 0.1, 0.3, 0.2, 0.2, 0.3, 0.1, 0.5, 0.4, 0.1,
0.3, 0.4, 0.4, 0.4, 0.2, 0.1, 0.1
],
[
0.1, 0.8, 0.0, 0.1, 0.8, 0.0, 0.6, 0.9, 0.2, 0.1, 0.8,
0.0, 0.3, 0.6, 0.0, 0.7, 0.9, 0.8
]], [('A', 0), ('I', 0), ('R', 0)], [2, 3, 3])
dbn.add_cpds(cpd_I, cpd_A, cpd_I1, cpd_O, cpd_R)
dbn.initialize_initial_state()
print "Model created successfully: ", dbn.check_model()
# CREATES SIMULATED DATA FROM DBN MODEL
samples = list()
for i in range(1, 10000):
top_order = list(nx.topological_sort(dbn))
sample = dict()
for node in top_order:
curr_cpd = dbn.get_cpds(node)
evidence = curr_cpd.get_evidence()
ev_index = 0
if len(evidence) != 0:
for var in evidence:
curr_cpd = curr_cpd.reduce([(var, sample[var])], inplace=False)
class TestDynamicBayesianNetworkMethods2(unittest.TestCase):
def setUp(self):
self.G = DynamicBayesianNetwork()
self.G.add_edges_from([
(("D", 0), ("G", 0)),
(("I", 0), ("G", 0)),
(("D", 0), ("D", 1)),
(("I", 0), ("I", 1)),
])
"""
G.edges()
[(('I', 0), ('G', 0)), (('I', 0), ('I', 1)),
(('D', 1), ('G', 1)), (('D', 0), ('G', 0)),
(('D', 0), ('D', 1)), (('I', 1), ('G', 1))]
"""
def test_check_model(self):
grade_cpd = TabularCPD(
("G", 0),
3,
values=[[0.3, 0.05, 0.7, 0.5], [0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.2, 0.2]],
evidence=[("D", 0), ("I", 0)],
evidence_card=[2, 2],
)
d_i_cpd = TabularCPD(
("D", 1),
2,
values=[[0.6, 0.3], [0.4, 0.7]],
evidence=[("D", 0)],
evidence_card=[2],
)
diff_cpd = TabularCPD(("D", 0), 2, values=[[0.6, 0.4]])
intel_cpd = TabularCPD(("I", 0), 2, values=[[0.7, 0.3]])
i_i_cpd = TabularCPD(
("I", 1),
2,
values=[[0.5, 0.4], [0.5, 0.6]],
evidence=[("I", 0)],
evidence_card=[2],
)
grade_1_cpd = TabularCPD(
("G", 1),
3,
values=[[0.3, 0.05, 0.8, 0.5], [0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]],
evidence=[("D", 1), ("I", 1)],
evidence_card=[2, 2],
)
self.G.add_cpds(grade_cpd, d_i_cpd, i_i_cpd)
self.assertTrue(self.G.check_model())
self.G.remove_cpds(grade_cpd, d_i_cpd, i_i_cpd)
self.G.add_cpds(grade_1_cpd, diff_cpd, intel_cpd)
self.assertTrue(self.G.check_model())
def test_check_model1(self):
diff_cpd = TabularCPD(
("D", 0),
3,
values=[[0.3, 0.05, 0.7, 0.5], [0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.2, 0.2]],
evidence=[("G", 0), ("I", 0)],
evidence_card=[2, 2],
)
self.G.add_cpds(diff_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(diff_cpd)
grade_cpd = TabularCPD(
("G", 0),
2,
values=[[0.6, 0.3], [0.4, 0.7]],
evidence=[("D", 0)],
evidence_card=[2],
)
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
diff_cpd = TabularCPD(
("D", 0),
2,
values=[[0.6, 0.3], [0.4, 0.7]],
evidence=[("D", 1)],
evidence_card=[2],
)
self.G.add_cpds(diff_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(diff_cpd)
grade_cpd = TabularCPD(
("G", 0),
3,
values=[[0.3, 0.05, 0.8, 0.5], [0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]],
evidence=[("D", 1), ("I", 1)],
evidence_card=[2, 2],
)
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
grade_cpd = TabularCPD(
("G", 1),
3,
values=[[0.3, 0.05, 0.8, 0.5], [0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]],
evidence=[("D", 0), ("I", 0)],
evidence_card=[2, 2],
)
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
grade_cpd = TabularCPD(
("G", 0),
2,
values=[[0.6, 0.3], [0.4, 0.7]],
evidence=[("D", 1)],
evidence_card=[2],
)
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
def test_check_model2(self):
grade_cpd = TabularCPD(
("G", 0),
3,
values=[[0.9, 0.05, 0.7, 0.5], [0.4, 0.25, 0.1, 0.3],
[0.3, 0.7, 0.2, 0.2]],
evidence=[("D", 0), ("I", 0)],
evidence_card=[2, 2],
)
self.G.add_cpds(grade_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_cpd)
d_i_cpd = TabularCPD(
("D", 1),
2,
values=[[0.1, 0.3], [0.4, 0.7]],
evidence=[("D", 0)],
evidence_card=[2],
)
self.G.add_cpds(d_i_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(d_i_cpd)
diff_cpd = TabularCPD(("D", 0), 2, values=[[0.7, 0.4]])
self.G.add_cpds(diff_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(diff_cpd)
intel_cpd = TabularCPD(("I", 0), 2, values=[[1.7, 0.3]])
self.G.add_cpds(intel_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(intel_cpd)
i_i_cpd = TabularCPD(
("I", 1),
2,
values=[[0.9, 0.4], [0.5, 0.6]],
evidence=[("I", 0)],
evidence_card=[2],
)
self.G.add_cpds(i_i_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(i_i_cpd)
grade_1_cpd = TabularCPD(
("G", 1),
3,
values=[[0.3, 0.05, 0.8, 0.5], [0.4, 0.5, 0.1, 0.3],
[0.3, 0.7, 0.1, 0.2]],
evidence=[("D", 1), ("I", 1)],
evidence_card=[2, 2],
)
self.G.add_cpds(grade_1_cpd)
self.assertRaises(ValueError, self.G.check_model)
self.G.remove_cpds(grade_1_cpd)
def tearDown(self):
del self.G