def setUp(self):
reader = XMLBIFReader(string=TEST_FILE)
self.expected_model = reader.get_model()
self.writer = XMLBIFWriter(self.expected_model)
self.model_stateless = BayesianModel([('D', 'G'), ('I', 'G'), ('G', 'L'), ('I', 'S')])
self.cpd_d = TabularCPD(variable='D', variable_card=2, values=[[0.6, 0.4]])
self.cpd_i = TabularCPD(variable='I', variable_card=2, values=[[0.7, 0.3]])
self.cpd_g = TabularCPD(variable='G', variable_card=3,
values=[[0.3, 0.05, 0.9, 0.5],
[0.4, 0.25, 0.08, 0.3],
[0.3, 0.7, 0.02, 0.2]],
evidence=['I', 'D'],
evidence_card=[2, 2])
self.cpd_l = TabularCPD(variable='L', variable_card=2,
values=[[0.1, 0.4, 0.99],
[0.9, 0.6, 0.01]],
evidence=['G'],
evidence_card=[3])
self.cpd_s = TabularCPD(variable='S', variable_card=2,
values=[[0.95, 0.2],
[0.05, 0.8]],
evidence=['I'],
evidence_card=[2])
self.model_stateless.add_cpds(self.cpd_d, self.cpd_i, self.cpd_g, self.cpd_l, self.cpd_s)
self.writer_stateless = XMLBIFWriter(self.model_stateless)
def setUp(self):
reader = XMLBIFReader(string=TEST_FILE)
self.expected_model = reader.get_model()
self.writer = XMLBIFWriter(self.expected_model)
self.model_stateless = BayesianModel([("D", "G"), ("I", "G"),
("G", "L"), ("I", "S")])
self.cpd_d = TabularCPD(variable="D",
variable_card=2,
values=[[0.6, 0.4]])
self.cpd_i = TabularCPD(variable="I",
variable_card=2,
values=[[0.7, 0.3]])
self.cpd_g = TabularCPD(
variable="G",
variable_card=3,
values=[
[0.3, 0.05, 0.9, 0.5],
[0.4, 0.25, 0.08, 0.3],
[0.3, 0.7, 0.02, 0.2],
],
evidence=["I", "D"],
evidence_card=[2, 2],
)
self.cpd_l = TabularCPD(
variable="L",
variable_card=2,
values=[[0.1, 0.4, 0.99], [0.9, 0.6, 0.01]],
evidence=["G"],
evidence_card=[3],
)
self.cpd_s = TabularCPD(
variable="S",
variable_card=2,
values=[[0.95, 0.2], [0.05, 0.8]],
evidence=["I"],
evidence_card=[2],
)
self.model_stateless.add_cpds(self.cpd_d, self.cpd_i, self.cpd_g,
self.cpd_l, self.cpd_s)
self.writer_stateless = XMLBIFWriter(self.model_stateless)
def setUp(self):
edges = [['family-out', 'dog-out'], ['bowel-problem', 'dog-out'],
['family-out', 'light-on'], ['dog-out', 'hear-bark']]
cpds = {
'bowel-problem': np.array([[0.01], [0.99]]),
'dog-out': np.array([[0.99, 0.01, 0.97, 0.03],
[0.9, 0.1, 0.3, 0.7]]),
'family-out': np.array([[0.15], [0.85]]),
'hear-bark': np.array([[0.7, 0.3], [0.01, 0.99]]),
'light-on': np.array([[0.6, 0.4], [0.05, 0.95]])
}
states = {
'bowel-problem': ['true', 'false'],
'dog-out': ['true', 'false'],
'family-out': ['true', 'false'],
'hear-bark': ['true', 'false'],
'light-on': ['true', 'false']
}
parents = {
'bowel-problem': [],
'dog-out': ['family-out', 'bowel-problem'],
'family-out': [],
'hear-bark': ['dog-out'],
'light-on': ['family-out']
}
properties = {
'bowel-problem': ['position = (190, 69)'],
'dog-out': ['position = (155, 165)'],
'family-out': ['position = (112, 69)'],
'hear-bark': ['position = (154, 241)'],
'light-on': ['position = (73, 165)']
}
self.model = BayesianModel(edges)
tabular_cpds = []
for var, values in cpds.items():
cpd = TabularCPD(var,
len(states[var]),
values,
evidence=parents[var],
evidence_card=[
len(states[evidence_var])
for evidence_var in parents[var]
])
tabular_cpds.append(cpd)
self.model.add_cpds(*tabular_cpds)
for node, properties in properties.items():
for prop in properties:
prop_name, prop_value = map(lambda t: t.strip(),
prop.split('='))
self.model.node[node][prop_name] = prop_value
self.writer = XMLBIFWriter(model=self.model)
