def mixed_cpd_model(edges_five_nodes):
"""
X-shaped 5 node model plus one more node, 'w', with edge from 'w' to 'z'.
'z' is an AND node while all other nodes are OR nodes.
"""
u_node = BernoulliOrNode(label_id='u', children=['x'], parents=[])
v_node = BernoulliOrNode(label_id='v', children=['x'], parents=[])
x_node = BernoulliOrNode(label_id='x',
children=['y', 'z'],
parents=['u', 'v'])
y_node = BernoulliOrNode(label_id='y', children=[], parents=['x'])
z_node = BernoulliAndNode(label_id='z', children=[], parents=['x', 'w'])
w_node = BernoulliOrNode(label_id='w', children=['z'], parents=[])
return BeliefUpdateNodeModel(
nodes_dict={
'u': u_node,
'v': v_node,
'x': x_node,
'y': y_node,
'z': z_node,
'w': w_node
})
def custom_cpd_model():
"""
Y-shaped model, with parents ,'u' and 'v' as Or-nodes, 'x' a node with
cardinality 3 and custom CPD, 'y' a node with cardinality 2 and custom CPD.
"""
custom_cpd_x = TabularCPD(variable='x',
variable_card=3,
parents=['u', 'v'],
parents_card=[2, 2],
values=[[0.2, 0, 0.3, 0.1], [0.4, 1, 0.7, 0.2],
[0.4, 0, 0, 0.7]],
state_names={
'x': ['lo', 'med', 'hi'],
'u': ['False', 'True'],
'v': ['False', 'True']
})
custom_cpd_y = TabularCPD(variable='y',
variable_card=2,
parents=['x'],
parents_card=[3],
values=[[0.3, 0.1, 0], [0.7, 0.9, 1]],
state_names={
'x': ['lo', 'med', 'hi'],
'y': ['False', 'True']
})
u_node = BernoulliOrNode(label_id='u', children=['x'], parents=[])
v_node = BernoulliOrNode(label_id='v', children=['x'], parents=[])
x_node = Node(children=['y'], cpd=custom_cpd_x)
y_node = Node(children=[], cpd=custom_cpd_y)
return BeliefUpdateNodeModel(nodes_dict={
'u': u_node,
'v': v_node,
'x': x_node,
'y': y_node
})
def five_node_and_model(edges_five_nodes):
return BeliefUpdateNodeModel.init_from_edges(edges_five_nodes,
BernoulliAndNode)
def one_node_model():
a_node = BernoulliOrNode(label_id='x', children=[], parents=[])
return BeliefUpdateNodeModel(nodes_dict={'x': a_node})
def many_parents_and_model(many_parents_edges):
return BeliefUpdateNodeModel.init_from_edges(many_parents_edges,
BernoulliAndNode)
def simple_model(simple_edges):
return BeliefUpdateNodeModel.init_from_edges(simple_edges, BernoulliOrNode)