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import numpy as np
from functools import reduce
from beliefs.models.beliefupdate.Node import (
Node,
MessageType,
InvalidLambdaMsgToParent
)
from beliefs.factors.BernoulliOrCPD import BernoulliOrCPD
class BernoulliOrNode(Node):
def __init__(self,
label_id,
children,
parents):
super().__init__(label_id=label_id,
children=children,
parents=parents,
cardinality=2,
cpd=BernoulliOrCPD(label_id, parents))
def compute_pi_agg(self):
if not self.parents:
self.pi_agg = self.cpd.values
else:
pi_msg_values = self.validate_and_return_msgs_received_for_msg_type(MessageType.PI)
parents_p0 = [p[0] for p in pi_msg_values]
p_0 = reduce(lambda x, y: x*y, parents_p0)
p_1 = 1 - p_0
self.pi_agg = np.array([p_0, p_1])
return self.pi_agg
def compute_lambda_msg_to_parent(self, parent_k):
if np.array_equal(self.lambda_agg, np.ones([self.cardinality])):
return np.ones([self.cardinality])
else:
# TODO: cleanup this validation
_ = self.validate_and_return_msgs_received_for_msg_type(MessageType.PI)
p0_excluding_k = [msg[0] for par_id, msg in self.pi_received_msgs.items() if par_id != parent_k]
p0_product = reduce(lambda x, y: x*y, p0_excluding_k, 1)
lambda_0 = self.lambda_agg[1] + (self.lambda_agg[0] - self.lambda_agg[1])*p0_product
lambda_1 = self.lambda_agg[1]
lambda_msg = np.array([lambda_0, lambda_1])
if not any(lambda_msg):
raise InvalidLambdaMsgToParent
return self._normalize(lambda_msg)
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