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import numpy as np
from beliefs.factors.cpd import TabularCPD
class BernoulliOrCPD(TabularCPD):
"""CPD class for a Bernoulli random variable whose relationship to its
parents (also Bernoulli random variables) is described by OR logic.
If at least one of the variable's parents is True, then the variable
is True, and False otherwise.
"""
def __init__(self, variable, parents=[]):
"""
Args:
variable: int or string
parents: optional, list of int and/or strings
"""
super().__init__(variable=variable,
variable_card=2,
parents=parents,
parents_card=[2]*len(parents),
values=[])
self._values = []
@property
def values(self):
if not any(self._values):
self._values = self._build_kwise_values_array(len(self.variables))
self._values = self._values.reshape(self.cardinality)
return self._values
@staticmethod
def _build_kwise_values_array(k):
# special case a completely independent factor, and
# return the uniform prior
if k == 1:
return np.array([0.5, 0.5])
return np.array(
[1.,] + [0.]*(2**(k-1)-1) + [0.,] + [1.]*(2**(k-1)-1)
)
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