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diff --git a/beliefs/factors/bernoulli_and_cpd.py b/beliefs/factors/bernoulli_and_cpd.py
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+import numpy as np
+
+from beliefs.factors.cpd import TabularCPD
+
+
+class BernoulliAndCPD(TabularCPD):
+ """CPD class for a Bernoulli random variable whose relationship to its
+ parents (also Bernoulli random variables) is described by AND logic.
+
+ If all of the variable's parents are 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 = None
+
+ @property
+ def values(self):
+ if self._values is None:
+ 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])
+
+ # values are stored as a row vector using an ordering such that
+ # the right-most variables as defined in [variable].extend(parents)
+ # cycle through their values the fastest.
+ return np.array(
+ [1.]*(2**(k-1)-1) + [0.] + [0.,]*(2**(k-1)-1) + [1.]
+ )