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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: list,
(optional) list of int and/or strings
"""
super().__init__(variable=variable,
variable_card=2,
parents=parents,
parents_card=[2]*len(parents),
values=None,
state_names={var: ['False', 'True'] for var in [variable] + parents})
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.]
)
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