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import numpy as np
from beliefs.factors.discrete_factor import DiscreteFactor
class TabularCPD(DiscreteFactor):
"""
Defines the conditional probability table for a discrete variable
whose parents are also discrete.
"""
def __init__(self, variable, variable_card,
parents=[], parents_card=[], values=[], state_names=None):
"""
Args:
variable: int or string
variable_card: int
parents: optional, list of int and/or strings
parents_card: optional, list of int
values: optional, 2d list or array
state_names: dictionary (optional),
mapping variables to their states, of format {label_name: ['state1', 'state2']}
"""
super().__init__(variables=[variable] + parents,
cardinality=[variable_card] + parents_card,
values=values,
state_names=state_names)
self.variable = variable
self.parents = parents
def get_values(self):
"""
Returns the tabular cpd form of the values.
"""
if len(self.cardinality) == 1:
return self.values.reshape(1, np.prod(self.cardinality))
else:
return self.values.reshape(self.cardinality[0], np.prod(self.cardinality[1:]))
def copy(self):
return self.__class__(self.variable,
self.cardinality[0],
self.parents,
self.cardinality[1:],
self._values)
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