diff options
Diffstat (limited to 'beliefs/factors')
-rw-r--r-- | beliefs/factors/BernoulliOrCPD.py | 37 | ||||
-rw-r--r-- | beliefs/factors/BernoulliOrFactor.py | 42 | ||||
-rw-r--r-- | beliefs/factors/CPD.py | 36 |
3 files changed, 73 insertions, 42 deletions
diff --git a/beliefs/factors/BernoulliOrCPD.py b/beliefs/factors/BernoulliOrCPD.py new file mode 100644 index 0000000..e4fcbf1 --- /dev/null +++ b/beliefs/factors/BernoulliOrCPD.py @@ -0,0 +1,37 @@ +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=set()): + super().__init__(variable=variable, + variable_card=2, + parents=parents, + parents_card=[2]*len(parents), + values=None) + 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]) + + return np.array( + [1.,] + [0.]*(2**(k-1)-1) + [0.,] + [1.]*(2**(k-1)-1) + ) diff --git a/beliefs/factors/BernoulliOrFactor.py b/beliefs/factors/BernoulliOrFactor.py deleted file mode 100644 index 4f973ae..0000000 --- a/beliefs/factors/BernoulliOrFactor.py +++ /dev/null @@ -1,42 +0,0 @@ -import numpy as np - - -class BernoulliOrFactor: - """CPD class for a Bernoulli random variable whose relationship to its - parents is described by OR logic. - - If at least one of a child's parents is True, then the child is True, and - False otherwise.""" - def __init__(self, child, parents=set()): - self.child = child - self.parents = set(parents) - self.variables = set([child] + list(parents)) - self.cardinality = [2]*len(self.variables) - 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 - - 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:])) - - @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) - ) diff --git a/beliefs/factors/CPD.py b/beliefs/factors/CPD.py new file mode 100644 index 0000000..8de47b3 --- /dev/null +++ b/beliefs/factors/CPD.py @@ -0,0 +1,36 @@ +import numpy as np + + +class TabularCPD: + """ + Defines the conditional probability table for a discrete variable + whose parents are also discrete. + + TODO: have this inherit from DiscreteFactor + """ + def __init__(self, variable, variable_card, + parents=[], parents_card=[], values=[]): + """ + 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 + """ + self.variable = variable + self.parents = parents + self.variables = [variable] + parents + self.cardinality = [variable_card] + parents_card + + if values: + self.values = np.array(values) + + 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:])) |