refactor msg passing methods to BeliefUpdateNodeModel from BayesianModel

2 files changed, 21 insertions, 7 deletions

diff --git a/beliefs/factors/BernoulliOrCPD.py b/beliefs/factors/BernoulliOrCPD.py
index e4fcbf1..2c6a31e 100644
--- a/beliefs/factors/BernoulliOrCPD.py
+++ b/beliefs/factors/BernoulliOrCPD.py
@@ -10,17 +10,22 @@ class BernoulliOrCPD(TabularCPD):
     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()):
+    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=None)
-        self._values = None
+                         values=[])
+        self._values = []
 
     @property
     def values(self):
-        if self._values is None:
+        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
diff --git a/beliefs/factors/CPD.py b/beliefs/factors/CPD.py
index 8de47b3..a286aaa 100644
--- a/beliefs/factors/CPD.py
+++ b/beliefs/factors/CPD.py
@@ -6,7 +6,7 @@ class TabularCPD:
     Defines the conditional probability table for a discrete variable
     whose parents are also discrete.
 
-    TODO: have this inherit from DiscreteFactor
+    TODO: have this inherit from DiscreteFactor implementing explicit factor methods
     """
     def __init__(self, variable, variable_card,
                  parents=[], parents_card=[], values=[]):
@@ -22,9 +22,11 @@ class TabularCPD:
         self.parents = parents
         self.variables = [variable] + parents
         self.cardinality = [variable_card] + parents_card
+        self._values = np.array(values)
 
-        if values:
-            self.values = np.array(values)
+    @property
+    def values(self):
+        return self._values
 
     def get_values(self):
         """
@@ -34,3 +36,10 @@ class TabularCPD:
             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)