3 files changed, 87 insertions, 0 deletions
diff --git a/beliefs/factors/__init__.py b/beliefs/factors/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/beliefs/factors/__init__.py
diff --git a/beliefs/factors/bernoulli_or_cpd.py b/beliefs/factors/bernoulli_or_cpd.py
new file mode 100644
index 0000000..bfb3a95
--- /dev/null
+++ b/beliefs/factors/bernoulli_or_cpd.py
@@ -0,0 +1,42 @@
+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=[]):
+        """
+        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 = []
+
+    @property
+    def values(self):
+        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
+
+    @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..a286aaa
--- /dev/null
+++ b/beliefs/factors/cpd.py
@@ -0,0 +1,45 @@
+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 implementing explicit factor methods
+    """
+    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
+        self._values = np.array(values)
+
+    @property
+    def values(self):
+        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:]))
+
+    def copy(self):
+        return self.__class__(self.variable,
+                              self.cardinality[0],
+                              self.parents,
+                              self.cardinality[1:],
+                              self._values)