LGS-173 Merge branch 'bernoulli_and_node'v0.0.3

3 files changed, 96 insertions, 5 deletions

diff --git a/beliefs/factors/bernoulli_and_cpd.py b/beliefs/factors/bernoulli_and_cpd.py
new file mode 100644
index 0000000..fdb0c25
--- /dev/null
+++ b/beliefs/factors/bernoulli_and_cpd.py
@@ -0,0 +1,45 @@
+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: optional, list of int and/or strings
+        """
+        super().__init__(variable=variable,
+                         variable_card=2,
+                         parents=parents,
+                         parents_card=[2]*len(parents),
+                         values=[])
+        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.]
+        )
diff --git a/beliefs/factors/bernoulli_or_cpd.py b/beliefs/factors/bernoulli_or_cpd.py
index bfb3a95..12ee2f6 100644
--- a/beliefs/factors/bernoulli_or_cpd.py
+++ b/beliefs/factors/bernoulli_or_cpd.py
@@ -21,11 +21,11 @@ class BernoulliOrCPD(TabularCPD):
                          parents=parents,
                          parents_card=[2]*len(parents),
                          values=[])
-        self._values = []
+        self._values = None
 
     @property
     def values(self):
-        if not any(self._values):
+        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
@@ -37,6 +37,9 @@ class BernoulliOrCPD(TabularCPD):
         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.,] + [0.]*(2**(k-1)-1) + [0.,] + [1.]*(2**(k-1)-1)
         )
diff --git a/beliefs/models/belief_update_node_model.py b/beliefs/models/belief_update_node_model.py
index 667e0f1..1c3ba6e 100644
--- a/beliefs/models/belief_update_node_model.py
+++ b/beliefs/models/belief_update_node_model.py
@@ -8,6 +8,7 @@ import networkx as nx
 
 from beliefs.models.base_models import BayesianModel
 from beliefs.factors.bernoulli_or_cpd import BernoulliOrCPD
+from beliefs.factors.bernoulli_and_cpd import BernoulliAndCPD
 
 
 class InvalidLambdaMsgToParent(Exception):
@@ -212,7 +213,7 @@ class Node:
         raise NotImplementedError
 
     def compute_lambda_agg(self):
-        if not self.children:
+        if len(self.children) == 0:
             return self.lambda_agg
         else:
             lambda_msg_values = self.validate_and_return_msgs_received_for_msg_type(MessageType.LAMBDA)
@@ -289,11 +290,13 @@ class BernoulliOrNode(Node):
                          cpd=BernoulliOrCPD(label_id, parents))
 
     def compute_pi_agg(self):
-        if not self.parents:
+        if len(self.parents) == 0:
             self.pi_agg = self.cpd.values
         else:
             pi_msg_values = self.validate_and_return_msgs_received_for_msg_type(MessageType.PI)
             parents_p0 = [p[0] for p in pi_msg_values]
+            # Parents are Bernoulli variables with pi_msg_values (surrogate prior probabilities)
+            # of p = [P(False), P(True)]
             p_0 = reduce(lambda x, y: x*y, parents_p0)
             p_1 = 1 - p_0
             self.pi_agg = np.array([p_0, p_1])
@@ -305,7 +308,7 @@ class BernoulliOrNode(Node):
         else:
             # TODO: cleanup this validation
             _ = self.validate_and_return_msgs_received_for_msg_type(MessageType.PI)
-            p0_excluding_k = [msg[0] for par_id, msg in self.pi_received_msgs.items() if par_id != parent_k]
+            p0_excluding_k = [p[0] for par_id, p in self.pi_received_msgs.items() if par_id != parent_k]
             p0_product = reduce(lambda x, y: x*y, p0_excluding_k, 1)
             lambda_0 = self.lambda_agg[1] + (self.lambda_agg[0] - self.lambda_agg[1])*p0_product
             lambda_1 = self.lambda_agg[1]
@@ -313,3 +316,43 @@ class BernoulliOrNode(Node):
             if not any(lambda_msg):
                 raise InvalidLambdaMsgToParent
             return self._normalize(lambda_msg)
+
+
+class BernoulliAndNode(Node):
+    def __init__(self,
+                 label_id,
+                 children,
+                 parents):
+        super().__init__(label_id=label_id,
+                         children=children,
+                         parents=parents,
+                         cardinality=2,
+                         cpd=BernoulliAndCPD(label_id, parents))
+
+    def compute_pi_agg(self):
+        if len(self.parents) == 0:
+            self.pi_agg = self.cpd.values
+        else:
+            pi_msg_values = self.validate_and_return_msgs_received_for_msg_type(MessageType.PI)
+            parents_p1 = [p[1] for p in pi_msg_values]
+            # Parents are Bernoulli variables with pi_msg_values (surrogate prior probabilities)
+            # of p = [P(False), P(True)]
+            p_1 = reduce(lambda x, y: x*y, parents_p1)
+            p_0 = 1 - p_1
+            self.pi_agg = np.array([p_0, p_1])
+        return self.pi_agg
+
+    def compute_lambda_msg_to_parent(self, parent_k):
+        if np.array_equal(self.lambda_agg, np.ones([self.cardinality])):
+            return np.ones([self.cardinality])
+        else:
+            # TODO: cleanup this validation
+            _ = self.validate_and_return_msgs_received_for_msg_type(MessageType.PI)
+            p1_excluding_k = [p[1] for par_id, p in self.pi_received_msgs.items() if par_id != parent_k]
+            p1_product = reduce(lambda x, y: x*y, p1_excluding_k, 1)
+            lambda_0 = self.lambda_agg[0]
+            lambda_1 = self.lambda_agg[0] + (self.lambda_agg[1] - self.lambda_agg[0])*p1_product
+            lambda_msg = np.array([lambda_0, lambda_1])
+            if not any(lambda_msg):
+                raise InvalidLambdaMsgToParent
+            return self._normalize(lambda_msg)