working on factor graph

examples/EarthquakeNet.py

@@ -3,6 +3,7 @@
 from  primo.core import BayesNet
 from  primo.reasoning import DiscreteNode
 from primo.reasoning.factorelemination import EasiestFactorElimination
+from primo.reasoning.factorelemination import FactorTreeFactory
 import numpy
 
 bn = BayesNet()
@@ -16,14 +17,14 @@ baum_calls = DiscreteNode("Baum calls", ["Calling", "Not Calling"])
 bn.add_node(burglary)
 bn.add_node(alarm)
 bn.add_node(earthquake)
-#bn.add_node(john_calls)
-#bn.add_node(baum_calls)
+bn.add_node(john_calls)
+bn.add_node(baum_calls)
 
 
 bn.add_edge(burglary,alarm)
 bn.add_edge(earthquake, alarm)
-#bn.add_edge(alarm, john_calls)
-#bn.add_edge(alarm, baum_calls)
+bn.add_edge(alarm, john_calls)
+bn.add_edge(alarm, baum_calls)
 
 
 cpt_burglary = numpy.array([0.001,0.999])
@@ -64,7 +65,11 @@ fe.set_BayesNet(bn)
 #print "PoE Earthquake: " + str(fe.calculate_PoE([(earthquake, "Calm")]))
 #print "PoE BaumCalls is Calling: " + str(fe.calculate_PoE([(baum_calls, "Calling")]))
 
-print "Posterior of earthquake : " + str(fe.calculate_PosteriorMarginal([burglary],[(alarm, "Ringing"),(earthquake, "Calm")]))
+#print "Posterior of earthquake : " + str(fe.calculate_PosteriorMarginal([burglary],[(alarm, "Ringing"),(earthquake, "Calm")]))
 
+factorTreeFactory = FactorTreeFactory()
+factorTree = factorTreeFactory.create_random_factortree(bn)
+
+#factorTree.draw()
 
 

primo/reasoning/factorelemination/Factor.py

@@ -10,5 +10,8 @@ class Factor(object):
         self.node = node
         self.calPT = node.get_cpd()
         
+    def __str__(self):
+        return self.node.name
         
     
+    

primo/reasoning/factorelemination/FactorEdge.py

@@ -2,3 +2,8 @@
 
 class FactorEdge(object):
     
+    
+        def __init__(self,graph,rootNode):
+            self.graph = graph
+            self.rootNode = rootNode
+    

primo/reasoning/factorelemination/FactorTree.py

@@ -10,6 +10,11 @@ class FactorTree(object):
         self.graph = graph
         self.rootNode = rootNode
         
+    def draw(self):
+        import matplotlib.pyplot as plt
+        nx.draw_circular(self.graph)
+        plt.show()
+            
     
     
     

primo/reasoning/factorelemination/FactorTreeFactory.py

 
+import networkx as nx
+from primo.core import Node
+from primo.reasoning.factorelemination import Factor
+from primo.reasoning.factorelemination import FactorTree
+from random import choice
 
 class FactorTreeFactory(object):
     
+    def create_random_factortree(self,bayesNet):
+        allNodes = bayesNet.get_all_nodes()
+        
+        if len(allNodes) == 0:
+            raise Exception("createRandomFactorTree: No nodes in given bayesNet")
+        
+        tn = allNodes.pop()
+        rootFactor = Factor(tn)
+
+        graph = nx.DiGraph()
+
+        graph.add_node(rootFactor)        
+        
+        usedNodes = [rootFactor]
+        
+        for n in allNodes[:]:
+            parentNode = choice(usedNodes[:])
+            newFactor = Factor(n)
+            graph.add_edge(parentNode,newFactor)
+            usedNodes.append(newFactor)
+            
+            
+        return FactorTree(graph,rootFactor)
+            
+        
+        
+