Easy Factorization done

examples/EarthquakeNet.py

@@ -16,20 +16,20 @@ 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([0.002,0.998])
+cpt_burglary = numpy.array([0.001,0.999])
 burglary.set_probability_table(cpt_burglary,[burglary])
 
-cpt_earthquake = numpy.array([0.001,0.999])
+cpt_earthquake = numpy.array([0.002,0.998])
 earthquake.set_probability_table(cpt_earthquake,[earthquake])
 
 alarm.set_probability(0.95,[(alarm,"Ringing"),(burglary,"Intruder"),(earthquake,"Shaking")])
@@ -52,7 +52,6 @@ john_calls.set_probability(0.01,[(alarm,"Silent"),(john_calls,"Calling")])
 john_calls.set_probability(0.99,[(alarm,"Silent"),(john_calls,"Not Calling")])
 
 
-
 #first Elimination:
 fe = EasiestFactorElimination()
 fe.set_BayesNet(bn)
@@ -65,3 +64,7 @@ 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")]))
+
+
+

primo/reasoning/density/ProbabilityTable.py

@@ -82,7 +82,7 @@ class ProbabilityTable(Density):
 
     def normalize_as_jpt(self):
         '''This method normalizes this ProbabilityTable so it represents a valid joint probability table'''
-        self.table=self.table*1.0/numpy.sum(self.table)
+        return self.table*1.0/numpy.sum(self.table)
 
     def multiplication(self, inputFactor):
         '''This method returns a unified ProbabilityTable which contains the variables of both; the inputFactor

primo/reasoning/factorelemination/EasiestFactorElimination.py

@@ -37,26 +37,41 @@ class EasiestFactorElimination(object):
         # Dann PoE berechnen und damit normalisieren
         nodes = self.bn.get_all_nodes()
         
-        unzipped_list = zip(*evidence)
+        ev_list = zip(*evidence)
+              
               
+        # Special Case: First Node
         node1 = nodes.pop()
-        if node1 in unzipped_list[0]:
-            ind = unzipped_list[0].index(node1)
+        if node1 in ev_list[0]:
+            ind = ev_list[0].index(node1)
             finCpd = node1.get_cpd().set_evidence(evidence[ind])
             
         else:
             finCpd = node1.get_cpd()
-                  
+            
+            
+        # For all other nodes
         for n in nodes:
-            if n in unzipped_list[0]:
-                ind = unzipped_list[0].index(n)
+            if n in ev_list[0]:
+                ind = ev_list[0].index(n)
                 nCPD = n.get_cpd().set_evidence(evidence[ind])
-                finCpd = finCpd.multiplication(nCPD)
+                finCpd = finCpd.multiplication(nCPD)            
             else:
                 finCpd = finCpd.multiplication(n.get_cpd())
-          
+
+                
         for v in finCpd.get_variables()[:]:
-            finCpd = finCpd.marginalization(v)
+            if v not in variables:
+                finCpd = finCpd.marginalization(v)
+                
+        finCpd = finCpd.normalize_as_jpt()
+        
+        #unityMarg = finCpd
+        
+        #for v in finCpd.get_variables()[:]:
+        #    finCpd = finCpd.marginalization(v)
+            
+        #unityMarg /= finCpd
             
         return finCpd