Added an additional test to check for asymmetrical relationships in Bayesian Networks.

src/main/java/com/syncleus/dann/graphicalmodel/bayesian/AbstractBayesianAdjacencyNetwork.java

+/******************************************************************************
+ *                                                                             *
+ *  Copyright: (c) Syncleus, Inc.                                              *
+ *                                                                             *
+ *  You may redistribute and modify this source code under the terms and       *
+ *  conditions of the Open Source Community License - Type C version 1.0       *
+ *  or any later version as published by Syncleus, Inc. at www.syncleus.com.   *
+ *  There should be a copy of the license included with this file. If a copy   *
+ *  of the license is not included you are granted no right to distribute or   *
+ *  otherwise use this file except through a legal and valid license. You      *
+ *  should also contact Syncleus, Inc. at the information below if you cannot  *
+ *  find a license:                                                            *
+ *                                                                             *
+ *  Syncleus, Inc.                                                             *
+ *  2604 South 12th Street                                                     *
+ *  Philadelphia, PA 19148                                                     *
+ *                                                                             *
+ ******************************************************************************/
+package com.syncleus.dann.graphicalmodel.bayesian;
+
+import java.util.Set;
+import com.syncleus.dann.graph.DirectedEdge;
+import com.syncleus.dann.graph.Graph;
+import com.syncleus.dann.graphicalmodel.AbstractGraphicalModelAdjacencyGraph;
+import com.syncleus.dann.graphicalmodel.GraphicalModelNode;
+
+public abstract class AbstractBayesianAdjacencyNetwork<N extends GraphicalModelNode, E extends DirectedEdge<N>> extends AbstractGraphicalModelAdjacencyGraph<N, E>
+{
+	protected AbstractBayesianAdjacencyNetwork()
+	{
+		super();
+	}
+
+	protected AbstractBayesianAdjacencyNetwork(final Graph<N, E> copyGraph)
+	{
+		super(copyGraph.getNodes(), copyGraph.getEdges());
+	}
+
+	protected AbstractBayesianAdjacencyNetwork(final Set<N> nodes, final Set<E> edges)
+	{
+		super(nodes, edges);
+	}
+
+	@Override
+	public double jointProbability()
+	{
+		double probabilityProduct = 1.0;
+		for(final N node : this.getNodes())
+			probabilityProduct *= node.stateProbability();
+		return probabilityProduct;
+	}
+}

src/main/java/com/syncleus/dann/graphicalmodel/markovrandomfield/AbstractMarkovRandomFieldAdjacencyGraph.java

+/******************************************************************************
+ *                                                                             *
+ *  Copyright: (c) Syncleus, Inc.                                              *
+ *                                                                             *
+ *  You may redistribute and modify this source code under the terms and       *
+ *  conditions of the Open Source Community License - Type C version 1.0       *
+ *  or any later version as published by Syncleus, Inc. at www.syncleus.com.   *
+ *  There should be a copy of the license included with this file. If a copy   *
+ *  of the license is not included you are granted no right to distribute or   *
+ *  otherwise use this file except through a legal and valid license. You      *
+ *  should also contact Syncleus, Inc. at the information below if you cannot  *
+ *  find a license:                                                            *
+ *                                                                             *
+ *  Syncleus, Inc.                                                             *
+ *  2604 South 12th Street                                                     *
+ *  Philadelphia, PA 19148                                                     *
+ *                                                                             *
+ ******************************************************************************/
+package com.syncleus.dann.graphicalmodel.markovrandomfield;
+
+import java.util.Set;
+import com.syncleus.dann.graph.*;
+import com.syncleus.dann.graph.topological.Topography;
+import com.syncleus.dann.graphicalmodel.AbstractGraphicalModelAdjacencyGraph;
+import com.syncleus.dann.graphicalmodel.GraphicalModelNode;
+
+public abstract class AbstractMarkovRandomFieldAdjacencyGraph<N extends GraphicalModelNode, E extends UndirectedEdge<N>> extends AbstractGraphicalModelAdjacencyGraph<N, E>
+{
+	protected AbstractMarkovRandomFieldAdjacencyGraph()
+	{
+		super();
+	}
+
+	protected AbstractMarkovRandomFieldAdjacencyGraph(final Graph<N, E> copyGraph)
+	{
+		super(copyGraph.getNodes(), copyGraph.getEdges());
+	}
+
+	protected AbstractMarkovRandomFieldAdjacencyGraph(final Set<N> nodes, final Set<E> edges)
+	{
+		super(nodes, edges);
+	}
+
+	@Override
+	public double jointProbability()
+	{
+		//First we want to attempt clique factorization
+		//if the graph consists of a single clique, then the joint probability is the probability of any single node.
+		if( Topography.isStronglyConnected(this) )
+			if(!this.getNodes().isEmpty())
+				return this.getNodes().iterator().next().stateProbability();
+			else return 1.0;
+
+		//otherwise the joint probability is the product of all the cliques' probabilities within this graph
+		Set<Graph<N,E>> cliques = Topography.getMaximallyConnectedComponents(this);
+		double probabilityProduct = 1.0;
+		for(final Graph<N,E> clique : cliques)
+		{
+			probabilityProduct *= clique.getNodes().iterator().next().stateProbability();
+		}
+		return probabilityProduct;
+	}
+}
\ No newline at end of file

src/test/java/com/syncleus/dann/graphicalmodel/bayesian/TestSimpleBayesianNetwork.java

@@ -82,4 +82,55 @@ public class TestSimpleBayesianNetwork
 		childNode.setState(SimpleEnum.FALSE);
 		Assert.assertTrue("bad state probability (FALSE,FALSE)!", Math.abs(network.conditionalProbability(goals, influences) - 0.25) < 0.0001);
 	}
+
+	@Test
+	public void testDependentNodeAsymmetrical()
+	{
+		final MutableBayesianAdjacencyNetwork network = new MutableBayesianAdjacencyNetwork();
+		final GraphicalModelNode<SimpleEnum> parentNode = new SimpleGraphicalModelNode<SimpleEnum>(SimpleEnum.TRUE);
+		final GraphicalModelNode<SimpleEnum> childNode = new SimpleGraphicalModelNode<SimpleEnum>(SimpleEnum.TRUE);
+
+		network.add(parentNode);
+		network.add(childNode);
+
+		final DirectedEdge<GraphicalModelNode> testEdge = new ImmutableDirectedEdge<GraphicalModelNode>(parentNode, childNode);
+		network.add(testEdge);
+
+		parentNode.setState(SimpleEnum.TRUE);
+		childNode.setState(SimpleEnum.FALSE);
+		network.learnStates();
+		network.learnStates();
+		network.learnStates();
+		childNode.setState(SimpleEnum.TRUE);
+		network.learnStates();
+
+		parentNode.setState(SimpleEnum.FALSE);
+		childNode.setState(SimpleEnum.TRUE);
+		network.learnStates();
+		network.learnStates();
+		childNode.setState(SimpleEnum.FALSE);
+		network.learnStates();
+		network.learnStates();
+
+		final Set<GraphicalModelNode> goals = new HashSet<GraphicalModelNode>();
+		goals.add(childNode);
+		final Set<GraphicalModelNode> influences = new HashSet<GraphicalModelNode>();
+		influences.add(parentNode);
+
+		parentNode.setState(SimpleEnum.TRUE);
+		childNode.setState(SimpleEnum.TRUE);
+		Assert.assertTrue("bad state probability (TRUE,TRUE)!", Math.abs(network.conditionalProbability(goals, influences) - 0.25) < 0.0001);
+
+		parentNode.setState(SimpleEnum.TRUE);
+		childNode.setState(SimpleEnum.FALSE);
+		Assert.assertTrue("bad state probability (TRUE,FALSE)!", Math.abs(network.conditionalProbability(goals, influences) - 0.75) < 0.0001);
+
+		parentNode.setState(SimpleEnum.FALSE);
+		childNode.setState(SimpleEnum.TRUE);
+		Assert.assertTrue("bad state probability (FALSE,TRUE)!", Math.abs(network.conditionalProbability(goals, influences) - 0.5) < 0.0001);
+
+		parentNode.setState(SimpleEnum.FALSE);
+		childNode.setState(SimpleEnum.FALSE);
+		Assert.assertTrue("bad state probability (FALSE,FALSE)!", Math.abs(network.conditionalProbability(goals, influences) - 0.5) < 0.0001);
+	}
 }