/*
* Copyright 2015 Red Hat, Inc. and/or its affiliates.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.drools.beliefs.bayes.example;
import org.drools.beliefs.bayes.BayesInstance;
import org.drools.beliefs.bayes.BayesVariable;
import org.drools.beliefs.bayes.BayesLikelyhood;
import org.drools.beliefs.bayes.BayesNetwork;
import org.drools.beliefs.bayes.BayesVariableState;
import org.drools.beliefs.bayes.JunctionTree;
import org.drools.beliefs.bayes.JunctionTreeBuilder;
import org.drools.beliefs.bayes.JunctionTreeClique;
import org.drools.beliefs.graph.Graph;
import org.drools.beliefs.graph.GraphNode;
import org.junit.Before;
import org.junit.Test;
import static org.drools.beliefs.bayes.JunctionTreeTest.assertArray;
import static org.drools.beliefs.bayes.JunctionTreeTest.scaleDouble;
import static org.drools.beliefs.bayes.example.SprinkerTest.connectParentToChildren;
public class EarthQuakeTest {
Graph<BayesVariable> graph = new BayesNetwork();
GraphNode<BayesVariable> burglaryNode = graph.addNode();
GraphNode<BayesVariable> earthquakeNode = graph.addNode();
GraphNode<BayesVariable> alarmNode = graph.addNode();
GraphNode<BayesVariable> johnCallsNode = graph.addNode();
GraphNode<BayesVariable> maryCallsNode = graph.addNode();
BayesVariable burglary = new BayesVariable<String>("Burglary", burglaryNode.getId(), new String[]{"false", "true"}, new double[][]{{0.001, 0.999}});
BayesVariable earthquake = new BayesVariable<String>("Earthquake", earthquakeNode.getId(), new String[]{"false", "true"}, new double[][]{{0.002, 0.998}});
BayesVariable alarm = new BayesVariable<String>("Alarm", alarmNode.getId(), new String[]{"false", "true"}, new double[][]{{0.95, 0.05}, {0.94, 0.06}, {0.29, 0.71}, {0.001, 0.999}});
BayesVariable johnCalls = new BayesVariable<String>("JohnCalls", johnCallsNode.getId(), new String[]{"false", "true"}, new double[][]{{0.90, 0.1}, {0.05, 0.95}});
BayesVariable maryCalls = new BayesVariable<String>("MaryCalls", maryCallsNode.getId(), new String[]{"false", "true"}, new double[][]{{0.7, 0.3}, {0.01, 0.99}});
BayesVariableState burglaryState;
BayesVariableState earthquakeState;
BayesVariableState alarmState;
BayesVariableState johnCallsState;
BayesVariableState maryCallsState;
JunctionTreeClique jtNode1;
JunctionTreeClique jtNode2;
JunctionTreeClique jtNode3;
JunctionTree jTree;
BayesInstance bayesInstance;
@Before
public void setUp() {
connectParentToChildren(burglaryNode, alarmNode);
connectParentToChildren(earthquakeNode, alarmNode);
connectParentToChildren(alarmNode, johnCallsNode, maryCallsNode);
burglaryNode.setContent(burglary);
earthquakeNode.setContent(earthquake);
alarmNode.setContent(alarm);
johnCallsNode.setContent(johnCalls);
maryCallsNode.setContent(maryCalls);
JunctionTreeBuilder jtBuilder = new JunctionTreeBuilder(graph);
jTree = jtBuilder.build();
//jTree.initialize();
jtNode1 = jTree.getRoot();
jtNode2 = jtNode1.getChildren().get(0).getChild();
jtNode3 = jtNode1.getChildren().get(1).getChild();
bayesInstance = new BayesInstance(jTree);
burglaryState = bayesInstance.getVarStates()[burglary.getId()];
earthquakeState = bayesInstance.getVarStates()[earthquake.getId()];
alarmState = bayesInstance.getVarStates()[alarm.getId()];
johnCallsState = bayesInstance.getVarStates()[johnCalls.getId()];
maryCallsState = bayesInstance.getVarStates()[maryCalls.getId()];
}
@Test
public void testInitialize() {
// johnCalls
assertArray(new double[]{0.90, 0.1, 0.05, 0.95}, scaleDouble( 3, jtNode1.getPotentials() ));
// maryCalls
assertArray( new double[]{ 0.7, 0.3, 0.01, 0.99 }, scaleDouble( 3, jtNode2.getPotentials() ));
// burglary, earthquake, alarm
assertArray( new double[]{0.0000019, 0.0000001, 0.0009381, 0.0000599, 0.0005794, 0.0014186, 0.0009970, 0.9960050 },
scaleDouble( 7, jtNode3.getPotentials() ));
}
@Test
public void testNoEvidence() {
bayesInstance.globalUpdate();
assertArray( new double[]{0.052139, 0.947861}, scaleDouble(6, bayesInstance.marginalize("JohnCalls").getDistribution()) );
assertArray( new double[]{0.011736, 0.988264 }, scaleDouble( 6, bayesInstance.marginalize("MaryCalls").getDistribution() ) );
assertArray( new double[]{0.001, 0.999}, scaleDouble(3, bayesInstance.marginalize("Burglary").getDistribution()) );
assertArray( new double[]{ 0.002, 0.998}, scaleDouble( 3, bayesInstance.marginalize("Earthquake").getDistribution() ) );
assertArray( new double[]{0.002516, 0.997484}, scaleDouble(6, bayesInstance.marginalize("Alarm").getDistribution()) );
}
@Test
public void testAlarmEvidence() {
BayesInstance bayesInstance = new BayesInstance(jTree);
bayesInstance.setLikelyhood( "Alarm", new double[]{1.0, 0.0} );
bayesInstance.globalUpdate();
assertArray( new double[]{0.9, 0.1}, scaleDouble(3, bayesInstance.marginalize("JohnCalls").getDistribution()) );
assertArray( new double[]{0.7, 0.3 }, scaleDouble( 3, bayesInstance.marginalize("MaryCalls").getDistribution() ) );
assertArray( new double[]{0.374, 0.626}, scaleDouble(3, bayesInstance.marginalize("Burglary").getDistribution()) );
assertArray( new double[]{ 0.231, 0.769}, scaleDouble( 3, bayesInstance.marginalize("Earthquake").getDistribution() ) );
assertArray( new double[]{1.0, 0.0}, scaleDouble(3, bayesInstance.marginalize("Alarm").getDistribution()) ); }
@Test
public void testEathQuakeEvidence() {
BayesInstance bayesInstance = new BayesInstance(jTree);
bayesInstance.setLikelyhood("Earthquake", new double[]{1.0, 0.0});
bayesInstance.globalUpdate();
assertArray( new double[]{0.297, 0.703}, scaleDouble(3, bayesInstance.marginalize("JohnCalls").getDistribution()) );
assertArray( new double[]{0.211, 0.789 }, scaleDouble( 3, bayesInstance.marginalize("MaryCalls").getDistribution() ) );
assertArray( new double[]{.001, 0.999}, scaleDouble(3, bayesInstance.marginalize("Burglary").getDistribution()) );
assertArray( new double[]{1.0, 0.0}, scaleDouble( 3, bayesInstance.marginalize("Earthquake").getDistribution() ) );
assertArray( new double[]{0.291, 0.709}, scaleDouble(3, bayesInstance.marginalize("Alarm").getDistribution()) );
}
@Test
public void testJoinCallsEvidence() {
BayesInstance bayesInstance = new BayesInstance(jTree);
bayesInstance.setLikelyhood("JohnCalls", new double[]{1.0, 0.0});
bayesInstance.globalUpdate();
assertArray( new double[]{1.0, 0.0}, scaleDouble(3, bayesInstance.marginalize("JohnCalls").getDistribution()) );
assertArray( new double[]{0.04, 0.96 }, scaleDouble( 3, bayesInstance.marginalize("MaryCalls").getDistribution() ) );
assertArray( new double[]{0.016, 0.984}, scaleDouble(3, bayesInstance.marginalize("Burglary").getDistribution()) );
assertArray( new double[]{0.011, 0.989}, scaleDouble( 3, bayesInstance.marginalize("Earthquake").getDistribution() ) );
assertArray( new double[]{0.043, 0.957}, scaleDouble(3, bayesInstance.marginalize("Alarm").getDistribution()) );
}
@Test
public void testEarthquakeAndJohnCallsEvidence() {
BayesInstance bayesInstance = new BayesInstance(jTree);
bayesInstance.setLikelyhood("JohnCalls", new double[]{1.0, 0.0});
bayesInstance.setLikelyhood("Earthquake", new double[]{1.0, 0.0});
bayesInstance.globalUpdate();
assertArray( new double[]{1.0, 0.0}, scaleDouble(3, bayesInstance.marginalize("JohnCalls").getDistribution()) );
assertArray( new double[]{0.618, 0.382 }, scaleDouble( 3, bayesInstance.marginalize("MaryCalls").getDistribution() ) );
assertArray( new double[]{0.003, 0.997}, scaleDouble(3, bayesInstance.marginalize("Burglary").getDistribution()) );
assertArray( new double[]{ 1.0, 0.0}, scaleDouble( 3, bayesInstance.marginalize("Earthquake").getDistribution() ) );
assertArray( new double[]{0.881, 0.119}, scaleDouble(3, bayesInstance.marginalize("Alarm").getDistribution()) );
}
}