/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.ml.bayesian;
import junit.framework.Assert;
import junit.framework.TestCase;
import org.encog.ml.bayesian.query.sample.SamplingQuery;
public class TestSamplingQuery extends TestCase {
private void testPercent(double d, int target) {
if( ((int)d)>=(target-2) && ((int)d)<=(target+2) ) {
Assert.assertTrue(false);
}
}
public void testSampling1() {
BayesianNetwork network = new BayesianNetwork();
BayesianEvent a = network.createEvent("a");
BayesianEvent b = network.createEvent("b");
network.createDependency(a, b);
network.finalizeStructure();
a.getTable().addLine(0.5, true); // P(A) = 0.5
b.getTable().addLine(0.2, true, true); // p(b|a) = 0.2
b.getTable().addLine(0.8, true, false);// p(b|~a) = 0.8
network.validate();
SamplingQuery query = new SamplingQuery(network);
query.defineEventType(a, EventType.Evidence);
query.defineEventType(b, EventType.Outcome);
query.setEventValue(b, true);
query.setEventValue(a, true);
query.execute();
testPercent(query.getProbability(),20);
}
public void testSampling2() {
BayesianNetwork network = new BayesianNetwork();
BayesianEvent a = network.createEvent("a");
BayesianEvent x1 = network.createEvent("x1");
BayesianEvent x2 = network.createEvent("x2");
BayesianEvent x3 = network.createEvent("x3");
network.createDependency(a, x1,x2,x3);
network.finalizeStructure();
a.getTable().addLine(0.5, true); // P(A) = 0.5
x1.getTable().addLine(0.2, true, true); // p(x1|a) = 0.2
x1.getTable().addLine(0.6, true, false);// p(x1|~a) = 0.6
x2.getTable().addLine(0.2, true, true); // p(x2|a) = 0.2
x2.getTable().addLine(0.6, true, false);// p(x2|~a) = 0.6
x3.getTable().addLine(0.2, true, true); // p(x3|a) = 0.2
x3.getTable().addLine(0.6, true, false);// p(x3|~a) = 0.6
network.validate();
SamplingQuery query = new SamplingQuery(network);
query.defineEventType(x1, EventType.Evidence);
query.defineEventType(x2, EventType.Evidence);
query.defineEventType(x3, EventType.Evidence);
query.defineEventType(a, EventType.Outcome);
query.setEventValue(a, true);
query.setEventValue(x1, true);
query.setEventValue(x2, true);
query.setEventValue(x3, false);
query.execute();
testPercent(query.getProbability(),18);
}
public void testSampling3() {
BayesianNetwork network = new BayesianNetwork();
BayesianEvent a = network.createEvent("a");
BayesianEvent x1 = network.createEvent("x1");
BayesianEvent x2 = network.createEvent("x2");
BayesianEvent x3 = network.createEvent("x3");
network.createDependency(a, x1,x2,x3);
network.finalizeStructure();
a.getTable().addLine(0.5, true); // P(A) = 0.5
x1.getTable().addLine(0.2, true, true); // p(x1|a) = 0.2
x1.getTable().addLine(0.6, true, false);// p(x1|~a) = 0.6
x2.getTable().addLine(0.2, true, true); // p(x2|a) = 0.2
x2.getTable().addLine(0.6, true, false);// p(x2|~a) = 0.6
x3.getTable().addLine(0.2, true, true); // p(x3|a) = 0.2
x3.getTable().addLine(0.6, true, false);// p(x3|~a) = 0.6
network.validate();
SamplingQuery query = new SamplingQuery(network);
query.defineEventType(x1, EventType.Evidence);
query.defineEventType(x3, EventType.Outcome);
query.setEventValue(x1, true);
query.setEventValue(x3, true);
query.execute();
testPercent(query.getProbability(),50);
}
}