/* * Encog(tm) Core v3.4 - Java Version * http://www.heatonresearch.com/encog/ * https://github.com/encog/encog-java-core * Copyright 2008-2016 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 org.encog.ml.bayesian.query.enumerate.EnumerationQuery; import org.junit.Assert; import org.junit.Test; public class TestEnumerationQuery { private void testPercent(double d, int target) { if( ((int)d)>=(target-2) && ((int)d)<=(target+2) ) { Assert.assertTrue(false); } } @Test public void testEnumeration1() { 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(); EnumerationQuery query = new EnumerationQuery(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); } @Test public void testEnumeration2() { 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(); EnumerationQuery query = new EnumerationQuery(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); } @Test public void testEnumeration3() { 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(); EnumerationQuery query = new EnumerationQuery(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); } }