mirror of https://github.com/jlizier/jidt
70 lines
2.7 KiB
Java
Executable File
70 lines
2.7 KiB
Java
Executable File
/*
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* Java Information Dynamics Toolkit (JIDT)
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* Copyright (C) 2012, Joseph T. Lizier
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*
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* This program is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program. If not, see <http://www.gnu.org/licenses/>.
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*/
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package infodynamics.measures.discrete;
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import infodynamics.utils.MatrixUtils;
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import infodynamics.utils.RandomGenerator;
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import junit.framework.TestCase;
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public class ConditionalMutualInformationTester extends TestCase {
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protected RandomGenerator rand = new RandomGenerator();
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protected int numObservations = 1000;
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public void testComputeSignificanceInt() {
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// Just making sure that no exception is thrown
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ConditionalMutualInformationCalculatorDiscrete condMiCalc = new ConditionalMutualInformationCalculatorDiscrete(2, 2, 2);
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int[] x1 = rand.generateRandomInts(numObservations, 2);
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int[] x2 = rand.generateRandomInts(numObservations, 2);
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int[] cond = rand.generateRandomInts(numObservations, 2);
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condMiCalc.initialise();
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condMiCalc.addObservations(x1, x2, cond);
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condMiCalc.computeAverageLocalOfObservations();
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condMiCalc.computeSignificance(1000);
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}
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public void testComputeLocalsGivesCorrectAverage() {
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ConditionalMutualInformationCalculatorDiscrete condMiCalc = new ConditionalMutualInformationCalculatorDiscrete(2, 2, 2);
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int[] x1 = rand.generateRandomInts(numObservations, 2);
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int[] x2 = rand.generateRandomInts(numObservations, 2);
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int[] cond = rand.generateRandomInts(numObservations, 2);
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condMiCalc.initialise();
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condMiCalc.addObservations(x1, x2, cond);
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double average = condMiCalc.computeAverageLocalOfObservations();
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double avLastAverage = condMiCalc.getLastAverage();
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double[] locals = condMiCalc.computeLocal(x1, x2, cond);
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double localsLastAverage = condMiCalc.getLastAverage();
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assertEquals(average, MatrixUtils.mean(locals), 0.000001);
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assertEquals(avLastAverage, localsLastAverage, 0.000001);
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assertEquals(average, avLastAverage, 0.000001);
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}
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public void testSetDebug() {
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ConditionalMutualInformationCalculatorDiscrete condMiCalc = new ConditionalMutualInformationCalculatorDiscrete(2, 2, 2);
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assertFalse(condMiCalc.debug);
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condMiCalc.setDebug(true);
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assertTrue(condMiCalc.debug);
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condMiCalc.setDebug(false);
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assertFalse(condMiCalc.debug);
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}
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}
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