mirror of https://github.com/jlizier/jidt
107 lines
3.5 KiB
Java
Executable File
107 lines
3.5 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.continuous;
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import infodynamics.utils.EmpiricalMeasurementDistribution;
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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 abstract class MultiInfoAbstractTester extends TestCase {
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protected double lastResult = 0.0;
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/**
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* Confirm that the local values average correctly back to the average value
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*
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* @param miCalc a pre-constructed MultiInfoCalculator object
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* @param dimensions number of dimensions for the data to use
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* @param timeSteps number of time steps for the random data
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*/
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public void testLocalsAverageCorrectly(MultiInfoCalculator miCalc,
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int dimensions, int timeSteps)
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throws Exception {
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miCalc.initialise(dimensions);
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// generate some random data
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RandomGenerator rg = new RandomGenerator();
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double[][] data = rg.generateNormalData(timeSteps, dimensions,
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0, 1);
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miCalc.setObservations(data);
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//miCalc.setDebug(true);
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double mi = miCalc.computeAverageLocalOfObservations();
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lastResult = mi;
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//miCalc.setDebug(false);
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double[] miLocal = miCalc.computeLocalOfPreviousObservations();
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System.out.printf("Average was %.5f\n", mi);
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assertEquals(mi, MatrixUtils.mean(miLocal), 0.00001);
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}
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/**
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* Confirm that significance testing doesn't alter the average that
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* would be returned.
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*
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* @param miCalc a pre-constructed MultiInfoCalculator object
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* @param dimensions number of dimensions for the data to use
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* @param timeSteps number of time steps for the random data
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* @throws Exception
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*/
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public void testComputeSignificanceDoesntAlterAverage(MultiInfoCalculator miCalc,
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int dimensions, int timeSteps) throws Exception {
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miCalc.initialise(dimensions);
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// generate some random data
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RandomGenerator rg = new RandomGenerator();
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double[][] data = rg.generateNormalData(timeSteps, dimensions,
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0, 1);
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miCalc.setObservations(data);
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//miCalc.setDebug(true);
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double mi = miCalc.computeAverageLocalOfObservations();
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//miCalc.setDebug(false);
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//double[] miLocal = miCalc.computeLocalOfPreviousObservations();
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System.out.printf("Average was %.5f\n", mi);
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// Now look at statistical significance tests
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EmpiricalMeasurementDistribution measDist =
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miCalc.computeSignificance(2);
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// Make sure that (the first) surrogate TE does not
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// match the actual TE (it could possibly match but with
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// an incredibly low probability)
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assertFalse(mi == measDist.distribution[0]);
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// And compute the average value again to check that it's consistent:
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for (int i = 0; i < 10; i++) {
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double lastAverage = miCalc.getLastAverage();
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assertEquals(mi, lastAverage);
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double averageCheck1 = miCalc.computeAverageLocalOfObservations();
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assertEquals(mi, averageCheck1);
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}
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}
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}
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