mirror of https://github.com/jlizier/jidt
69 lines
2.2 KiB
Java
Executable File
69 lines
2.2 KiB
Java
Executable File
/*
|
|
* Java Information Dynamics Toolkit (JIDT)
|
|
* Copyright (C) 2012, Joseph T. Lizier
|
|
*
|
|
* This program is free software: you can redistribute it and/or modify
|
|
* it under the terms of the GNU General Public License as published by
|
|
* the Free Software Foundation, either version 3 of the License, or
|
|
* (at your option) any later version.
|
|
*
|
|
* This program is distributed in the hope that it will be useful,
|
|
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
* GNU General Public License for more details.
|
|
*
|
|
* You should have received a copy of the GNU General Public License
|
|
* along with this program. If not, see <http://www.gnu.org/licenses/>.
|
|
*/
|
|
|
|
package infodynamics.measures.continuous.kernel;
|
|
|
|
import junit.framework.TestCase;
|
|
import infodynamics.utils.RandomGenerator;
|
|
|
|
public class MutualInfoMultiVariateTester extends TestCase {
|
|
|
|
public void testComputeSignificanceDoesntAlterAverage() throws Exception {
|
|
|
|
MutualInfoCalculatorMultiVariateKernel miCalc =
|
|
new MutualInfoCalculatorMultiVariateKernel();
|
|
|
|
int dimensions = 2;
|
|
int timeSteps = 100;
|
|
double kernelWidth = 1;
|
|
|
|
miCalc.setProperty(
|
|
MutualInfoCalculatorMultiVariateKernel.NORMALISE_PROP_NAME,
|
|
"true");
|
|
miCalc.initialise(dimensions, dimensions, kernelWidth);
|
|
|
|
// generate some random data
|
|
RandomGenerator rg = new RandomGenerator();
|
|
double[][] sourceData = rg.generateNormalData(timeSteps, dimensions,
|
|
0, 1);
|
|
double[][] destData = rg.generateNormalData(timeSteps, dimensions,
|
|
0, 1);
|
|
|
|
miCalc.setObservations(sourceData, destData);
|
|
|
|
//miCalc.setDebug(true);
|
|
double mi = miCalc.computeAverageLocalOfObservations();
|
|
//miCalc.setDebug(false);
|
|
//double[] miLocal = miCalc.computeLocalOfPreviousObservations();
|
|
|
|
System.out.printf("Average was %.5f\n", mi);
|
|
|
|
// Now look at statistical significance tests
|
|
int[][] newOrderings = rg.generateDistinctRandomPerturbations(
|
|
timeSteps, 100);
|
|
|
|
miCalc.computeSignificance(newOrderings);
|
|
|
|
// And compute the average value again to check that it's consistent:
|
|
for (int i = 0; i < 10; i++) {
|
|
double averageCheck1 = miCalc.computeAverageLocalOfObservations();
|
|
assertEquals(mi, averageCheck1);
|
|
}
|
|
}
|
|
}
|