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Example3TeContinuousDataKernel.java
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Example3TeContinuousDataKernel.java
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/*
* 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.demos;
import infodynamics.utils.EmpiricalMeasurementDistribution;
import infodynamics.utils.RandomGenerator;
import infodynamics.measures.continuous.kernel.TransferEntropyCalculatorKernel;
/**
*
* = Example 3 - Transfer entropy on continuous data using kernel estimators =
*
* Simple transfer entropy (TE) calculation on continuous-valued data using the (box) kernel-estimator TE calculator.
*
* @author Joseph Lizier
*
*/
public class Example3TeContinuousDataKernel {
/**
* @param args
*/
public static void main(String[] args) throws Exception {
// Generate some random normalised data.
int numObservations = 1000;
double covariance = 0.4;
// Create destArray correlated to previous value of sourceArray:
RandomGenerator rg = new RandomGenerator();
double[] sourceArray = rg.generateNormalData(numObservations, 0, 1);
double[] destArray = rg.generateNormalData(numObservations, 0, 1-covariance);
for (int t = 1; t < numObservations; t++) {
destArray[t] += covariance * sourceArray[t-1];
}
// And an uncorrelated second source
double[] sourceArray2 = rg.generateNormalData(numObservations, 0, 1);
// Create a TE calculator and run it:
TransferEntropyCalculatorKernel teCalc =
new TransferEntropyCalculatorKernel();
teCalc.setProperty("NORMALISE", "true"); // Normalise the individual variables (default)
teCalc.initialise(1, 0.5); // Use history length 1 (Schreiber k=1), kernel width of 0.5 normalised units
teCalc.setObservations(sourceArray, destArray);
double result = teCalc.computeAverageLocalOfObservations();
// For copied source, should give something close to expected value for correlated Gaussians:.
// Expected correlation is expected covariance / product of expected standard deviations:
// (where square of destArray standard dev is sum of squares of std devs of
// underlying distributions)
double corr_expected = covariance /
(1.0 * Math.sqrt(Math.pow(covariance,2) + Math.pow(1-covariance,2)));
System.out.printf("TE result %.4f bits; expected to be close to " +
"%.4f bits for these correlated Gaussians but biased upwards\n",
result, -0.5 * Math.log(1-Math.pow(corr_expected,2))/Math.log(2));
teCalc.initialise(); // Initialise leaving the parameters the same
teCalc.setObservations(sourceArray2, destArray);
// For random source, it should give something close to 0 bits
double result2 = teCalc.computeAverageLocalOfObservations();
System.out.printf("TE result %.4f bits; expected to be close to " +
"0 bits for uncorrelated Gaussians but will be biased upwards\n",
result2);
// We can get insight into the bias by examining the null distribution:
EmpiricalMeasurementDistribution nullDist = teCalc.computeSignificance(100);
System.out.printf("Null distribution for unrelated source and destination " +
"(i.e. the bias) has mean %.4f and standard deviation %.4f\n",
nullDist.getMeanOfDistribution(), nullDist.getStdOfDistribution());
}
}