mirror of https://github.com/jlizier/jidt
82 lines
3.4 KiB
Java
Executable File
82 lines
3.4 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.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);
|
|
// For copied source, should give something close to expected value for correlated Gaussians:
|
|
double result = teCalc.computeAverageLocalOfObservations();
|
|
System.out.printf("TE result %.4f bits; expected to be close to " +
|
|
"%.4f bits for these correlated Gaussians but biased upwards\n",
|
|
result, Math.log(1.0/(1-Math.pow(covariance,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());
|
|
}
|
|
}
|