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