mirror of https://github.com/jlizier/jidt
400 lines
18 KiB
Java
Executable File
400 lines
18 KiB
Java
Executable File
package infodynamics.measures.continuous;
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import java.util.Vector;
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import infodynamics.utils.MatrixUtils;
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/**
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*
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* <p>Transfer entropy calculator for multivariate source and destination
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* which is implemented using a
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* Conditional Mutual Information calculator.
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* </p>
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*
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* <p>This implementation inherits from the TransferEntropyCalculatorViaCondMutualInfo,
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* so the univariate methods for adding observations etc are still available, however
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* they will throw Exceptions if this calculator was not initialised for single
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* dimensional data sets.
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* </p>
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*
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* TODO Delete TransferEntropyCalculatorCommon once we've switched everything over to use this?
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* Might be useful to leave it after all, and move common functionality from here to there.
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*
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* TODO Switch the properties like l etc into TransferEntropyCalculator
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* once all TE calculators follow this class.
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*
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* @see "Schreiber, Physical Review Letters 85 (2) pp.461-464, 2000;
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* <a href='http://dx.doi.org/10.1103/PhysRevLett.85.461'>download</a>
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* (for definition of transfer entropy)"
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* @see "Lizier, Prokopenko and Zomaya, Physical Review E 77, 026110, 2008;
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* <a href='http://dx.doi.org/10.1103/PhysRevE.77.026110'>download</a>
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* (for definition of <i>local</i> transfer entropy and qualification
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* of naming it as <i>apparent</i> transfer entropy)"
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* @see "J.T. Lizier, J. Heinzle, A. Horstmann, J.-D. Haynes, M. Prokopenko,
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* Journal of Computational Neuroscience, vol. 30, pp. 85-107, 2011
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* <a href='http://dx.doi.org/10.1007/s10827-010-0271-2'>download</a>
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* (for definition of <i>multivariate</i> transfer entropy"
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*
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* @see {@link TransferEntropyCalculatorViaCondMutualInfo}
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* @see {@link TransferEntropyCalculatorMultiVariate}
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* @see {@link TransferEntropyCalculator}
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*
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* @author Joseph Lizier, <a href="joseph.lizier at gmail.com">email</a>,
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* <a href="http://lizier.me/joseph/">www</a>
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*/
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public abstract class TransferEntropyCalculatorMultiVariateViaCondMutualInfo
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extends TransferEntropyCalculatorViaCondMutualInfo
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// which means we implement TransferEntropyCalculator
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implements TransferEntropyCalculatorMultiVariate {
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/**
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* Number of dimensions of the destination
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*/
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protected int destDimensions = 1;
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/**
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* Number of dimensions of the source
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*/
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protected int sourceDimensions = 1;
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public TransferEntropyCalculatorMultiVariateViaCondMutualInfo(String condMiCalculatorClassName)
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throws InstantiationException, IllegalAccessException, ClassNotFoundException {
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super(condMiCalculatorClassName);
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}
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public TransferEntropyCalculatorMultiVariateViaCondMutualInfo(Class<ConditionalMutualInfoCalculatorMultiVariate> condMiCalcClass)
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throws InstantiationException, IllegalAccessException, ClassNotFoundException {
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super(condMiCalcClass);
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}
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/**
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* Construct this calculator by passing in a constructed but not initialised
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* underlying Conditional Mutual information calculator.
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*
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* @param condMiCalc
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* @throws Exception
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*/
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public TransferEntropyCalculatorMultiVariateViaCondMutualInfo(ConditionalMutualInfoCalculatorMultiVariate condMiCalc) throws Exception {
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super(condMiCalc);
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}
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/* (non-Javadoc)
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* @see infodynamics.measures.continuous.ChannelCalculatorMultiVariate#initialise(int, int)
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*/
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public void initialise(int sourceDimensions, int destDimensions)
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throws Exception {
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initialise(sourceDimensions, destDimensions, k, k_tau, l, l_tau, delay);
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}
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/* (non-Javadoc)
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* @see infodynamics.measures.continuous.TransferEntropyCalculatorMultiVariate#initialise(int, int, int)
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*/
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public void initialise(int k, int sourceDimensions, int destDimensions)
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throws Exception {
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initialise(sourceDimensions, destDimensions, k, k_tau, l, l_tau, delay);
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}
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/* (non-Javadoc)
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* @see infodynamics.measures.continuous.TransferEntropyCalculatorViaCondMutualInfo#initialise(int, int, int, int, int)
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*/
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@Override
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public void initialise(int k, int k_tau, int l, int l_tau, int delay)
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throws Exception {
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initialise(sourceDimensions, destDimensions, k, k_tau, l, l_tau, delay);
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}
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/**
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* Initialise the calculator.
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*
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* @param sourceDimensions dimensionality of the source variable
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* @param destDimensions dimensionality of the destination variable
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* @param k Length of destination past history to consider
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* @param k_tau embedding delay for the destination variable
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* @param l length of source past history to consider
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* @param l_tau embedding delay for the source variable
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* @param delay time lag between last element of source and destination next value
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*/
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public void initialise(int sourceDimensions, int destDimensions, int k, int k_tau, int l, int l_tau, int delay) throws Exception {
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if (delay < 0) {
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throw new Exception("Cannot compute TE with source-destination delay < 0");
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}
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this.sourceDimensions = sourceDimensions;
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this.destDimensions = destDimensions;
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this.k = k;
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this.k_tau = k_tau;
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this.l = l;
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this.l_tau = l_tau;
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this.delay = delay;
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// Now check which point we can start taking observations from in any
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// addObservations call. These two integers represent the last
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// point of the destination embedding, in the cases where the destination
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// embedding itself determines where we can start taking observations, or
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// the case where the source embedding plus delay is longer and so determines
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// where we can start taking observations.
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int startTimeBasedOnDestPast = (k-1)*k_tau;
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int startTimeBasedOnSourcePast = (l-1)*l_tau + delay - 1;
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startTimeForFirstDestEmbedding = Math.max(startTimeBasedOnDestPast, startTimeBasedOnSourcePast);
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condMiCalc.initialise(l*sourceDimensions, destDimensions, k*destDimensions);
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}
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/**
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* <p>Sets the single set of observations to compute the PDFs from.
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* Cannot be called in conjunction with
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* {@link #startAddObservations()}/{@link #addObservations(double[], double[])} /
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* {@link #finaliseAddObservations()}.</p>
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*
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* <p>Can only be called on this multivariate calculator if the dimensions
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* of both source and destination are 1, otherwise throws an exception</p>
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*
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* @param source univariate observations for the source variable
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* @param destination univariate observations for the destination variable
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* @throws Exception
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*/
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public void setObservations(double[] source, double[] destination) throws Exception {
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if ((sourceDimensions != 1) || (destDimensions != 1)) {
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throw new Exception("Cannot call the univariate setObservations if you " +
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"have initialised with dimension > 1 for either source or destination");
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}
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super.setObservations(source, destination);
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}
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/* (non-Javadoc)
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* @see infodynamics.measures.continuous.ChannelCalculatorMultiVariate#setObservations(double[][], double[][])
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*/
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public void setObservations(double[][] source, double[][] destination)
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throws Exception {
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if (source.length != destination.length) {
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throw new Exception(String.format("Source and destination lengths (%d and %d) must match!",
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source.length, destination.length));
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}
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if (source.length < startTimeForFirstDestEmbedding + 2) {
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// There are no observations to add here, the time series is too short
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throw new Exception("Not enough observations to set here given k, k_tau, l, l_tau and delay parameters");
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}
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double[][] currentDestPastVectors =
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MatrixUtils.makeDelayEmbeddingVector(destination, k, k_tau,
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startTimeForFirstDestEmbedding,
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destination.length - startTimeForFirstDestEmbedding - 1);
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double[][] currentDestNextVectors =
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MatrixUtils.makeDelayEmbeddingVector(destination, 1,
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startTimeForFirstDestEmbedding + 1,
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destination.length - startTimeForFirstDestEmbedding - 1);
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double[][] currentSourcePastVectors =
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MatrixUtils.makeDelayEmbeddingVector(source, l, l_tau,
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startTimeForFirstDestEmbedding + 1 - delay,
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source.length - startTimeForFirstDestEmbedding - 1);
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condMiCalc.setObservations(currentSourcePastVectors, currentDestNextVectors, currentDestPastVectors);
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}
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/**
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* <p>Adds new observations for the PDFs, as specified in
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* {@link ChannelCalculator#addObservations(double[], double[])}.</p>
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*
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* <p>Can only be called on this multivariate calculator if the dimensions
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* of both source and destination are 1, otherwise throws an exception</p>
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*
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* @param source univariate observations for the source variable
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* @param destination univariate observations for the destination variable
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* @throws Exception
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* @see {@link ChannelCalculator#addObservations(double[], double[])}
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*/
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public void addObservations(double[] source, double[] destination) throws Exception {
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if ((sourceDimensions != 1) || (destDimensions != 1)) {
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throw new Exception("Cannot call the univariate addObservations if you " +
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"have initialised with dimension > 1 for either source or destination");
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}
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super.addObservations(source, destination);
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}
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/* (non-Javadoc)
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* @see infodynamics.measures.continuous.ChannelCalculatorMultiVariate#addObservations(double[][], double[][])
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*/
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public void addObservations(double[][] source, double[][] destination)
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throws Exception {
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if (source.length != destination.length) {
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throw new Exception(String.format("Source and destination lengths (%d and %d) must match!",
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source.length, destination.length));
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}
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if (source.length < startTimeForFirstDestEmbedding + 2) {
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// There are no observations to add here, the time series is too short
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// Don't throw an exception, do nothing since more observations
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// can be added later.
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return;
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}
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double[][] currentDestPastVectors =
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MatrixUtils.makeDelayEmbeddingVector(destination, k, k_tau,
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startTimeForFirstDestEmbedding,
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destination.length - startTimeForFirstDestEmbedding - 1);
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double[][] currentDestNextVectors =
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MatrixUtils.makeDelayEmbeddingVector(destination, 1,
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startTimeForFirstDestEmbedding + 1,
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destination.length - startTimeForFirstDestEmbedding - 1);
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double[][] currentSourcePastVectors =
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MatrixUtils.makeDelayEmbeddingVector(source, l, l_tau,
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startTimeForFirstDestEmbedding + 1 - delay,
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source.length - startTimeForFirstDestEmbedding - 1);
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condMiCalc.addObservations(currentSourcePastVectors, currentDestNextVectors, currentDestPastVectors);
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}
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/**
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* <p>Adds new observations for the PDFs, as specified in
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* {@link ChannelCalculator#addObservations(double[], double[], int, int)}.</p>
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*
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* <p>Can only be called on this multivariate calculator if the dimensions
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* of both source and destination are 1, otherwise throws an exception</p>
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*
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* @param source univariate observations for the source variable
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* @param destination univariate observations for the destination variable
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* @param startTime time index to start taking the observations
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* @param numTimeSteps number of time steps including startTime to take for observations
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* @throws Exception
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* @see {@link ChannelCalculator#addObservations(double[], double[], int, int)}
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*/
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public void addObservations(double[] source, double[] destination,
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int startTime, int numTimeSteps) throws Exception {
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if ((sourceDimensions != 1) || (destDimensions != 1)) {
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throw new Exception("Cannot call the univariate addObservations if you " +
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"have initialised with dimension > 1 for either source or destination");
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}
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super.addObservations(source, destination, startTime, numTimeSteps);
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}
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/* (non-Javadoc)
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* @see infodynamics.measures.continuous.ChannelCalculatorMultiVariate#addObservations(double[][], double[][], int, int)
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*/
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public void addObservations(double[][] source, double[][] destination,
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int startTime, int numTimeSteps) throws Exception {
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if (source.length != destination.length) {
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throw new Exception(String.format("Source and destination lengths (%d and %d) must match!",
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source.length, destination.length));
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}
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if (source.length < startTime + numTimeSteps) {
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// There are not enough observations given the arguments here
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throw new Exception("Not enough observations to set here given startTime and numTimeSteps parameters");
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}
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addObservations(MatrixUtils.selectRows(source, startTime, numTimeSteps),
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MatrixUtils.selectRows(destination, startTime, numTimeSteps));
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}
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/**
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* <p>Sets new observations for the PDFs, as specified in
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* {@link ChannelCalculator#setObservations(double[], double[], boolean[], boolean[])}.</p>
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*
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* <p>Can only be called on this multivariate calculator if the dimensions
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* of both source and destination are 1, otherwise throws an exception</p>
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*
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* @param source univariate observations for the source variable
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* @param destination univariate observations for the destination variable
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* @param sourceValid time series (with time indices the same as source)
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* indicating whether the source at that point is valid.
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* @param destValid time series (with time indices the same as destination)
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* indicating whether the destination at that point is valid.
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* @throws Exception
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* @see {@link ChannelCalculator#setObservations(double[], double[], boolean[], boolean[])}
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*/
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public void setObservations(double[] source, double[] destination,
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boolean[] sourceValid, boolean[] destValid) throws Exception {
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if ((sourceDimensions != 1) || (destDimensions != 1)) {
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throw new Exception("Cannot call the univariate setObservations if you " +
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"have initialised with dimension > 1 for either source or destination");
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}
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super.setObservations(source, destination, sourceValid, destValid);
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}
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/* (non-Javadoc)
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* @see infodynamics.measures.continuous.ChannelCalculatorMultiVariate#setObservations(double[][], double[][], boolean[], boolean[])
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*/
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public void setObservations(double[][] source, double[][] destination,
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boolean[] sourceValid, boolean[] destValid) throws Exception {
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Vector<int[]> startAndEndTimePairs = computeStartAndEndTimePairs(sourceValid, destValid);
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// We've found the set of start and end times for this pair
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startAddObservations();
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for (int[] timePair : startAndEndTimePairs) {
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int startTime = timePair[0];
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int endTime = timePair[1];
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addObservations(source, destination, startTime, endTime - startTime + 1);
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}
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finaliseAddObservations();
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}
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/* (non-Javadoc)
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* @see infodynamics.measures.continuous.ChannelCalculatorMultiVariate#setObservations(double[][], double[][], boolean[][], boolean[][])
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*/
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public void setObservations(double[][] source, double[][] destination,
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boolean[][] sourceValid, boolean[][] destValid) throws Exception {
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boolean[] jointSourceValid = MatrixUtils.andRows(sourceValid);
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boolean[] jointDestValid = MatrixUtils.andRows(destValid);
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setObservations(source, destination, jointSourceValid, jointDestValid);
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}
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/**
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* <p>Computes the local values of the transfer entropy
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* for each valid observation in the supplied observations,
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* as specified in ...
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* TODO I don't think this is specified in any interfaces yet ...?
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* </p>
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*
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* <p>Can only be called on this multivariate calculator if the dimensions
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* of both source and destination are 1, otherwise throws an exception</p>
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*
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* @param newSourceObservations univariate observations for the source variable
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* @param newDestObservations univariate observations for the destination variable
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* @throws Exception
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*/
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public double[] computeLocalUsingPreviousObservations(double[] newSourceObservations, double[] newDestObservations) throws Exception {
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if ((sourceDimensions != 1) || (destDimensions != 1)) {
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throw new Exception("Cannot call the univariate computeLocalUsingPreviousObservations if you " +
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"have initialised with dimension > 1 for either source or destination");
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}
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return super.computeLocalUsingPreviousObservations(newSourceObservations, newDestObservations);
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}
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/**
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* <p>Computes the local values of the transfer entropy
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* for each valid observation in the supplied observations,
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* as specified in ...
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* TODO I don't think this is specified in any interfaces yet ...?
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* </p>
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*
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* @param newSourceObservations multivariate observations for the source variable
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* @param newDestObservations multivariate observations for the destination variable
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* @throws Exception
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*/
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public double[] computeLocalUsingPreviousObservations(double[][] newSourceObservations, double[][] newDestObservations) throws Exception {
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if (newSourceObservations.length != newDestObservations.length) {
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throw new Exception(String.format("Source and destination lengths (%d and %d) must match!",
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newSourceObservations.length, newDestObservations.length));
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}
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if (newDestObservations.length < startTimeForFirstDestEmbedding + 2) {
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// There are no observations to compute for here
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return new double[newDestObservations.length];
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}
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double[][] newDestPastVectors =
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MatrixUtils.makeDelayEmbeddingVector(newDestObservations, k, k_tau,
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startTimeForFirstDestEmbedding,
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newDestObservations.length - startTimeForFirstDestEmbedding - 1);
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double[][] newDestNextVectors =
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MatrixUtils.makeDelayEmbeddingVector(newDestObservations, 1,
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startTimeForFirstDestEmbedding + 1,
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newDestObservations.length - startTimeForFirstDestEmbedding - 1);
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double[][] newSourcePastVectors =
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MatrixUtils.makeDelayEmbeddingVector(newSourceObservations, l, l_tau,
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startTimeForFirstDestEmbedding + 1 - delay,
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newSourceObservations.length - startTimeForFirstDestEmbedding - 1);
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double[] local = condMiCalc.computeLocalUsingPreviousObservations(
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newSourcePastVectors, newDestNextVectors, newDestPastVectors);
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// Pad the front of the array with zeros where local TE isn't defined:
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double[] localsToReturn = new double[local.length + startTimeForFirstDestEmbedding + 1];
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System.arraycopy(local, 0, localsToReturn, startTimeForFirstDestEmbedding + 1, local.length);
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return localsToReturn;
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}
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}
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