mirror of https://github.com/jlizier/jidt
1619 lines
59 KiB
Java
Executable File
1619 lines
59 KiB
Java
Executable File
package infodynamics.measures.discrete;
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import infodynamics.utils.AnalyticMeasurementDistribution;
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import infodynamics.utils.AnalyticNullDistributionComputer;
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import infodynamics.utils.ChiSquareMeasurementDistribution;
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import infodynamics.utils.MathsUtils;
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import infodynamics.utils.MatrixUtils;
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import infodynamics.utils.EmpiricalMeasurementDistribution;
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import infodynamics.utils.RandomGenerator;
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/**
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* <p>Implements transfer entropy (see Schreiber, PRL, 2000)
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* and local transfer entropy (see Lizier et al, PRE, 2008).
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* We use the term <i>apparent</i> transfer entropy to mean that
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* we compute the transfer that appears to come from a single
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* source variable, without examining any other potential sources
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* (see Lizier et al, PRE, 2008). This is also known as <i>pairwise</i>
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* transfer entropy.</p>
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*
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* <p>Specifically, this implements the transfer entropy for
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* <i>discrete</i>-valued variables.</p>
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*
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* <p>Usage:
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* <ol>
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* <li>Construct: {@link #TransferEntropyCalculator(int, int)}</li>
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* <li>Initialise: {@link #initialise()}</li>
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* <li>Either:
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* <ol>
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* <li>Continuous accumulation of observations then measurement; call:
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* <ol>
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* <li>{@link #addObservations(int[], int[])} or related calls
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* several times over - <b>note:</b> each method call adding
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* observations can be viewed as updating the PDFs; they do not
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* append the separate time series (this would be incorrect behaviour
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* for the transfer entropy, since the start of one time series
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* is not necessarily related to the end of the other).</li>
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* <li>The compute relevant quantities, e.g.
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* {@link #computeLocalFromPreviousObservations(int[], int[])} or
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* {@link #computeAverageLocalOfObservations()}</li>
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* </ol>
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* <li>or Standalone computation from a single set of observations;
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* call e.g.: {@link #computeLocal(int[], int[])} or
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* {@link #computeAverageLocal(int[][], int)}.>/li>
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* </ol>
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* </ol>
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* </p>
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*
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* TODO Add arbitrary source-dest delay
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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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*
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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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*/
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public class TransferEntropyCalculator extends ContextOfPastMeasureCalculator
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implements ChannelCalculator, AnalyticNullDistributionComputer {
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protected int[][][] sourceNextPastCount = null; // count for (source[n],dest[n+1],dest[n]^k) tuples
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protected int[][] sourcePastCount = null; // count for (source[n],dest[n]^k) tuples
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/**
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* Whether to assume periodic boundary conditions for channels across
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* the boundary of the multidimensional
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* calls supplying observations, e.g.
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* {@link #addObservations(int[][], int)} calls
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*/
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protected boolean periodicBoundaryConditions = true;
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/**
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* Embedding length of the source variable.
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* This is "l" in Schreiber's notation.
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*/
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protected int sourceHistoryEmbedLength = 1;
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/**
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* A cached value of base^sourceHistoryEmbedLength
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*/
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protected int base_power_l = 1;
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/**
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* A cached value of each discrete value left shifted (in "base" counting) by (sourceHistoryEmbedLength-1).
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*/
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protected int[] maxShiftedSourceValue = null; // states * (base^(sourceHistoryEmbedLength-1))
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/**
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* First time step at which we can take an observation
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* (needs to account for an embedding in the previous steps)
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*/
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protected int startObservationTime = 1;
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/**
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* Tracks whether the measure has been computed since the last initialisation
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*/
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protected boolean estimateComputed = false;
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/**
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* User was formerly forced to create new instances through this factory method.
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* Retained for backwards compatibility.
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*
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* @param base
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* @param destHistoryEmbedLength
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*
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* @return
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*/
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public static TransferEntropyCalculator newInstance(int base, int destHistoryEmbedLength) {
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return new TransferEntropyCalculator(base, destHistoryEmbedLength);
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// Old code for an attempted optimisation:
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/*
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if (isPowerOf2(base)) {
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return new ApparentTransferEntropyCalculatorBase2(base, history);
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} else {
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return new ApparentTransferEntropyCalculator(base, history);
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}
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*/
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}
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/**
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* Create a new TE calculator for the given base and destination history embedding length.
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*
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* @param base number of quantisation levels for each variable.
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* E.g. binary variables are in base-2.
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* @param destHistoryEmbedLength embedded history length of the destination to condition on -
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* this is k in Schreiber's notation.
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*/
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public TransferEntropyCalculator(int base, int destHistoryEmbedLength) {
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super(base, destHistoryEmbedLength);
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base_power_l = MathsUtils.power(base, sourceHistoryEmbedLength);
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// Create constants for tracking sourceValues
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maxShiftedSourceValue = new int[base];
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for (int v = 0; v < base; v++) {
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maxShiftedSourceValue[v] = v;
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}
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// Create storage for extra counts of observations
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sourceNextPastCount = new int[base_power_l][base][base_power_k];
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sourcePastCount = new int[base_power_l][base_power_k];
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// Which time step do we start taking observations from?
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// Normally this is k (to allow k previous time steps)
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// but if k==0 (becoming a lagged MI), it's 1.
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startObservationTime = Math.max(k, 1);
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}
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/**
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* Create a new TE calculator for the given base and destination history embedding length.
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*
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* @param base number of quantisation levels for each variable.
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* E.g. binary variables are in base-2.
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* @param destHistoryEmbedLength embedded history length of the destination to condition on -
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* this is k in Schreiber's notation.
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* @param sourceHistoryEmbeddingLength embedded history length of the source to include -
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* this is l in Schreiber's notation.
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*/
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public TransferEntropyCalculator(int base, int destHistoryEmbedLength, int sourceHistoryEmbeddingLength) {
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super(base, destHistoryEmbedLength);
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this.sourceHistoryEmbedLength = sourceHistoryEmbeddingLength;
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base_power_l = MathsUtils.power(base, sourceHistoryEmbedLength);
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// Check that we can convert the history value into an integer ok:
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if (sourceHistoryEmbedLength > Math.log(Integer.MAX_VALUE) / log_base) {
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throw new RuntimeException("Base and source history combination too large");
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}
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// Create constants for tracking sourceValues
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maxShiftedSourceValue = new int[base];
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for (int v = 0; v < base; v++) {
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maxShiftedSourceValue[v] = v * MathsUtils.power(base, sourceHistoryEmbedLength-1);
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}
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// Create storage for extra counts of observations
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sourceNextPastCount = new int[base_power_l][base][base_power_k];
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sourcePastCount = new int[base_power_l][base_power_k];
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// Which time step do we start taking observations from?
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// Normally this is k (to allow k previous time steps)
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// but if k==0 (becoming a lagged MI), it's 1.
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// We also allow for source embeddings here too
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startObservationTime = Math.max(Math.max(k, sourceHistoryEmbedLength), 1);
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}
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/**
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* Initialise calculator, preparing to take observation sets in
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* Should be called prior to any of the addObservations() methods.
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* You can reinitialise without needing to create a new object.
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*
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*/
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public void initialise(){
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super.initialise();
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estimateComputed = false;
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MatrixUtils.fill(sourceNextPastCount, 0);
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MatrixUtils.fill(sourcePastCount, 0);
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}
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/**
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* Add observations for a single source-destination pair
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* to our estimates of the pdfs.
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* @param source source timte series
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* @param dest destination time series
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*/
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public void addObservations(int[] source, int[] dest) {
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int rows = dest.length;
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// increment the count of observations:
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observations += (rows - startObservationTime);
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// Initialise and store the current previous value
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int pastVal = 0;
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for (int p = 0; p < k; p++) {
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pastVal *= base;
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pastVal += dest[startObservationTime - k + p];
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}
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int sourceVal = 0;
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for (int p = 0; p < sourceHistoryEmbedLength; p++) {
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sourceVal *= base;
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sourceVal += source[startObservationTime - sourceHistoryEmbedLength + p];
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}
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// 1. Count the tuples observed
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int destVal;
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for (int r = startObservationTime; r < rows; r++) {
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// Add to the count for this particular transition:
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// (cell's assigned as above)
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destVal = dest[r];
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sourceNextPastCount[sourceVal][destVal][pastVal]++;
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sourcePastCount[sourceVal][pastVal]++;
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nextPastCount[destVal][pastVal]++;
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pastCount[pastVal]++;
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nextCount[destVal]++;
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// Update the previous values:
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if (k > 0) {
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pastVal -= maxShiftedValue[dest[r-k]];
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pastVal *= base;
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pastVal += dest[r];
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}
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sourceVal -= maxShiftedSourceValue[source[r-sourceHistoryEmbedLength]];
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sourceVal *= base;
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sourceVal += source[r];
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}
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}
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/**
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* Add observations for a single source-destination pair
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* to our estimates of the pdfs.
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* @param source source timte series
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* @param dest destination time series
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* @param valid time series of whether the signals
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* at the given time should be considered valid
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* and added to our PDFs
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*/
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public void addObservations(int[] source, int[] dest, boolean[] valid) {
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int rows = dest.length;
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// Initialise and store the current previous value
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int pastVal = 0;
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// We can take an observation if timeSinceLastDestInvalid > k
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// and timeSinceLastSourceInvalid > sourceHistoryEmbedLength.
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// In preparation for introducing a source-dest delay,
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// we're using two different time variables here.
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int timeSinceLastDestInvalid = k;
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int timeSinceLastSourceInvalid = sourceHistoryEmbedLength;
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for (int p = 0; p < k; p++) {
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pastVal *= base;
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pastVal += dest[startObservationTime - k + p];
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if (!valid[startObservationTime - k + p]) {
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// Time from startObservationTime backwards to this step
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timeSinceLastDestInvalid = k - p - 1;
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}
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}
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int sourceVal = 0;
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for (int p = 0; p < sourceHistoryEmbedLength; p++) {
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sourceVal *= base;
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sourceVal += source[startObservationTime - sourceHistoryEmbedLength + p];
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if (!valid[startObservationTime - sourceHistoryEmbedLength + p]) {
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// Time from startObservationTime backwards to this step
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timeSinceLastSourceInvalid = sourceHistoryEmbedLength - p - 1;
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}
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}
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// 1. Count the tuples observed
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int destVal;
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for (int r = startObservationTime; r < rows; r++) {
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timeSinceLastDestInvalid++;
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timeSinceLastSourceInvalid++;
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// Pre-condition now:
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// timeSinceLastDestInvalid holds the time from r back to
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// the last valid destination point (not including current destination)
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// timeSinceLastSourceInvalid holds the time from r back to
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// the last valid source point (not including new source value
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if (!valid[r]) {
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timeSinceLastDestInvalid = 0;
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} else if ((timeSinceLastDestInvalid > k) &&
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(timeSinceLastSourceInvalid > sourceHistoryEmbedLength)) {
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// Add to the count for this particular transition:
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// (cell's assigned as above)
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destVal = dest[r];
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sourceNextPastCount[sourceVal][destVal][pastVal]++;
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sourcePastCount[sourceVal][pastVal]++;
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nextPastCount[destVal][pastVal]++;
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pastCount[pastVal]++;
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nextCount[destVal]++;
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observations++;
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}
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// Update the previous values:
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if (k > 0) {
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pastVal -= maxShiftedValue[dest[r-k]];
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pastVal *= base;
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pastVal += dest[r];
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}
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sourceVal -= maxShiftedSourceValue[source[r-sourceHistoryEmbedLength]];
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sourceVal *= base;
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sourceVal += source[r];
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if (!valid[r]) {
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timeSinceLastSourceInvalid = 0;
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}
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}
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}
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/**
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* Add observations for a single source-destination pair of the multi-agent system
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* to our estimates of the pdfs.
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* This call is for time series not part of the same 2D array.
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* Start and end time are the (inclusive) indices within which to add the observations.
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* The start time is from the earliest of the k historical values of the destination (inclusive),
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* the end time is the last destination time point to add in.
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* @param source
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* @param dest
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* @param startTime
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* @param endTime
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*
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*/
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public void addObservations(int[] source, int[] dest, int startTime, int endTime) {
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// increment the count of observations:
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observations += (endTime - startTime) - startObservationTime + 1;
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// Initialise and store the current previous value
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int pastVal = 0;
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for (int p = 0; p < k; p++) {
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pastVal *= base;
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pastVal += dest[startTime + startObservationTime - k + p];
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}
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int sourceVal = 0;
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for (int p = 0; p < sourceHistoryEmbedLength; p++) {
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sourceVal *= base;
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sourceVal += source[startTime + startObservationTime - sourceHistoryEmbedLength + p];
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}
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// 1. Count the tuples observed
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int destVal;
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for (int r = startTime + startObservationTime; r <= endTime; r++) {
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// Add to the count for this particular transition:
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// (cell's assigned as above)
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destVal = dest[r];
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sourceNextPastCount[sourceVal][destVal][pastVal]++;
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sourcePastCount[sourceVal][pastVal]++;
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nextPastCount[destVal][pastVal]++;
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pastCount[pastVal]++;
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nextCount[destVal]++;
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// Update the previous value:
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if (k > 0) {
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pastVal -= maxShiftedValue[dest[r-k]];
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pastVal *= base;
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pastVal += dest[r];
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}
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sourceVal -= maxShiftedSourceValue[source[r-sourceHistoryEmbedLength]];
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sourceVal *= base;
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sourceVal += source[r];
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}
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}
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/**
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* Add observations in to our estimates of the pdfs.
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* This call suitable only for homogeneous agents, as all
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* agents will contribute to single pdfs.
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*
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* @param states 1st index is time, 2nd index is agent number
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* @param j - number of columns to compute transfer entropy across
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* (i.e. src is column i-j, dest is column i: transfer is j cells to the right)
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*/
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public void addObservations(int states[][], int j) {
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int timeSteps = states.length;
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int agents = states[0].length;
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// increment the count of observations:
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if (periodicBoundaryConditions) {
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observations += (timeSteps - startObservationTime)*agents;
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} else {
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observations += (timeSteps - startObservationTime)*(agents - Math.abs(j));
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}
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// Initialise and store the current previous and source value for each column
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int[] pastVal = new int[agents];
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for (int c = 0; c < agents; c++) {
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pastVal[c] = 0;
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for (int p = 0; p < k; p++) {
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pastVal[c] *= base;
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pastVal[c] += states[startObservationTime - k + p][c];
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}
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}
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int[] sourceVal = new int[agents];
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for (int c = 0; c < agents; c++) {
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sourceVal[c] = 0;
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int sourceAgent = c-j;
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if ((sourceAgent < 0) || (sourceAgent >= agents)) {
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// Source agent is out of bounds unless we are using periodic boundary conditions
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if (periodicBoundaryConditions) {
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sourceAgent = (sourceAgent+agents) % agents;
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} else {
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// Don't add this to our observations
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continue;
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}
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}
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for (int p = 0; p < sourceHistoryEmbedLength; p++) {
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sourceVal[c] *= base;
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sourceVal[c] += states[startObservationTime - sourceHistoryEmbedLength + p][sourceAgent];
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}
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}
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// 1. Count the tuples observed
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int destVal;
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for (int r = startObservationTime; r < timeSteps; r++) {
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for (int c = 0; c < agents; c++) {
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// Add to the count for this particular transition:
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// (cell's assigned as above)
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int sourceAgent = c-j;
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if ((sourceAgent < 0) || (sourceAgent >= agents)) {
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// Source agent is out of bounds unless we are using periodic boundary conditions
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if (periodicBoundaryConditions) {
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sourceAgent = (sourceAgent+agents) % agents;
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} else {
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// Don't add this to our observations
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continue;
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}
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}
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destVal = states[r][c];
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sourceNextPastCount[sourceVal[c]][destVal][pastVal[c]]++;
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sourcePastCount[sourceVal[c]][pastVal[c]]++;
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nextPastCount[destVal][pastVal[c]]++;
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pastCount[pastVal[c]]++;
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nextCount[destVal]++;
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// Update the previous value:
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if (k > 0) {
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pastVal[c] -= maxShiftedValue[states[r-k][c]];
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pastVal[c] *= base;
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pastVal[c] += states[r][c];
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}
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sourceVal[c] -= maxShiftedSourceValue[states[r-sourceHistoryEmbedLength][sourceAgent]];
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sourceVal[c] *= base;
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sourceVal[c] += states[r][sourceAgent];
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}
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}
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}
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/**
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* Add observations in to our estimates of the pdfs.
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* This call suitable only for homogeneous agents, as all
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* agents will contribute to single pdfs.
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*
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* @param states 1st index is time, 2nd and 3rd index give the 2D agent number
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* @param h - number of rows to compute transfer entropy across
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* @param j - number of columns to compute transfer entropy across
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* (i.e. src (g-h,i-j), dest (g,i): transfer is h cells down, j cells to the right)
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*/
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public void addObservations(int states[][][], int h, int j) {
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int timeSteps = states.length;
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if (timeSteps == 0) {
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return;
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}
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int agentRows = states[0].length;
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if (agentRows == 0) {
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return;
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}
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int agentColumns = states[0][0].length;
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// increment the count of observations:
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if (periodicBoundaryConditions) {
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observations += (timeSteps - startObservationTime) * agentRows * agentColumns;
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} else {
|
|
observations += (timeSteps - startObservationTime) * (agentRows - Math.abs(h)) * (agentColumns - Math.abs(j));
|
|
}
|
|
|
|
// Initialise and store the current previous and source value for each agent
|
|
int[][] pastVal = new int[agentRows][agentColumns];
|
|
for (int r = 0; r < agentRows; r++) {
|
|
for (int c = 0; c < agentColumns; c++) {
|
|
pastVal[r][c] = 0;
|
|
for (int p = 0; p < k; p++) {
|
|
pastVal[r][c] *= base;
|
|
pastVal[r][c] += states[startObservationTime - k + p][r][c];
|
|
}
|
|
}
|
|
}
|
|
int[][] sourceVal = new int[agentRows][agentColumns];
|
|
for (int r = 0; r < agentRows; r++) {
|
|
for (int c = 0; c < agentColumns; c++) {
|
|
sourceVal[r][c] = 0;
|
|
int sourceAgentRow = r-h;
|
|
if ((sourceAgentRow < 0) || (sourceAgentRow >= agentRows)) {
|
|
// Source agent is out of bounds unless we are using periodic boundary conditions
|
|
if (periodicBoundaryConditions) {
|
|
sourceAgentRow = (sourceAgentRow+agentRows) % agentRows;
|
|
} else {
|
|
// Don't add this to our observations
|
|
continue;
|
|
}
|
|
}
|
|
int sourceAgentColumn = c-j;
|
|
if ((sourceAgentColumn < 0) || (sourceAgentColumn >= agentColumns)) {
|
|
// Source agent is out of bounds unless we are using periodic boundary conditions
|
|
if (periodicBoundaryConditions) {
|
|
sourceAgentColumn = (sourceAgentColumn+agentColumns) % agentColumns;
|
|
} else {
|
|
// Don't add this to our observations
|
|
continue;
|
|
}
|
|
}
|
|
for (int p = 0; p < sourceHistoryEmbedLength; p++) {
|
|
sourceVal[r][c] *= base;
|
|
sourceVal[r][c] += states[startObservationTime - sourceHistoryEmbedLength + p][sourceAgentRow][sourceAgentColumn];
|
|
}
|
|
}
|
|
}
|
|
|
|
// 1. Count the tuples observed
|
|
int destVal;
|
|
for (int t = startObservationTime; t < timeSteps; t++) {
|
|
for (int r = 0; r < agentRows; r++) {
|
|
for (int c = 0; c < agentColumns; c++) {
|
|
// Add to the count for this particular transition:
|
|
// (cell's assigned as above)
|
|
int sourceAgentRow = r-h;
|
|
if ((sourceAgentRow < 0) || (sourceAgentRow >= agentRows)) {
|
|
// Source agent is out of bounds unless we are using periodic boundary conditions
|
|
if (periodicBoundaryConditions) {
|
|
sourceAgentRow = (sourceAgentRow+agentRows) % agentRows;
|
|
} else {
|
|
// Don't add this to our observations
|
|
continue;
|
|
}
|
|
}
|
|
int sourceAgentColumn = c-j;
|
|
if ((sourceAgentColumn < 0) || (sourceAgentColumn >= agentColumns)) {
|
|
// Source agent is out of bounds unless we are using periodic boundary conditions
|
|
if (periodicBoundaryConditions) {
|
|
sourceAgentColumn = (sourceAgentColumn+agentColumns) % agentColumns;
|
|
} else {
|
|
// Don't add this to our observations
|
|
continue;
|
|
}
|
|
}
|
|
destVal = states[t][r][c];
|
|
sourceNextPastCount[sourceVal[r][c]][destVal][pastVal[r][c]]++;
|
|
sourcePastCount[sourceVal[r][c]][pastVal[r][c]]++;
|
|
nextPastCount[destVal][pastVal[r][c]]++;
|
|
pastCount[pastVal[r][c]]++;
|
|
nextCount[destVal]++;
|
|
// Update the previous value:
|
|
if (k > 0) {
|
|
pastVal[r][c] -= maxShiftedValue[states[t-k][r][c]];
|
|
pastVal[r][c] *= base;
|
|
pastVal[r][c] += states[t][r][c];
|
|
}
|
|
sourceVal[r][c] -= maxShiftedSourceValue[states[t-sourceHistoryEmbedLength][sourceAgentRow][sourceAgentColumn]];
|
|
sourceVal[r][c] *= base;
|
|
sourceVal[r][c] += states[t][sourceAgentRow][sourceAgentColumn];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Add observations for a single source-destination pair of the multi-agent system
|
|
* to our estimates of the pdfs.
|
|
* This call should be made as opposed to addObservations(int states[][])
|
|
* for computing active info for heterogeneous agents.
|
|
*
|
|
* @param states 1st index is time, 2nd index is agent number
|
|
* @param sourceIndex source agent index
|
|
* @param destIndex destination agent index
|
|
*/
|
|
public void addObservations(int states[][], int sourceIndex, int destIndex) {
|
|
int rows = states.length;
|
|
// increment the count of observations:
|
|
observations += (rows - startObservationTime);
|
|
|
|
// Initialise and store the current previous value for each column
|
|
int pastVal = 0;
|
|
for (int p = 0; p < k; p++) {
|
|
pastVal *= base;
|
|
pastVal += states[startObservationTime - k + p][destIndex];
|
|
}
|
|
int sourceVal = 0;
|
|
for (int p = 0; p < sourceHistoryEmbedLength; p++) {
|
|
sourceVal *= base;
|
|
sourceVal += states[startObservationTime - sourceHistoryEmbedLength + p][sourceIndex];
|
|
}
|
|
|
|
// 1. Count the tuples observed
|
|
int destVal;
|
|
for (int r = startObservationTime; r < rows; r++) {
|
|
// Add to the count for this particular transition:
|
|
// (cell's assigned as above)
|
|
destVal = states[r][destIndex];
|
|
sourceNextPastCount[sourceVal][destVal][pastVal]++;
|
|
sourcePastCount[sourceVal][pastVal]++;
|
|
nextPastCount[destVal][pastVal]++;
|
|
pastCount[pastVal]++;
|
|
nextCount[destVal]++;
|
|
// Update the previous value:
|
|
if (k > 0) {
|
|
pastVal -= maxShiftedValue[states[r-k][destIndex]];
|
|
pastVal *= base;
|
|
pastVal += states[r][destIndex];
|
|
}
|
|
sourceVal -= maxShiftedSourceValue[states[r-sourceHistoryEmbedLength][sourceIndex]];
|
|
sourceVal *= base;
|
|
sourceVal += states[r][sourceIndex];
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Add observations for a single source-destination pair of the multi-agent system
|
|
* to our estimates of the pdfs.
|
|
* This call should be made as opposed to addObservations(int states[][])
|
|
* for computing active info for heterogeneous agents.
|
|
*
|
|
* @param states 1st index is time, 2nd and 3rd index give the 2D agent number
|
|
* @param sourceRowIndex source agent row index
|
|
* @param sourceColumnIndex source agent column index
|
|
* @param destRowIndex destination agent row index
|
|
* @param destColumnIndex destination agent column index
|
|
*/
|
|
public void addObservations(int states[][][], int sourceRowIndex, int sourceColumnIndex,
|
|
int destRowIndex, int destColumnIndex) {
|
|
int timeSteps = states.length;
|
|
// increment the count of observations:
|
|
observations += (timeSteps - startObservationTime);
|
|
|
|
// Initialise and store the current previous value for each column
|
|
int pastVal = 0;
|
|
for (int p = 0; p < k; p++) {
|
|
pastVal *= base;
|
|
pastVal += states[startObservationTime - k + p][destRowIndex][destColumnIndex];
|
|
}
|
|
int sourceVal = 0;
|
|
for (int p = 0; p < sourceHistoryEmbedLength; p++) {
|
|
sourceVal *= base;
|
|
sourceVal += states[startObservationTime - sourceHistoryEmbedLength + p][sourceRowIndex][sourceColumnIndex];
|
|
}
|
|
|
|
// 1. Count the tuples observed
|
|
int destVal;
|
|
for (int r = startObservationTime; r < timeSteps; r++) {
|
|
// Add to the count for this particular transition:
|
|
// (cell's assigned as above)
|
|
destVal = states[r][destRowIndex][destColumnIndex];
|
|
sourceNextPastCount[sourceVal][destVal][pastVal]++;
|
|
sourcePastCount[sourceVal][pastVal]++;
|
|
nextPastCount[destVal][pastVal]++;
|
|
pastCount[pastVal]++;
|
|
nextCount[destVal]++;
|
|
// Update the previous value:
|
|
if (k > 0) {
|
|
pastVal -= maxShiftedValue[states[r-k][destRowIndex][destColumnIndex]];
|
|
pastVal *= base;
|
|
pastVal += states[r][destRowIndex][destColumnIndex];
|
|
}
|
|
sourceVal -= maxShiftedSourceValue[states[r-sourceHistoryEmbedLength][sourceRowIndex][sourceColumnIndex]];
|
|
sourceVal *= base;
|
|
sourceVal += states[r][sourceRowIndex][sourceColumnIndex];
|
|
}
|
|
}
|
|
|
|
/**
|
|
*
|
|
* Returns the count of observations of the past given state dest[n]^k.
|
|
* The past state is indicated by a discrete integer representing the joint variable
|
|
* of the k past states: (dest[n-k+1],dest[n-k+2],...,dest[n-1],dest[n]).
|
|
* The integer is computed as:<br/>
|
|
* pastVal = dest[n-k+1] * base^(k-1) + dest[n-k+2] * base^(k-2) + ... + dest[n-1] * base + dest[n]
|
|
*
|
|
*
|
|
* @param pastVal joint state of the past of the destination dest[n]^k
|
|
* @return count of observations of the given past state
|
|
*/
|
|
public int getPastCount(int pastVal) {
|
|
return pastCount[pastVal];
|
|
}
|
|
|
|
/**
|
|
*
|
|
* @see {@link #getPastCount(int)} for how the joint value representing the past is calculated.
|
|
* @param pastVal joint state of the past of the destination dest[n]^k.
|
|
* @return probability of the given past state
|
|
*/
|
|
public double getPastProbability(int pastVal) {
|
|
return (double) pastCount[pastVal] / (double) observations;
|
|
}
|
|
|
|
/**
|
|
*
|
|
* Returns the count of observations of the past given state dest[n]^k and next state dest[n+1].
|
|
*
|
|
* @see {@link #getPastCount(int)} for how the joint value representing the past is calculated.
|
|
*
|
|
* @param destVal next state of the destination dest[n+1]
|
|
* @param pastVal joint state of the past of the destination dest[n]^k
|
|
* @return count of observations of the given past state and next state
|
|
*/
|
|
public int getNextPastCount(int destVal, int pastVal) {
|
|
return nextPastCount[destVal][pastVal];
|
|
}
|
|
|
|
/**
|
|
*
|
|
* @param destVal next state of the destination dest[n+1]
|
|
* @param pastVal joint state of the past of the destination dest[n]^k
|
|
* @return probability of the given past state and next state
|
|
*/
|
|
public double getNextPastProbability(int destVal, int pastVal) {
|
|
return (double) nextPastCount[destVal][pastVal] / (double) observations;
|
|
}
|
|
|
|
/**
|
|
*
|
|
* Returns the count of observations of the past given state dest[n]^k and the source state source[n]^l.
|
|
* @see {@link #getPastCount(int)} for how the joint values representing the past are calculated.
|
|
*
|
|
* @param sourceVal joint state of the source source[n]^l
|
|
* @param pastVal joint state of the past of the destination dest[n]^k
|
|
* @return count of observations of the given past state and the source state
|
|
*/
|
|
public int getSourcePastCount(int sourceVal, int pastVal) {
|
|
return sourcePastCount[sourceVal][pastVal];
|
|
}
|
|
|
|
/**
|
|
*
|
|
* @see {@link #getPastCount(int)} for how the joint values representing the past are calculated.
|
|
* @param sourceVal joint state of the source source[n]^l
|
|
* @param pastVal joint state of the past of the destination dest[n]^k
|
|
* @return probability of the given past state and the source state
|
|
*/
|
|
public double getSourcePastProbability(int sourceVal, int pastVal) {
|
|
return (double) sourcePastCount[sourceVal][pastVal] / (double) observations;
|
|
}
|
|
|
|
/**
|
|
*
|
|
* Returns the count of observations of the past given state dest[n]^k,
|
|
* the next state of the destination dest[n+1] and the source state source[n]^l.
|
|
* @see {@link #getPastCount(int)} for how the joint values representing the past are calculated.
|
|
*
|
|
* @param sourceVal state of the source source[n]
|
|
* @param nextVal next state of the destination dest[n+1]
|
|
* @param pastVal joint state of the past of the destination dest[n]^k
|
|
* @return count of observations of the given past state, next state of destination and the source state
|
|
*/
|
|
public int getSourceNextPastCount(int sourceVal, int destVal, int pastVal) {
|
|
return sourceNextPastCount[sourceVal][destVal][pastVal];
|
|
}
|
|
|
|
/**
|
|
*
|
|
* @see {@link #getPastCount(int)} for how the joint values representing the past are calculated.
|
|
* @param sourceVal state of the source source[n]^l
|
|
* @param nextVal next state of the destination dest[n+1]
|
|
* @param pastVal joint state of the past of the destination dest[n]^k
|
|
* @return probability of the given past state, next state of destination and the source state
|
|
*/
|
|
public double getSourceNextPastProbability(int sourceVal, int destVal, int pastVal) {
|
|
return (double) sourceNextPastCount[sourceVal][destVal][pastVal] / (double) observations;
|
|
}
|
|
|
|
/**
|
|
*
|
|
* Returns the count of observations of the next state dest[n+1].
|
|
*
|
|
* @param nextVal next state of the destination dest[n+1]
|
|
* @return count of observations of the given next state
|
|
*/
|
|
public int getNextCount(int destVal) {
|
|
return nextCount[destVal];
|
|
}
|
|
|
|
/**
|
|
*
|
|
* @param nextVal state of the next destination dest[n+1]
|
|
* @return probability of the given next state
|
|
*/
|
|
public double getNextProbability(int destVal) {
|
|
return (double) nextCount[destVal] / (double) observations;
|
|
}
|
|
|
|
/**
|
|
* Returns the average local transfer entropy from
|
|
* the observed values which have been passed in previously.
|
|
*
|
|
* @return
|
|
*/
|
|
public double computeAverageLocalOfObservations() {
|
|
double te = 0.0;
|
|
double teCont = 0.0;
|
|
|
|
max = 0;
|
|
min = 0;
|
|
double meanSqLocals = 0;
|
|
for (int pastVal = 0; pastVal < base_power_k; pastVal++) {
|
|
// compute p(past)
|
|
// double p_past = (double) pastCount[pastVal] / (double) observations;
|
|
for (int destVal = 0; destVal < base; destVal++) {
|
|
// compute p(dest,past)
|
|
// double p_dest_past = (double) destPastCount[destVal][pastVal] / (double) observations;
|
|
for (int sourceVal = 0; sourceVal < base_power_l; sourceVal++) {
|
|
// compute p(source,dest,past)
|
|
double p_source_dest_past = (double) sourceNextPastCount[sourceVal][destVal][pastVal] / (double) observations;
|
|
// compute p(source,past)
|
|
// double p_source_past = (double) sourcePastCount[sourceVal][pastVal] / (double) observations;
|
|
// Compute TE contribution:
|
|
if (sourceNextPastCount[sourceVal][destVal][pastVal] != 0) {
|
|
/* Double check: should never happen
|
|
if ((sourcePastCount[sourceVal][pastVal] == 0) ||
|
|
(destPastCount[destVal][pastVal] == 0) ||
|
|
(pastCount[pastVal] == 0)) {
|
|
throw new RuntimeException("one subcount was zero!!");
|
|
}
|
|
*/
|
|
|
|
double logTerm = ((double) sourceNextPastCount[sourceVal][destVal][pastVal] / (double) sourcePastCount[sourceVal][pastVal]) /
|
|
((double) nextPastCount[destVal][pastVal] / (double) pastCount[pastVal]);
|
|
double localValue = Math.log(logTerm) / log_2;
|
|
teCont = p_source_dest_past * localValue;
|
|
if (localValue > max) {
|
|
max = localValue;
|
|
} else if (localValue < min) {
|
|
min = localValue;
|
|
}
|
|
// Add this contribution to the mean
|
|
// of the squared local values
|
|
meanSqLocals += teCont * localValue;
|
|
} else {
|
|
teCont = 0.0;
|
|
}
|
|
te += teCont;
|
|
}
|
|
}
|
|
}
|
|
|
|
average = te;
|
|
std = Math.sqrt(meanSqLocals - average * average);
|
|
estimateComputed = true;
|
|
return te;
|
|
}
|
|
|
|
/**
|
|
* Returns the average active information storage from
|
|
* the observed values which have been passed in previously.
|
|
*
|
|
* @return
|
|
*/
|
|
public double computeAverageActiveInfoStorageOfObservations() {
|
|
double active = 0.0;
|
|
double activeCont = 0.0;
|
|
|
|
for (int nextVal = 0; nextVal < base; nextVal++) {
|
|
// compute p_next
|
|
double p_next = (double) nextCount[nextVal] / (double) observations;
|
|
for (int prevVal = 0; prevVal < base_power_k; prevVal++) {
|
|
// Compute MI contribution:
|
|
if (nextPastCount[nextVal][prevVal] != 0) {
|
|
double logTerm = (double) nextPastCount[nextVal][prevVal] /
|
|
(double) pastCount[prevVal] /
|
|
p_next;
|
|
double localValue = Math.log(logTerm) / log_2;
|
|
activeCont = (nextPastCount[nextVal][prevVal] /
|
|
(double) observations) * localValue;
|
|
} else {
|
|
activeCont = 0.0;
|
|
}
|
|
active += activeCont;
|
|
}
|
|
}
|
|
|
|
return active;
|
|
}
|
|
|
|
/**
|
|
* Dump a debug print of the PDFs of our observations
|
|
*/
|
|
public void debugPrintObservations() {
|
|
|
|
System.out.println("Src\tDst\tPast\tc(s,d,p)\tc(s,p)\tc(d,p)\tc(p)");
|
|
for (int pastVal = 0; pastVal < base_power_k; pastVal++) {
|
|
for (int destVal = 0; destVal < base; destVal++) {
|
|
for (int sourceVal = 0; sourceVal < base_power_l; sourceVal++) {
|
|
// Compute TE contribution:
|
|
System.out.println(sourceVal + "\t" + destVal + "\t" + pastVal + "\t" +
|
|
sourceNextPastCount[sourceVal][destVal][pastVal] + "\t\t" +
|
|
sourcePastCount[sourceVal][pastVal] + "\t" +
|
|
nextPastCount[destVal][pastVal] + "\t" +
|
|
pastCount[pastVal]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Compute the significance of obtaining the given average TE from the given observations
|
|
*
|
|
* This is as per Chavez et. al., "Statistical assessment of nonlinear causality:
|
|
* application to epileptic EEG signals", Journal of Neuroscience Methods 124 (2003) 113-128.
|
|
*
|
|
* @param numPermutationsToCheck number of new orderings of the source values to compare against
|
|
* @return
|
|
*/
|
|
public EmpiricalMeasurementDistribution computeSignificance(int numPermutationsToCheck) {
|
|
double actualTE = computeAverageLocalOfObservations();
|
|
|
|
// Reconstruct the *joint* source values (not necessarily in order, but using joint values retains their l-tuples)
|
|
int[] sourceValues = new int[observations];
|
|
int t_s = 0;
|
|
for (int sourceVal = 0; sourceVal < base_power_l; sourceVal++) {
|
|
// Count up the number of times this joint source value was observed:
|
|
int numberOfSamples = 0;
|
|
for (int pastVal = 0; pastVal < base_power_k; pastVal++) {
|
|
numberOfSamples += sourcePastCount[sourceVal][pastVal];
|
|
}
|
|
// Now add all of these as unordered observations:
|
|
MatrixUtils.fill(sourceValues, sourceVal, t_s, numberOfSamples);
|
|
t_s += numberOfSamples;
|
|
}
|
|
|
|
// And construct unordered (dest,past) tuples.
|
|
// It doesn't matter that we've appeared to destroy the ordering here because
|
|
// the joint distribution nextPastCount is actually preserved in
|
|
// our construction of pastVal and destValues together here.
|
|
int[] destValues = new int[observations];
|
|
int[] pastValues = new int[observations];
|
|
int t_d = 0;
|
|
int t_p = 0;
|
|
for (int pastVal = 0; pastVal < base_power_k; pastVal++) {
|
|
MatrixUtils.fill(pastValues, pastVal, t_p, pastCount[pastVal]);
|
|
t_p += pastCount[pastVal];
|
|
for (int destVal = 0; destVal < base; destVal++) {
|
|
MatrixUtils.fill(destValues, destVal, t_d, nextPastCount[destVal][pastVal]);
|
|
t_d += nextPastCount[destVal][pastVal];
|
|
}
|
|
}
|
|
|
|
// Construct new source orderings based on the source probabilities only
|
|
// Generate the re-ordered indices:
|
|
RandomGenerator rg = new RandomGenerator();
|
|
// (Not necessary to check for distinct random perturbations)
|
|
int[][] newOrderings = rg.generateRandomPerturbations(observations, numPermutationsToCheck);
|
|
|
|
// TODO The use of base_power_l as the base for all variables is particularly wasteful
|
|
// of resources here, but there's not much other choice. A better solution
|
|
// will come when we switch to an underlying conditional MI calculator, with separate
|
|
// bases for each variable, and just use its computeSignificance() method.
|
|
TransferEntropyCalculator ate2 = new TransferEntropyCalculator(base_power_l, k, 1);
|
|
ate2.initialise();
|
|
ate2.observations = observations;
|
|
ate2.pastCount = pastCount;
|
|
ate2.nextPastCount = nextPastCount;
|
|
int countWhereTeIsMoreSignificantThanOriginal = 0;
|
|
EmpiricalMeasurementDistribution measDistribution = new EmpiricalMeasurementDistribution(numPermutationsToCheck);
|
|
for (int p = 0; p < numPermutationsToCheck; p++) {
|
|
// Generate a new re-ordered data set for the source
|
|
int[] newSourceData = MatrixUtils.extractSelectedTimePoints(sourceValues, newOrderings[p]);
|
|
// compute the joint probability distributions
|
|
MatrixUtils.fill(ate2.sourceNextPastCount, 0);
|
|
MatrixUtils.fill(ate2.sourcePastCount, 0);
|
|
for (int t = 0; t < observations; t++) {
|
|
// This looks like we're doing no source embedding --
|
|
// but we're already using joint source values in newSourceData
|
|
ate2.sourcePastCount[newSourceData[t]][pastValues[t]]++;
|
|
ate2.sourceNextPastCount[newSourceData[t]][destValues[t]][pastValues[t]]++;
|
|
}
|
|
// And get a TE value for this realisation:
|
|
double newTe = ate2.computeAverageLocalOfObservations();
|
|
measDistribution.distribution[p] = newTe;
|
|
if (newTe >= actualTE) {
|
|
countWhereTeIsMoreSignificantThanOriginal++;
|
|
}
|
|
|
|
}
|
|
|
|
// And return the significance
|
|
measDistribution.pValue = (double) countWhereTeIsMoreSignificantThanOriginal / (double) numPermutationsToCheck;
|
|
measDistribution.actualValue = actualTE;
|
|
return measDistribution;
|
|
}
|
|
|
|
@Override
|
|
public AnalyticMeasurementDistribution computeSignificance()
|
|
throws Exception {
|
|
if (!estimateComputed) {
|
|
computeAverageLocalOfObservations();
|
|
}
|
|
return new ChiSquareMeasurementDistribution(2.0*((double)observations)*average,
|
|
(sourceHistoryEmbedLength*base - 1)*(base - 1)*(k*base));
|
|
}
|
|
|
|
/**
|
|
* Computes local transfer entropy for the given values
|
|
* @param sourceCurrent
|
|
* @param destNext
|
|
* @param destPast
|
|
*
|
|
* @see {@link #getPastCount(int)} for how the joint values representing the past are calculated.
|
|
* @return
|
|
*/
|
|
public double computeLocalFromPreviousObservations(int sourceCurrent, int destNext, int destPast){
|
|
|
|
double logTerm = ((double) sourceNextPastCount[sourceCurrent][destNext][destPast] / (double) sourcePastCount[sourceCurrent][destPast]) /
|
|
((double) nextPastCount[destNext][destPast] / (double) pastCount[destPast]);
|
|
return Math.log(logTerm) / log_2;
|
|
}
|
|
|
|
/**
|
|
* Computes local apparent transfer entropy for the given
|
|
* states, using pdfs built up from observations previously
|
|
* sent in via the addObservations method.
|
|
* This method to be used for homogeneous agents only
|
|
* @param states 1st index is time, 2nd index is agent number
|
|
*
|
|
* @return
|
|
*/
|
|
public double[] computeLocalFromPreviousObservations(int sourceStates[], int destStates[]){
|
|
int timeSteps = destStates.length;
|
|
|
|
// Allocate for all rows even though we'll leave the first ones as zeros
|
|
double[] localTE = new double[timeSteps];
|
|
average = 0;
|
|
max = 0;
|
|
min = 0;
|
|
|
|
// Initialise and store the current previous value for each column
|
|
int pastVal = 0;
|
|
for (int p = 0; p < k; p++) {
|
|
pastVal *= base;
|
|
pastVal += destStates[startObservationTime - k + p];
|
|
}
|
|
int sourceVal = 0;
|
|
for (int p = 0; p < sourceHistoryEmbedLength; p++) {
|
|
sourceVal *= base;
|
|
sourceVal += sourceStates[startObservationTime - sourceHistoryEmbedLength + p];
|
|
}
|
|
int destVal;
|
|
double logTerm;
|
|
for (int t = startObservationTime; t < timeSteps; t++) {
|
|
destVal = destStates[t];
|
|
// Now compute the local value
|
|
logTerm = ((double) sourceNextPastCount[sourceVal][destVal][pastVal] / (double) sourcePastCount[sourceVal][pastVal]) /
|
|
((double) nextPastCount[destVal][pastVal] / (double) pastCount[pastVal]);
|
|
localTE[t] = Math.log(logTerm) / log_2;
|
|
average += localTE[t];
|
|
if (localTE[t] > max) {
|
|
max = localTE[t];
|
|
} else if (localTE[t] < min) {
|
|
min = localTE[t];
|
|
}
|
|
// Update the previous value:
|
|
if (k > 0) {
|
|
pastVal -= maxShiftedValue[destStates[t-k]];
|
|
pastVal *= base;
|
|
pastVal += destStates[t];
|
|
}
|
|
sourceVal -= maxShiftedSourceValue[sourceStates[t-sourceHistoryEmbedLength]];
|
|
sourceVal *= base;
|
|
sourceVal += sourceStates[t];
|
|
}
|
|
|
|
average = average/(double) (timeSteps - startObservationTime);
|
|
|
|
return localTE;
|
|
}
|
|
|
|
/**
|
|
* Computes local transfer for the given
|
|
* states, using pdfs built up from observations previously
|
|
* sent in via the addObservations method.
|
|
* This method to be used for homogeneous agents only
|
|
*
|
|
* @param states 1st index is time, 2nd index is agent number
|
|
* @param j columns across which to compute TE
|
|
* @return
|
|
*/
|
|
public double[][] computeLocalFromPreviousObservations(int states[][], int j){
|
|
int timeSteps = states.length;
|
|
int agents = states[0].length;
|
|
|
|
// Allocate for all rows even though we'll leave the first ones as zeros
|
|
double[][] localTE = new double[timeSteps][agents];
|
|
average = 0;
|
|
max = 0;
|
|
min = 0;
|
|
|
|
// Initialise and store the current previous value for each column
|
|
int[] pastVal = new int[agents];
|
|
for (int c = 0; c < agents; c++) {
|
|
pastVal[c] = 0;
|
|
for (int p = 0; p < k; p++) {
|
|
pastVal[c] *= base;
|
|
pastVal[c] += states[startObservationTime - k + p][c];
|
|
}
|
|
}
|
|
int[] sourceVal = new int[agents];
|
|
for (int c = 0; c < agents; c++) {
|
|
sourceVal[c] = 0;
|
|
int sourceAgentIndex = c-j;
|
|
if ((sourceAgentIndex < 0) || (sourceAgentIndex >= agents)) {
|
|
// Source agent is out of bounds unless we are using periodic boundary conditions
|
|
if (periodicBoundaryConditions) {
|
|
sourceAgentIndex = (sourceAgentIndex+agents) % agents;
|
|
} else {
|
|
// Don't add this to our observations
|
|
continue;
|
|
}
|
|
}
|
|
for (int p = 0; p < sourceHistoryEmbedLength; p++) {
|
|
sourceVal[c] *= base;
|
|
sourceVal[c] += states[startObservationTime - sourceHistoryEmbedLength + p][sourceAgentIndex];
|
|
}
|
|
}
|
|
int destVal;
|
|
double logTerm;
|
|
for (int t = startObservationTime; t < timeSteps; t++) {
|
|
for (int c = 0; c < agents; c++) {
|
|
int sourceAgentIndex = c-j;
|
|
if ((sourceAgentIndex < 0) || (sourceAgentIndex >= agents)) {
|
|
// Source agent is out of bounds unless we are using periodic boundary conditions
|
|
if (periodicBoundaryConditions) {
|
|
sourceAgentIndex = (sourceAgentIndex+agents) % agents;
|
|
} else {
|
|
// Don't compute a local value for this one
|
|
continue;
|
|
}
|
|
}
|
|
destVal = states[t][c];
|
|
// Now compute the local value
|
|
logTerm = ((double) sourceNextPastCount[sourceVal[c]][destVal][pastVal[c]] / (double) sourcePastCount[sourceVal[c]][pastVal[c]]) /
|
|
((double) nextPastCount[destVal][pastVal[c]] / (double) pastCount[pastVal[c]]);
|
|
localTE[t][c] = Math.log(logTerm) / log_2;
|
|
average += localTE[t][c];
|
|
if (localTE[t][c] > max) {
|
|
max = localTE[t][c];
|
|
} else if (localTE[t][c] < min) {
|
|
min = localTE[t][c];
|
|
}
|
|
// Update the previous value:
|
|
if (k > 0) {
|
|
pastVal[c] -= maxShiftedValue[states[t-k][c]];
|
|
pastVal[c] *= base;
|
|
pastVal[c] += states[t][c];
|
|
}
|
|
sourceVal[c] -= maxShiftedSourceValue[states[t-sourceHistoryEmbedLength][sourceAgentIndex]];
|
|
sourceVal[c] *= base;
|
|
sourceVal[c] += states[t][sourceAgentIndex];
|
|
}
|
|
}
|
|
|
|
if (periodicBoundaryConditions) {
|
|
average = average/(double) ((timeSteps - startObservationTime) * agents);
|
|
} else {
|
|
average = average/(double) ((timeSteps - startObservationTime) * (agents - Math.abs(j)));
|
|
}
|
|
|
|
return localTE;
|
|
}
|
|
|
|
/**
|
|
* Computes local transfer for the given
|
|
* states, using pdfs built up from observations previously
|
|
* sent in via the addObservations method.
|
|
* This method to be used for homogeneous agents only
|
|
*
|
|
* @param states 1st index is time, 2nd and 3rd index give the 2D agent number
|
|
* @param h - number of rows to compute transfer entropy across
|
|
* @param j - number of columns to compute transfer entropy across
|
|
* (i.e. src (g-h,i-j), dest (g,i): transfer is h cells down, j cells to the right)
|
|
* @return
|
|
*/
|
|
public double[][][] computeLocalFromPreviousObservations(int states[][][], int h, int j){
|
|
int timeSteps = states.length;
|
|
int agentRows = states[0].length;
|
|
int agentColumns = states[0][0].length;
|
|
|
|
// Allocate for all rows even though we'll leave the first ones as zeros
|
|
double[][][] localTE = new double[timeSteps][agentRows][agentColumns];
|
|
average = 0;
|
|
max = 0;
|
|
min = 0;
|
|
|
|
// Initialise and store the current previous value for each column
|
|
int[][] pastVal = new int[agentRows][agentColumns];
|
|
for (int r = 0; r < agentRows; r++){
|
|
for (int c = 0; c < agentColumns; c++) {
|
|
pastVal[r][c] = 0;
|
|
for (int p = 0; p < k; p++) {
|
|
pastVal[r][c] *= base;
|
|
pastVal[r][c] += states[startObservationTime - k + p][r][c];
|
|
}
|
|
}
|
|
}
|
|
int[][] sourceVal = new int[agentRows][agentColumns];
|
|
for (int r = 0; r < agentRows; r++) {
|
|
for (int c = 0; c < agentColumns; c++) {
|
|
sourceVal[r][c] = 0;
|
|
int sourceAgentRow = r-h;
|
|
if ((sourceAgentRow < 0) || (sourceAgentRow >= agentRows)) {
|
|
// Source agent is out of bounds unless we are using periodic boundary conditions
|
|
if (periodicBoundaryConditions) {
|
|
sourceAgentRow = (sourceAgentRow+agentRows) % agentRows;
|
|
} else {
|
|
// Don't add this to our observations
|
|
continue;
|
|
}
|
|
}
|
|
int sourceAgentColumn = c-j;
|
|
if ((sourceAgentColumn < 0) || (sourceAgentColumn >= agentColumns)) {
|
|
// Source agent is out of bounds unless we are using periodic boundary conditions
|
|
if (periodicBoundaryConditions) {
|
|
sourceAgentColumn = (sourceAgentColumn+agentColumns) % agentColumns;
|
|
} else {
|
|
// Don't add this to our observations
|
|
continue;
|
|
}
|
|
}
|
|
for (int p = 0; p < sourceHistoryEmbedLength; p++) {
|
|
sourceVal[r][c] *= base;
|
|
sourceVal[r][c] += states[startObservationTime - sourceHistoryEmbedLength + p][sourceAgentRow][sourceAgentColumn];
|
|
}
|
|
}
|
|
}
|
|
|
|
int destVal;
|
|
double logTerm;
|
|
for (int t = startObservationTime; t < timeSteps; t++) {
|
|
for (int r = 0; r < agentRows; r++) {
|
|
for (int c = 0; c < agentColumns; c++) {
|
|
int sourceAgentRow = r-h;
|
|
if ((sourceAgentRow < 0) || (sourceAgentRow >= agentRows)) {
|
|
// Source agent is out of bounds unless we are using periodic boundary conditions
|
|
if (periodicBoundaryConditions) {
|
|
sourceAgentRow = (sourceAgentRow+agentRows) % agentRows;
|
|
} else {
|
|
// Don't compute a local value for this one
|
|
continue;
|
|
}
|
|
}
|
|
int sourceAgentColumn = c-j;
|
|
if ((sourceAgentColumn < 0) || (sourceAgentColumn >= agentColumns)) {
|
|
// Source agent is out of bounds unless we are using periodic boundary conditions
|
|
if (periodicBoundaryConditions) {
|
|
sourceAgentColumn = (sourceAgentColumn+agentColumns) % agentColumns;
|
|
} else {
|
|
// Don't compute a local value for this one
|
|
continue;
|
|
}
|
|
}
|
|
destVal = states[t][r][c];
|
|
// Now compute the local value
|
|
logTerm = ((double) sourceNextPastCount[sourceVal[r][c]][destVal][pastVal[r][c]] / (double) sourcePastCount[sourceVal[r][c]][pastVal[r][c]]) /
|
|
((double) nextPastCount[destVal][pastVal[r][c]] / (double) pastCount[pastVal[r][c]]);
|
|
localTE[t][r][c] = Math.log(logTerm) / log_2;
|
|
average += localTE[t][r][c];
|
|
if (localTE[t][r][c] > max) {
|
|
max = localTE[t][r][c];
|
|
} else if (localTE[t][r][c] < min) {
|
|
min = localTE[t][r][c];
|
|
}
|
|
// Update the previous value:
|
|
if (k > 0) {
|
|
pastVal[r][c] -= maxShiftedValue[states[t-k][r][c]];
|
|
pastVal[r][c] *= base;
|
|
pastVal[r][c] += states[t][r][c];
|
|
}
|
|
sourceVal[r][c] -= maxShiftedSourceValue[states[t-sourceHistoryEmbedLength][sourceAgentRow][sourceAgentColumn]];
|
|
sourceVal[r][c] *= base;
|
|
sourceVal[r][c] += states[t][sourceAgentRow][sourceAgentColumn];
|
|
}
|
|
}
|
|
}
|
|
|
|
if (periodicBoundaryConditions) {
|
|
average = average/(double) ((timeSteps - startObservationTime) * agentRows * agentColumns);
|
|
} else {
|
|
average = average/(double) ((timeSteps - startObservationTime) * (agentRows - Math.abs(h)) *
|
|
(agentColumns - Math.abs(j)));
|
|
}
|
|
|
|
return localTE;
|
|
}
|
|
|
|
/**
|
|
* Computes local transfer for the given
|
|
* states, using pdfs built up from observations previously
|
|
* sent in via the addObservations method.
|
|
* This method is suitable for heterogeneous agents
|
|
*
|
|
* @param states 1st index is time, 2nd index is agent number
|
|
* @return
|
|
*/
|
|
public double[] computeLocalFromPreviousObservations(int states[][], int sourceCol, int destCol){
|
|
int rows = states.length;
|
|
// int columns = states[0].length;
|
|
|
|
// Allocate for all rows even though we'll leave the first ones as zeros
|
|
double[] localTE = new double[rows];
|
|
average = 0;
|
|
max = 0;
|
|
min = 0;
|
|
|
|
// Initialise and store the current previous value for each column
|
|
int pastVal = 0;
|
|
pastVal = 0;
|
|
for (int p = 0; p < k; p++) {
|
|
pastVal *= base;
|
|
pastVal += states[startObservationTime - k + p][destCol];
|
|
}
|
|
int sourceVal = 0;
|
|
for (int p = 0; p < sourceHistoryEmbedLength; p++) {
|
|
sourceVal *= base;
|
|
sourceVal += states[startObservationTime - sourceHistoryEmbedLength + p][sourceCol];
|
|
}
|
|
int destVal;
|
|
double logTerm;
|
|
for (int r = startObservationTime; r < rows; r++) {
|
|
destVal = states[r][destCol];
|
|
// Now compute the local value
|
|
logTerm = ((double) sourceNextPastCount[sourceVal][destVal][pastVal] / (double) sourcePastCount[sourceVal][pastVal]) /
|
|
((double) nextPastCount[destVal][pastVal] / (double) pastCount[pastVal]);
|
|
localTE[r] = Math.log(logTerm) / log_2;
|
|
average += localTE[r];
|
|
if (localTE[r] > max) {
|
|
max = localTE[r];
|
|
} else if (localTE[r] < min) {
|
|
min = localTE[r];
|
|
}
|
|
// Update the previous value:
|
|
if (k > 0) {
|
|
pastVal -= maxShiftedValue[states[r-k][destCol]];
|
|
pastVal *= base;
|
|
pastVal += states[r][destCol];
|
|
}
|
|
sourceVal -= maxShiftedSourceValue[states[r-sourceHistoryEmbedLength][sourceCol]];
|
|
sourceVal *= base;
|
|
sourceVal += states[r][sourceCol];
|
|
}
|
|
|
|
average = average/(double) (rows - startObservationTime);
|
|
|
|
return localTE;
|
|
}
|
|
|
|
/**
|
|
* Computes local transfer for the given
|
|
* states, using pdfs built up from observations previously
|
|
* sent in via the addObservations method.
|
|
* This method is suitable for heterogeneous agents
|
|
*
|
|
* @param states 1st index is time, 2nd and 3rd index give the 2D agent number
|
|
* @param sourceRowIndex source agent row index
|
|
* @param sourceColumnIndex source agent column index
|
|
* @param destRowIndex destination agent row index
|
|
* @param destColumnIndex destination agent column index
|
|
* @return
|
|
*/
|
|
public double[] computeLocalFromPreviousObservations(int states[][][],
|
|
int sourceRowIndex, int sourceColumnIndex, int destRowIndex, int destColumnIndex){
|
|
int timeSteps = states.length;
|
|
// int columns = states[0].length;
|
|
|
|
// Allocate for all rows even though we'll leave the first ones as zeros
|
|
double[] localTE = new double[timeSteps];
|
|
average = 0;
|
|
max = 0;
|
|
min = 0;
|
|
|
|
// Initialise and store the current previous value for each column
|
|
int pastVal = 0;
|
|
pastVal = 0;
|
|
for (int p = 0; p < k; p++) {
|
|
pastVal *= base;
|
|
pastVal += states[startObservationTime - k + p][destRowIndex][destColumnIndex];
|
|
}
|
|
int sourceVal = 0;
|
|
for (int p = 0; p < sourceHistoryEmbedLength; p++) {
|
|
sourceVal *= base;
|
|
sourceVal += states[startObservationTime - sourceHistoryEmbedLength + p][sourceRowIndex][sourceColumnIndex];
|
|
}
|
|
int destVal;
|
|
double logTerm;
|
|
for (int r = startObservationTime; r < timeSteps; r++) {
|
|
destVal = states[r][destRowIndex][destColumnIndex];
|
|
// Now compute the local value
|
|
logTerm = ((double) sourceNextPastCount[sourceVal][destVal][pastVal] / (double) sourcePastCount[sourceVal][pastVal]) /
|
|
((double) nextPastCount[destVal][pastVal] / (double) pastCount[pastVal]);
|
|
localTE[r] = Math.log(logTerm) / log_2;
|
|
average += localTE[r];
|
|
if (localTE[r] > max) {
|
|
max = localTE[r];
|
|
} else if (localTE[r] < min) {
|
|
min = localTE[r];
|
|
}
|
|
// Update the previous value:
|
|
if (k > 0) {
|
|
pastVal -= maxShiftedValue[states[r-k][destRowIndex][destColumnIndex]];
|
|
pastVal *= base;
|
|
pastVal += states[r][destRowIndex][destColumnIndex];
|
|
}
|
|
sourceVal -= maxShiftedSourceValue[states[r-sourceHistoryEmbedLength][sourceRowIndex][sourceColumnIndex]];
|
|
sourceVal *= base;
|
|
sourceVal += states[r][sourceRowIndex][sourceColumnIndex];
|
|
}
|
|
|
|
average = average/(double) (timeSteps - startObservationTime);
|
|
|
|
return localTE;
|
|
}
|
|
|
|
/**
|
|
* Standalone routine to
|
|
* compute local transfer entropy between two time series
|
|
* Return a time series of local values.
|
|
* First history rows are zeros
|
|
* @param sourceStates time series of source states
|
|
* @param destStates time series of destination states
|
|
*
|
|
* @return
|
|
*/
|
|
public double[] computeLocal(int sourceStates[], int destStates[]) {
|
|
|
|
initialise();
|
|
addObservations(sourceStates, destStates);
|
|
return computeLocalFromPreviousObservations(sourceStates, destStates);
|
|
}
|
|
|
|
/**
|
|
* Standalone routine to
|
|
* compute local transfer entropy across a 2D spatiotemporal
|
|
* array of the states of homogeneous agents
|
|
* Return a 2D spatiotemporal array of local values.
|
|
* First history rows are zeros
|
|
* This method to be called for homogeneous agents only
|
|
*
|
|
* @param states - 2D array of states
|
|
* @param j number of columns across which to compute the TE
|
|
* @return
|
|
*/
|
|
public double[][] computeLocal(int states[][], int j) {
|
|
|
|
initialise();
|
|
addObservations(states, j);
|
|
return computeLocalFromPreviousObservations(states, j);
|
|
}
|
|
|
|
/**
|
|
* Standalone routine to
|
|
* compute local transfer entropy across a 3D spatiotemporal
|
|
* array of the states of homogeneous agents.
|
|
* Return a 3D spatiotemporal array of local values.
|
|
* First history rows are zeros
|
|
* This method to be called for homogeneous agents only
|
|
*
|
|
* @param states 1st index is time, 2nd and 3rd index give the 2D agent number
|
|
* @param h - number of rows to compute transfer entropy across
|
|
* @param j - number of columns to compute transfer entropy across
|
|
* (i.e. src (g-h,i-j), dest (g,i): transfer is h cells down, j cells to the right)
|
|
* @return
|
|
*/
|
|
public double[][][] computeLocal(int states[][][], int h, int j) {
|
|
|
|
initialise();
|
|
addObservations(states, h, j);
|
|
return computeLocalFromPreviousObservations(states, h, j);
|
|
}
|
|
|
|
/**
|
|
* Standalone routine to
|
|
* compute average local transfer entropy across a 2D spatiotemporal
|
|
* array of the states of homogeneous agents
|
|
* Return the average
|
|
* This method to be called for homogeneous agents only
|
|
*
|
|
* @param states - 2D array of states
|
|
* @param j - TE across j cells to the right
|
|
* @return
|
|
*/
|
|
public double computeAverageLocal(int states[][], int j) {
|
|
|
|
initialise();
|
|
addObservations(states, j);
|
|
return computeAverageLocalOfObservations();
|
|
}
|
|
|
|
/**
|
|
* Standalone routine to
|
|
* compute average local transfer entropy across a 3D spatiotemporal
|
|
* array of the states of homogeneous agents
|
|
* Return the average
|
|
* This method to be called for homogeneous agents only
|
|
*
|
|
* @param states 1st index is time, 2nd and 3rd index give the 2D agent number
|
|
* @param h - number of rows to compute transfer entropy across
|
|
* @param j - number of columns to compute transfer entropy across
|
|
* (i.e. src (g-h,i-j), dest (g,i): transfer is h cells down, j cells to the right)
|
|
* @return
|
|
*/
|
|
public double computeAverageLocal(int states[][][], int h, int j) {
|
|
|
|
initialise();
|
|
addObservations(states, h, j);
|
|
return computeAverageLocalOfObservations();
|
|
}
|
|
|
|
/**
|
|
* Standalone routine to
|
|
* compute local transfer entropy across a 2D spatiotemporal
|
|
* array of the states of homogeneous agents
|
|
* Return a 2D spatiotemporal array of local values.
|
|
* First history rows are zeros
|
|
* This method suitable for heterogeneous agents
|
|
*
|
|
* @param states - 2D array of states
|
|
* @param sourceCol - column index for the source agent
|
|
* @param destCol - column index for the destination agent
|
|
* @return
|
|
*/
|
|
public double[] computeLocal(int states[][], int sourceCol, int destCol) {
|
|
|
|
initialise();
|
|
addObservations(states, sourceCol, destCol);
|
|
return computeLocalFromPreviousObservations(states, sourceCol, destCol);
|
|
}
|
|
|
|
/**
|
|
* Standalone routine to
|
|
* compute local transfer entropy across a 3D spatiotemporal
|
|
* array of the states of homogeneous agents
|
|
* Return a 2D spatiotemporal array of local values.
|
|
* First history rows are zeros
|
|
* This method suitable for heterogeneous agents
|
|
*
|
|
* @param states 1st index is time, 2nd and 3rd index give the 2D agent number
|
|
* @param sourceRowIndex source agent row index
|
|
* @param sourceColumnIndex source agent column index
|
|
* @param destRowIndex destination agent row index
|
|
* @param destColumnIndex destination agent column index
|
|
* @return
|
|
*/
|
|
public double[] computeLocal(int states[][][], int sourceRowIndex, int sourceColumnIndex,
|
|
int destRowIndex, int destColumnIndex) {
|
|
|
|
initialise();
|
|
addObservations(states, sourceRowIndex, sourceColumnIndex, destRowIndex, destColumnIndex);
|
|
return computeLocalFromPreviousObservations(states, sourceRowIndex, sourceColumnIndex,
|
|
destRowIndex, destColumnIndex);
|
|
}
|
|
|
|
/**
|
|
* Standalone routine to
|
|
* compute average local transfer entropy across a 2D spatiotemporal
|
|
* array of the states of homogeneous agents
|
|
* Returns the average
|
|
* This method suitable for heterogeneous agents.
|
|
*
|
|
* @param states - 2D array of states
|
|
* @param sourceCol - column index for the source agent
|
|
* @param destCol - column index for the destination agent
|
|
* @return
|
|
*/
|
|
public double computeAverageLocal(int states[][], int sourceCol, int destCol) {
|
|
|
|
initialise();
|
|
addObservations(states, sourceCol, destCol);
|
|
return computeAverageLocalOfObservations();
|
|
}
|
|
|
|
/**
|
|
* Standalone routine to
|
|
* compute average local transfer entropy across a 3D spatiotemporal
|
|
* array of the states of homogeneous agents
|
|
* Returns the average
|
|
* This method suitable for heterogeneous agents
|
|
*
|
|
* @param states 1st index is time, 2nd and 3rd index give the 2D agent number
|
|
* @param sourceRowIndex source agent row index
|
|
* @param sourceColumnIndex source agent column index
|
|
* @param destRowIndex destination agent row index
|
|
* @param destColumnIndex destination agent column index
|
|
* @return
|
|
*/
|
|
public double computeAverageLocal(int states[][][], int sourceRowIndex, int sourceColumnIndex,
|
|
int destRowIndex, int destColumnIndex) {
|
|
|
|
initialise();
|
|
addObservations(states, sourceRowIndex, sourceColumnIndex, destRowIndex, destColumnIndex);
|
|
return computeAverageLocalOfObservations();
|
|
}
|
|
|
|
public boolean isPeriodicBoundaryConditions() {
|
|
return periodicBoundaryConditions;
|
|
}
|
|
public void setPeriodicBoundaryConditions(boolean periodicBoundaryConditions) {
|
|
this.periodicBoundaryConditions = periodicBoundaryConditions;
|
|
}
|
|
}
|