mirror of https://github.com/jlizier/jidt
126 lines
4.5 KiB
Java
Executable File
126 lines
4.5 KiB
Java
Executable File
/*
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* Java Information Dynamics Toolkit (JIDT)
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* Copyright (C) 2012, Joseph T. Lizier
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*
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* This program is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program. If not, see <http://www.gnu.org/licenses/>.
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*/
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package infodynamics.measures.continuous.kraskov;
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import java.util.Calendar;
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import java.util.PriorityQueue;
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import infodynamics.measures.continuous.MutualInfoCalculatorMultiVariate;
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import infodynamics.utils.MathsUtils;
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import infodynamics.utils.MatrixUtils;
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import infodynamics.utils.NeighbourNodeData;
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import infodynamics.utils.EuclideanUtils;
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/**
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* <p>Computes the differential mutual information of two given multivariate sets of
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* observations (implementing {@link MutualInfoCalculatorMultiVariate}),
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* using Kraskov-Stoegbauer-Grassberger (KSG) estimation (see Kraskov et al., below),
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* <b>algorithm 1</b>.
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* Most of the functionality is defined by the parent class
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* {@link MutualInfoCalculatorMultiVariateKraskov}.</p>
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*
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* <p>Usage is as per the paradigm outlined for {@link MutualInfoCalculatorMultiVariate},
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* and expanded on in {@link MutualInfoCalculatorMultiVariateKraskov}.
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* </p>
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*
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* <p><b>References:</b><br/>
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* <ul>
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* <li>Kraskov, A., Stoegbauer, H., Grassberger, P.,
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* <a href="http://dx.doi.org/10.1103/PhysRevE.69.066138">"Estimating mutual information"</a>,
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* Physical Review E 69, (2004) 066138.</li>
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* </ul>
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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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* @author Ipek Özdemir
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*/
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public class MutualInfoCalculatorMultiVariateKraskov1
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extends MutualInfoCalculatorMultiVariateKraskov {
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public MutualInfoCalculatorMultiVariateKraskov1() {
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super();
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isAlgorithm1 = true;
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}
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@Override
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protected double[] partialComputeFromObservations(
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int startTimePoint, int numTimePoints, boolean returnLocals) throws Exception {
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double startTime = Calendar.getInstance().getTimeInMillis();
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double[] localMi = null;
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if (returnLocals) {
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localMi = new double[numTimePoints];
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}
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// Count the average number of points within eps_x and eps_y of each point
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double sumDiGammas = 0;
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double sumNx = 0;
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double sumNy = 0;
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for (int t = startTimePoint; t < startTimePoint + numTimePoints; t++) {
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// Compute eps for this time step by
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// finding the kth closest neighbour for point t:
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PriorityQueue<NeighbourNodeData> nnPQ =
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kdTreeJoint.findKNearestNeighbours(k, t, dynCorrExclTime);
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// First element in the PQ is the kth NN,
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// and epsilon = kthNnData.distance
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NeighbourNodeData kthNnData = nnPQ.poll();
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// Count the number of points whose x distance is less
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// than eps, and whose y distance is less than
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// epsilon = kthNnData.distance
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int n_x = nnSearcherSource.countPointsStrictlyWithinR(
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t, kthNnData.distance, dynCorrExclTime);
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int n_y = nnSearcherDest.countPointsStrictlyWithinR(
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t, kthNnData.distance, dynCorrExclTime);
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sumNx += n_x;
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sumNy += n_y;
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// And take the digammas:
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double digammaNxPlusOne = MathsUtils.digamma(n_x+1);
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double digammaNyPlusOne = MathsUtils.digamma(n_y+1);
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sumDiGammas += digammaNxPlusOne + digammaNyPlusOne;
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if (returnLocals) {
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// TODO should digammaN be adjusted if we are using
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// dynamic correlation exclusion, since we're not sampling from
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// N after all? Think about this for all Kraskov estimators.
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localMi[t-startTimePoint] = digammaK - digammaNxPlusOne - digammaNyPlusOne + digammaN;
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}
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}
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if (debug) {
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Calendar rightNow2 = Calendar.getInstance();
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long endTime = rightNow2.getTimeInMillis();
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System.out.println("Subset " + startTimePoint + ":" +
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(startTimePoint + numTimePoints) + " Calculation time: " +
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((endTime - startTime)/1000.0) + " sec" );
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}
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// Select what to return:
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if (returnLocals) {
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return localMi;
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} else {
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return new double[] {sumDiGammas, sumNx, sumNy};
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}
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}
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}
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