mirror of https://github.com/jlizier/jidt
236 lines
9.3 KiB
Java
Executable File
236 lines
9.3 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.ConditionalMutualInfoCalculatorMultiVariate;
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import infodynamics.utils.MathsUtils;
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import infodynamics.utils.NeighbourNodeData;
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/**
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* <p>Computes the differential conditional mutual information of two multivariate
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* <code>double[][]</code> sets of observations, conditioned on another
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* (implementing {@link ConditionalMutualInfoCalculatorMultiVariate}),
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* using Kraskov-Stoegbauer-Grassberger (KSG) estimation (see references 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 ConditionalMutualInfoCalculatorMultiVariateKraskov}.</p>
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*
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* <p>Crucially, the calculation is performed by examining
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* neighbours in the full joint space (as specified by Frenzel and Pompe)
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* rather than two MI calculators.</p>
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*
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* <p>Usage is as per the paradigm outlined for {@link ConditionalMutualInfoCalculatorMultiVariate},
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* and expanded on in {@link ConditionalMutualInfoCalculatorMultiVariateKraskov}.
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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>Frenzel and Pompe, <a href="http://dx.doi.org/10.1103/physrevlett.99.204101">
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* "Partial Mutual Information for Coupling Analysis of Multivariate Time Series"</a>,
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* Physical Review Letters, <b>99</b>, p. 204101+ (2007).</li>
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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 ConditionalMutualInfoCalculatorMultiVariateKraskov1
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extends ConditionalMutualInfoCalculatorMultiVariateKraskov {
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public ConditionalMutualInfoCalculatorMultiVariateKraskov1() {
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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(int startTimePoint,
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int numTimePoints, boolean returnLocals) throws Exception {
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double startTime = Calendar.getInstance().getTimeInMillis();
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double[] localCondMi = null;
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if (returnLocals) {
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localCondMi = new double[numTimePoints];
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}
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// Count the average number of points within eps_xz and eps_yz and eps_z of each point
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double sumDiGammas = 0;
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double sumNxz = 0;
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double sumNyz = 0;
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double sumNz = 0;
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long knnTime = 0, conditionalTime = 0,
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conditionalXTime = 0, conditionalYTime = 0;
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// Arrays used for fast searching on conditionals with a marginal:
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boolean[] isWithinRForConditionals = new boolean[totalObservations];
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int[] indicesWithinRForConditionals = new int[totalObservations+1];
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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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long methodStartTime = Calendar.getInstance().getTimeInMillis();
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PriorityQueue<NeighbourNodeData> nnPQ =
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kdTreeJoint.findKNearestNeighbours(k, t);
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knnTime += Calendar.getInstance().getTimeInMillis() -
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methodStartTime;
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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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// Now count the points in the conditional space, and
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// the var1-conditional and var2-conditional spaces.
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// We have 3 coded options for how to do this:
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/* Option A -- straightforward way using each k-d tree separately:
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* To use this, need to construct kdTreeVar1Conditional and
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* kdTreeVar2Conditional regardless of dimensionsVar1 and 2.
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int n_xz = kdTreeVar1Conditional.countPointsStrictlyWithinR(
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t, kthNnData.distance);
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int n_yz = kdTreeVar2Conditional.countPointsStrictlyWithinR(
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t, kthNnData.distance);
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int n_z = nnSearcherConditional.countPointsStrictlyWithinR(
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t, kthNnData.distance);
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*/ // end option A
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/* Option B --
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* Select all points within conditional z, then check x and y norms for
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* these points only. Works better than A if few points qualify for the conditionals
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* (e.g. large multivariate conditional) but not so well for
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* many qualifying points (e.g. low dimensional conditional).
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*
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Collection<NeighbourNodeData> z_pointsWithinR =
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nnSearcherConditional.findPointsStrictlyWithinR(
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t, kthNnData.distance);
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int n_z = z_pointsWithinR.size();
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int n_xz = 0, n_yz = 0;
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for(NeighbourNodeData zNeighbour : z_pointsWithinR) {
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if (KdTree.normWithAbort(
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var1Observations[t],
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var1Observations[zNeighbour.sampleIndex],
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kthNnData.distance, normType) < kthNnData.distance) {
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n_xz++;
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}
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if (KdTree.normWithAbort(
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var2Observations[t],
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var2Observations[zNeighbour.sampleIndex],
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kthNnData.distance, normType) < kthNnData.distance) {
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n_yz++;
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}
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}
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*/ // end option B
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// Option C --
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// Identify the points satisfying the conditional criteria, then use
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// the knowledge of which points made this cut to speed up the searching
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// in the conditional-marginal spaces:
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// 1. Identify the n_z points within the conditional boundaries:
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if (debug) {
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methodStartTime = Calendar.getInstance().getTimeInMillis();
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}
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nnSearcherConditional.findPointsWithinR(t, kthNnData.distance,
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false, isWithinRForConditionals, indicesWithinRForConditionals);
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if (debug) {
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conditionalTime += Calendar.getInstance().getTimeInMillis() -
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methodStartTime;
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methodStartTime = Calendar.getInstance().getTimeInMillis();
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}
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// 2. Then compute n_xz and n_yz harnessing our knowledge of
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// which points qualified for the conditional already:
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int n_xz;
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if (dimensionsVar1 > 1) {
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n_xz = kdTreeVar1Conditional.countPointsWithinR(t, kthNnData.distance,
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false, 1, isWithinRForConditionals);
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} else { // Generally faster to search only the marginal space if it is univariate
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n_xz = uniNNSearcherVar1.countPointsWithinR(t, kthNnData.distance,
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false, isWithinRForConditionals);
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}
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if (debug) {
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conditionalXTime += Calendar.getInstance().getTimeInMillis() -
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methodStartTime;
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methodStartTime = Calendar.getInstance().getTimeInMillis();
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}
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int n_yz;
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if (dimensionsVar2 > 1) {
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n_yz = kdTreeVar2Conditional.countPointsWithinR(t, kthNnData.distance,
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false, 1, isWithinRForConditionals);
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} else { // Generally faster to search only the marginal space if it is univariate
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n_yz = uniNNSearcherVar2.countPointsWithinR(t, kthNnData.distance,
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false, isWithinRForConditionals);
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}
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if (debug) {
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conditionalYTime += Calendar.getInstance().getTimeInMillis() -
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methodStartTime;
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}
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// 3. Finally, reset our boolean array for its next use while we count n_z:
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int n_z;
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for (n_z = 0; indicesWithinRForConditionals[n_z] != -1; n_z++) {
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isWithinRForConditionals[indicesWithinRForConditionals[n_z]] = false;
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}
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// end option C
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sumNxz += n_xz;
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sumNyz += n_yz;
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sumNz += n_z;
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// And take the digammas:
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double digammaNxzPlusOne = MathsUtils.digamma(n_xz+1);
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double digammaNyzPlusOne = MathsUtils.digamma(n_yz+1);
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double digammaNzPlusOne = MathsUtils.digamma(n_z+1);
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sumDiGammas += digammaNzPlusOne - digammaNxzPlusOne - digammaNyzPlusOne;
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if (returnLocals) {
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localCondMi[t-startTimePoint] = digammaK - digammaNxzPlusOne - digammaNyzPlusOne + digammaNzPlusOne;
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if (debug) {
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System.out.printf("t=%d, n_xz=%d, n_yz=%d, n_z=%d, local=%.4f\n",
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t, n_xz, n_yz, n_z, localCondMi[t-startTimePoint]);
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}
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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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System.out.println("Total exec times for: ");
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System.out.println("\tknn search: " + (knnTime/1000.0));
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System.out.println("\tz search: " + (conditionalTime/1000.0));
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System.out.println("\tzx search: " + (conditionalXTime/1000.0));
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System.out.println("\tzy search: " + (conditionalYTime/1000.0));
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}
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// Select what to return:
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if (returnLocals) {
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return localCondMi;
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} else {
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// Pad return array with two values, to allow compatibility in
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// return length with algorithm 2
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return new double[] {sumDiGammas, sumNxz, sumNyz, sumNz, 0, 0};
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}
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}
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}
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