mirror of https://github.com/jlizier/jidt
315 lines
14 KiB
Java
Executable File
315 lines
14 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 infodynamics.measures.continuous.ConditionalMutualInfoCalculatorMultiVariate;
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import infodynamics.measures.continuous.TransferEntropyCalculator;
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import infodynamics.measures.continuous.TransferEntropyCalculatorViaCondMutualInfo;
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import infodynamics.utils.MatrixUtils;
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/**
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* <p>Computes the differential transfer entropy (TE) between two univariate
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* <code>double[]</code> time-series of observations
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* (implementing {@link TransferEntropyCalculator}),
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* using Kraskov-Stoegbauer-Grassberger (KSG) estimation (see references below).
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* This estimator is realised here by plugging in
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* a {@link ConditionalMutualInfoCalculatorMultiVariateKraskov}
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* as the calculator into the parent class {@link TransferEntropyCalculatorViaCondMutualInfo}.</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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* and Gomez-Herrero et al.)
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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 TransferEntropyCalculator},
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* with:
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* <ul>
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* <li>The constructor step is either a simple call to {@link #TransferEntropyCalculatorKraskov()},
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* or else specifies which KSG algorithm to implement via
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* {@link #TransferEntropyCalculatorKraskov(String)};</li>
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* <li>{@link #setProperty(String, String)} allowing properties defined for both
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* {@link TransferEntropyCalculator#setProperty(String, String)} and
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* {@link ConditionalMutualInfoCalculatorMultiVariateKraskov#setProperty(String, String)}
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* as outlined
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* in {@link TransferEntropyCalculatorViaCondMutualInfo#setProperty(String, String)});
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* as well as for {@link #PROP_KRASKOV_ALG_NUM}.
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* Embedding parameters may be automatically determined as outlined in
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* {@link TransferEntropyCalculatorViaCondMutualInfo#setProperty(String, String)}.</li>
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* </li>
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* <li>Computed values are in <b>nats</b>, not bits!</li>
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* </ul>
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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>T. Schreiber, <a href="http://dx.doi.org/10.1103/PhysRevLett.85.461">
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* "Measuring information transfer"</a>,
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* Physical Review Letters 85 (2) pp.461-464, 2000.</li>
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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>G. Gomez-Herrero, W. Wu, K. Rutanen, M. C. Soriano, G. Pipa, and R. Vicente,
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* <a href="http://arxiv.org/abs/1008.0539">
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* "Assessing coupling dynamics from an ensemble of time series"</a>,
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* arXiv:1008.0539 (2010).</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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* <li>J. T. Lizier, M. Prokopenko and A. Zomaya,
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* <a href="http://dx.doi.org/10.1103/PhysRevE.77.026110">
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* "Local information transfer as a spatiotemporal filter for complex systems"</a>
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* Physical Review E 77, 026110, 2008.</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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* @see TransferEntropyCalculator
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* @see ConditionalMutualInfoCalculatorMultiVariateKraskov
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*/
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public class TransferEntropyCalculatorKraskov
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extends TransferEntropyCalculatorViaCondMutualInfo {
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/**
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* Class name for KSG conditional MI estimator via KSG algorithm 1
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*/
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public static final String COND_MI_CALCULATOR_KRASKOV1 = ConditionalMutualInfoCalculatorMultiVariateKraskov1.class.getName();
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/**
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* Class name for KSG conditional MI estimator via KSG algorithm 2
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*/
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public static final String COND_MI_CALCULATOR_KRASKOV2 = ConditionalMutualInfoCalculatorMultiVariateKraskov2.class.getName();
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/**
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* Property for setting which underlying Kraskov-Grassberger algorithm to use (1 or 2).
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* Will only be applied at the next initialisation.
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*/
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public final static String PROP_KRASKOV_ALG_NUM = "ALG_NUM";
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/**
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* Which Kraskov algorithm number we are using
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*/
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protected int kraskovAlgorithmNumber = 1;
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protected boolean algChanged = false;
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/**
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* Creates a new instance of the Kraskov-estimate style transfer entropy calculator
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*
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* Uses algorithm 1 by default, as per Gomez-Herro et al.
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*
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* @throws ClassNotFoundException
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* @throws IllegalAccessException
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* @throws InstantiationException
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*
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*/
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public TransferEntropyCalculatorKraskov() throws InstantiationException, IllegalAccessException, ClassNotFoundException {
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super(COND_MI_CALCULATOR_KRASKOV1);
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kraskovAlgorithmNumber = 1;
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}
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/**
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* Creates a new instance of the Kraskov-Grassberger style transfer entropy calculator,
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* with the supplied conditional MI calculator name
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*
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* @param calculatorName fully qualified name of the underlying MI class.
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* Must be {@link #COND_MI_CALCULATOR_KRASKOV1} or {@link #COND_MI_CALCULATOR_KRASKOV2}
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* @throws ClassNotFoundException
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* @throws IllegalAccessException
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* @throws InstantiationException
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*
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*/
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public TransferEntropyCalculatorKraskov(String calculatorName) throws InstantiationException, IllegalAccessException, ClassNotFoundException {
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super(calculatorName);
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// Now check that it was one of our Kraskov-Grassberger calculators:
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if (calculatorName.equalsIgnoreCase(COND_MI_CALCULATOR_KRASKOV1)) {
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kraskovAlgorithmNumber = 1;
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} else if (calculatorName.equalsIgnoreCase(COND_MI_CALCULATOR_KRASKOV2)) {
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kraskovAlgorithmNumber = 2;
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} else {
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throw new ClassNotFoundException("Must be an underlying Kraskov-Grassberger conditional MI calculator");
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}
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}
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/**
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* Creates a new instance of the Kraskov-Grassberger style transfer entropy calculator,
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* with the CMI Kraskov calculator using the given Kraskov algorithm number
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*
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* @param calculatorName fully qualified name of the underlying MI class.
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* Must be {@link #COND_MI_CALCULATOR_KRASKOV1} or {@link #COND_MI_CALCULATOR_KRASKOV2}
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* @throws ClassNotFoundException
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* @throws IllegalAccessException
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* @throws InstantiationException
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*
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*/
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public TransferEntropyCalculatorKraskov(int algorithm) throws InstantiationException, IllegalAccessException, ClassNotFoundException {
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super((algorithm == 1) ? COND_MI_CALCULATOR_KRASKOV1 : COND_MI_CALCULATOR_KRASKOV2);
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if ((algorithm != 1) && (algorithm != 2)) {
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throw new ClassNotFoundException("Algorithm must be 1 or 2");
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}
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}
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/* (non-Javadoc)
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* @see infodynamics.measures.continuous.TransferEntropyCalculatorViaCondMutualInfo#initialise(int, int, int, int, int)
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*/
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@Override
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public void initialise(int k, int k_tau, int l, int l_tau, int delay)
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throws Exception {
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if (algChanged) {
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// The algorithm number was changed in a setProperties call:
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String newCalcName = COND_MI_CALCULATOR_KRASKOV1;
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if (kraskovAlgorithmNumber == 2) {
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newCalcName = COND_MI_CALCULATOR_KRASKOV2;
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}
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@SuppressWarnings("unchecked")
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Class<ConditionalMutualInfoCalculatorMultiVariate> condMiClass =
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(Class<ConditionalMutualInfoCalculatorMultiVariate>) Class.forName(newCalcName);
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ConditionalMutualInfoCalculatorMultiVariate newCondMiCalc = condMiClass.newInstance();
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construct(newCondMiCalc);
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// Set the properties for the Kraskov MI calculators (may pass in properties for our super class
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// as well, but they should be ignored)
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for (String key : props.keySet()) {
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newCondMiCalc.setProperty(key, props.get(key));
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}
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algChanged = false;
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}
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super.initialise(k, k_tau, l, l_tau, delay);
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}
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/**
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* Sets properties for the TE calculator.
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* New property values are not guaranteed to take effect until the next call
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* to an initialise method.
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*
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* <p>Valid property names, and what their
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* values should represent, include:</p>
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* <ul>
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* <li>{@link #PROP_KRASKOV_ALG_NUM} -- which Kraskov algorithm number to use (1 or 2).</li>
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* <li>Any properties accepted by {@link TransferEntropyCalculatorViaCondMutualInfo#setProperty(String, String)}</li>
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* <li>Or properties accepted by the underlying
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* {@link ConditionalMutualInfoCalculatorMultiVariateKraskov#setProperty(String, String)} implementation.</li>
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* </ul>
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* <p><b>Note:</b> further properties may be defined by child classes.</p>
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*
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* <p>Unknown property values are ignored.</p>
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*
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* @param propertyName name of the property
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* @param propertyValue value of the property.
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* @throws Exception if there is a problem with the supplied value).
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*/
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public void setProperty(String propertyName, String propertyValue)
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throws Exception {
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if (propertyName.equalsIgnoreCase(PROP_KRASKOV_ALG_NUM)) {
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int previousAlgNumber = kraskovAlgorithmNumber;
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kraskovAlgorithmNumber = Integer.parseInt(propertyValue);
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if ((kraskovAlgorithmNumber != 1) && (kraskovAlgorithmNumber != 2)) {
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throw new Exception("Kraskov algorithm number (" + kraskovAlgorithmNumber
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+ ") must be either 1 or 2");
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}
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if (kraskovAlgorithmNumber != previousAlgNumber) {
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algChanged = true;
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}
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if (debug) {
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System.out.println(this.getClass().getSimpleName() + ": Set property " + propertyName +
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" to " + propertyValue);
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}
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} else {
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// Assume it was a property for the parent class or underlying conditional MI calculator
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super.setProperty(propertyName, propertyValue);
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}
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}
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@Override
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public String getProperty(String propertyName) throws Exception {
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if (propertyName.equalsIgnoreCase(PROP_KRASKOV_ALG_NUM)) {
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return Integer.toString(kraskovAlgorithmNumber);
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} else if (propertyName.equalsIgnoreCase(PROP_RAGWITZ_NUM_NNS)) {
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// Need to deal with this one here instead of in super class,
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// since if it's not set especially by the user we'll default to the
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// kNNs used in the KSG method:
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if (ragwitz_num_nns_set) {
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return Integer.toString(ragwitz_num_nns);
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} else {
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return condMiCalc.getProperty(ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_K);
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}
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} else {
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// Assume it was a property for the parent class or underlying conditional MI calculator
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return super.getProperty(propertyName);
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}
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}
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/**
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* Debug method to return the k nearest neighbour distances that
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* would be utilised for each sample point here.
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* Note that this is specifically the max-norm across the source-target-targetPast variables, which is used for each
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* range search in algorithm 1 (although algorithm 2 would use the max distance
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* for each variable within the kNNs in their separate range searches).
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*
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* @param startTimePoint
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* @param numTimePoints
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* @return
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* @throws Exception
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*/
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public double[] kNNDistances(int startTimePoint, int numTimePoints) throws Exception {
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// Defer the call to the underlying KSG CMI estimator
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return ((ConditionalMutualInfoCalculatorMultiVariateKraskov) condMiCalc).kNNDistances(startTimePoint, numTimePoints);
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}
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/**
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* Debug method to return the k nearest neighbour distances that
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* would be utilised in {@link #computeLocalUsingPreviousObservations(double[], double[])}
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* for a cross TE.
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* Note that this is specifically the max-norm across the source-target-targetPast variables, which is used for each
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* range search in algorithm 1 (although algorithm 2 would use the max distance
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* for each variable within the kNNs in their separate range searches).
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*
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* @param startTimePoint
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* @param numTimePoints
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* @param newSourceObservations
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* @param newDestObservations
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* @return
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* @throws Exception
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*/
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public double[] kNNDistancesForNewSamples(int startTimePoint, int numTimePoints,
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double[] newSourceObservations, double[] newDestObservations) throws Exception {
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if (newSourceObservations.length != newDestObservations.length) {
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throw new Exception(String.format("Source and destination lengths (%d and %d) must match!",
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newSourceObservations.length, newDestObservations.length));
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}
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if (newDestObservations.length < startTimeForFirstDestEmbedding + 2) {
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// There are no observations to compute for here
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return new double[newDestObservations.length];
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}
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// Now embed as per computeLocalUsingPreviousObservations() in super:
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double[][] newDestPastVectors =
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MatrixUtils.makeDelayEmbeddingVector(newDestObservations, k, k_tau,
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startTimeForFirstDestEmbedding,
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newDestObservations.length - startTimeForFirstDestEmbedding - 1);
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double[][] newDestNextVectors =
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MatrixUtils.makeDelayEmbeddingVector(newDestObservations, 1,
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startTimeForFirstDestEmbedding + 1,
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newDestObservations.length - startTimeForFirstDestEmbedding - 1);
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double[][] newSourcePastVectors =
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MatrixUtils.makeDelayEmbeddingVector(newSourceObservations, l, l_tau,
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startTimeForFirstDestEmbedding + 1 - delay,
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newSourceObservations.length - startTimeForFirstDestEmbedding - 1);
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return ((ConditionalMutualInfoCalculatorMultiVariateKraskov) condMiCalc).
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kNNDistancesForNewSamples(startTimePoint, numTimePoints, newSourcePastVectors, newDestNextVectors, newDestPastVectors);
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}
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}
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