mirror of https://github.com/jlizier/jidt
121 lines
4.1 KiB
Java
121 lines
4.1 KiB
Java
/*
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* Java Information Dynamics Toolkit (JIDT)
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* Copyright (C) 2017, Joseph T. Lizier, Ipek Oezdemir and Pedro Mediano
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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.gaussian;
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import infodynamics.utils.MatrixUtils;
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/**
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* <p>Computes the differential S-information of a given multivariate
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* <code>double[][]</code> set of
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* observations (extending {@link MultiVariateInfoMeasureCalculatorGaussian}),
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* assuming that the probability distribution function for these observations is
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* a multivariate Gaussian distribution.</p>
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*
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* <p>Usage is as per the paradigm outlined for {@link MultiVariateInfoMeasureCalculatorCommon}.
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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>Rosas, F., Mediano, P., Gastpar, M, Jensen, H.,
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* <a href="http://dx.doi.org/10.1103/PhysRevE.100.032305">"Quantifying high-order
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* interdependencies via multivariate extensions of the mutual information"</a>,
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* Physical Review E 100, (2019) 032305.</li>
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* </ul>
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*
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* @author Pedro A.M. Mediano (<a href="pmediano at pm.me">email</a>,
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* <a href="http://www.doc.ic.ac.uk/~pam213">www</a>)
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*/
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public class SInfoCalculatorGaussian
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extends MultiVariateInfoMeasureCalculatorGaussian {
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/**
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* Constructor.
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*/
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public SInfoCalculatorGaussian() {
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// Nothing to do
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}
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/**
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* {@inheritDoc}
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*
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* @return the average S-info in nats (not bits!)
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* @throws Exception if not sufficient data have been provided, or if the
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* supplied covariance matrix is invalid.
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*/
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public double computeAverageLocalOfObservations() throws Exception {
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if (covariance == null) {
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throw new Exception("Cannot calculate O-Info without having " +
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"a covariance either supplied or computed via setObservations()");
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}
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if (!isComputed) {
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MultiInfoCalculatorGaussian tcCalc = new MultiInfoCalculatorGaussian();
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tcCalc.initialise(dimensions);
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tcCalc.setCovariance(covariance);
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double tc = tcCalc.computeAverageLocalOfObservations();
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DualTotalCorrelationCalculatorGaussian dtcCalc = new DualTotalCorrelationCalculatorGaussian();
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dtcCalc.initialise(dimensions);
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dtcCalc.setCovariance(covariance);
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double dtc = dtcCalc.computeAverageLocalOfObservations();
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lastAverage = tc + dtc;
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isComputed = true;
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}
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return lastAverage;
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}
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/**
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* {@inheritDoc}
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*
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* @return the "time-series" of local S-info values in nats (not bits!)
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* for the supplied states.
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* @throws Exception if not sufficient data have been provided, or if the
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* supplied covariance matrix is invalid.
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*/
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public double[] computeLocalUsingPreviousObservations(double[][] states) throws Exception {
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if ((means == null) || (covariance == null)) {
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throw new Exception("Cannot compute local values without having means " +
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"and covariance either supplied or computed via setObservations()");
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}
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MultiInfoCalculatorGaussian tcCalc = new MultiInfoCalculatorGaussian();
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tcCalc.initialise(dimensions);
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tcCalc.setCovarianceAndMeans(covariance, means);
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double[] localTC = tcCalc.computeLocalUsingPreviousObservations(states);
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DualTotalCorrelationCalculatorGaussian dtcCalc = new DualTotalCorrelationCalculatorGaussian();
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dtcCalc.initialise(dimensions);
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dtcCalc.setCovarianceAndMeans(covariance, means);
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double[] localDTC = dtcCalc.computeLocalUsingPreviousObservations(states);
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double[] localValues = MatrixUtils.add(localTC, localDTC);
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return localValues;
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}
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}
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