mirror of https://github.com/jlizier/jidt
120 lines
3.7 KiB
Java
Executable File
120 lines
3.7 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.gaussian;
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import infodynamics.measures.continuous.EntropyCalculator;
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import infodynamics.utils.MatrixUtils;
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/**
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* <p>Computes the differential entropy of a given set of observations
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* (implementing {@link EntropyCalculator}, assuming that
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* the probability distribution function for these observations is Gaussian.</p>
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*
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* <p>Usage is as per the paradigm outlined for {@link EntropyCalculator},
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* with:
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* <ul>
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* <li>The constructor step being a simple call to {@link #EntropyCalculatorGaussian()}.</li>
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* <li>The user can call {@link #setVariance(double)}
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* instead of supplying observations via {@link #setObservations(double[])}.</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. M. Cover and J. A. Thomas, 'Elements of Information
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Theory' (John Wiley & Sons, New York, 1991).</li>
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<li>Differential entropy for Gaussian random variables defined at
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* <a href="http://mathworld.wolfram.com/DifferentialEntropy.html">MathWorld</a></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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*/
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public class EntropyCalculatorGaussian implements EntropyCalculator {
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/**
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* Variance of the most recently supplied observations, or set directly
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*/
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protected double variance;
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/**
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* Whether we are in debug mode
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*/
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protected boolean debug;
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/**
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* Construct an instance
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*/
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public EntropyCalculatorGaussian() {
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// Nothing to do
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}
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public void initialise() {
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// Nothing to do
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}
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public void setObservations(double[] observations) {
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variance = MatrixUtils.stdDev(observations);
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variance *= variance;
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}
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/**
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* An alternative to {@link #setObservations(double[])}, allowing user to
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* set the variance of the distribution for which we will compute the
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* entropy.
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*
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* @param variance the variance of the univariate distribution.
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*/
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public void setVariance(double variance) {
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this.variance = variance;
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}
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/**
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* Compute the entropy from the previously supplied observations, or
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* based on the supplied variance.
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*
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* <p>The entropy for a Gaussian-distribution random variable with
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* variance \sigma is 0.5*\log_e{2*pi*e*\sigma}.</p>
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*
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* <p>Here we compute the entropy assuming that the recorded estimation of the
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* variance is correct (i.e. we will not make a bias correction for limited
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* observations here).</p>
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*
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* @return the entropy of the previously provided observations or from the supplied
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* covariance matrix. Entropy returned in <b>nats</b>, not bits!
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*/
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public double computeAverageLocalOfObservations() {
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return 0.5 * Math.log(2.0*Math.PI*Math.E*variance);
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}
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public void setDebug(boolean debug) {
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this.debug = debug;
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}
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/**
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* No properties are defined here, so this method will have no effect.
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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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// No properties to set here
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}
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}
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