jidt/java/source/infodynamics/measures/continuous/gaussian/EntropyCalculatorGaussian.java

120 lines
3.7 KiB
Java
Executable File

/*
* Java Information Dynamics Toolkit (JIDT)
* Copyright (C) 2012, Joseph T. Lizier
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
package infodynamics.measures.continuous.gaussian;
import infodynamics.measures.continuous.EntropyCalculator;
import infodynamics.utils.MatrixUtils;
/**
* <p>Computes the differential entropy of a given set of observations
* (implementing {@link EntropyCalculator}, assuming that
* the probability distribution function for these observations is Gaussian.</p>
*
* <p>Usage is as per the paradigm outlined for {@link EntropyCalculator},
* with:
* <ul>
* <li>The constructor step being a simple call to {@link #EntropyCalculatorGaussian()}.</li>
* <li>The user can call {@link #setVariance(double)}
* instead of supplying observations via {@link #setObservations(double[])}.</li>
* <li>Computed values are in <b>nats</b>, not bits!</li>
* </ul>
* </p>
*
* <p><b>References:</b><br/>
* <ul>
* <li>T. M. Cover and J. A. Thomas, 'Elements of Information
Theory' (John Wiley & Sons, New York, 1991).</li>
<li>Differential entropy for Gaussian random variables defined at
* <a href="http://mathworld.wolfram.com/DifferentialEntropy.html">MathWorld</a></li>
* </ul>
*
* @author Joseph Lizier (<a href="joseph.lizier at gmail.com">email</a>,
* <a href="http://lizier.me/joseph/">www</a>)
*/
public class EntropyCalculatorGaussian implements EntropyCalculator {
/**
* Variance of the most recently supplied observations, or set directly
*/
protected double variance;
/**
* Whether we are in debug mode
*/
protected boolean debug;
/**
* Construct an instance
*/
public EntropyCalculatorGaussian() {
// Nothing to do
}
public void initialise() {
// Nothing to do
}
public void setObservations(double[] observations) {
variance = MatrixUtils.stdDev(observations);
variance *= variance;
}
/**
* An alternative to {@link #setObservations(double[])}, allowing user to
* set the variance of the distribution for which we will compute the
* entropy.
*
* @param variance the variance of the univariate distribution.
*/
public void setVariance(double variance) {
this.variance = variance;
}
/**
* Compute the entropy from the previously supplied observations, or
* based on the supplied variance.
*
* <p>The entropy for a Gaussian-distribution random variable with
* variance \sigma is 0.5*\log_e{2*pi*e*\sigma}.</p>
*
* <p>Here we compute the entropy assuming that the recorded estimation of the
* variance is correct (i.e. we will not make a bias correction for limited
* observations here).</p>
*
* @return the entropy of the previously provided observations or from the supplied
* covariance matrix. Entropy returned in <b>nats</b>, not bits!
*/
public double computeAverageLocalOfObservations() {
return 0.5 * Math.log(2.0*Math.PI*Math.E*variance);
}
public void setDebug(boolean debug) {
this.debug = debug;
}
/**
* No properties are defined here, so this method will have no effect.
*/
public void setProperty(String propertyName, String propertyValue)
throws Exception {
// No properties to set here
}
}