mirror of https://github.com/jlizier/jidt
128 lines
3.8 KiB
Java
Executable File
128 lines
3.8 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.kernel;
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import infodynamics.measures.continuous.EntropyCalculator;
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public class EntropyCalculatorKernel implements EntropyCalculator {
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protected KernelEstimatorUniVariate svke = null;
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protected int totalObservations = 0;
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protected boolean debug = false;
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protected double[] observations;
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private boolean normalise = true;
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public static final String NORMALISE_PROP_NAME = "NORMALISE";
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/**
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* Default value for epsilon
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*/
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public static final double DEFAULT_EPSILON = 0.25;
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/**
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* Kernel width
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*/
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private double epsilon = DEFAULT_EPSILON;
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public static final String EPSILON_PROP_NAME = "EPSILON";
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public EntropyCalculatorKernel() {
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svke = new KernelEstimatorUniVariate();
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svke.setDebug(debug);
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svke.setNormalise(normalise);
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}
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public void initialise() {
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initialise(epsilon);
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}
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public void initialise(double epsilon) {
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this.epsilon = epsilon;
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svke.initialise(epsilon);
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}
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/**
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* Set the observations for the PDFs.
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* Should only be called once, the last call contains the
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* observations that are used (they are not accumulated).
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*
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* @param observations
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*/
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public void setObservations(double observations[]) {
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this.observations = observations;
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svke.setObservations(observations);
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totalObservations = observations.length;
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}
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public double computeAverageLocalOfObservations() {
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double entropy = 0.0;
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for (int t = 0; t < observations.length; t++) {
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double prob = svke.getProbability(observations[t]);
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double cont = Math.log(prob);
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entropy -= cont;
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if (debug) {
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System.out.println(t + ": p(" + observations[t] + ")= " +
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prob + " -> " + (cont/Math.log(2.0)) + " -> sum: " +
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(entropy/Math.log(2.0)));
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}
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}
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return entropy / (double) totalObservations / Math.log(2.0);
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}
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public void setDebug(boolean debug) {
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this.debug = debug;
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if (svke != null) {
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svke.setDebug(debug);
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}
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}
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/**
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* Allows the user to set properties for the underlying calculator implementation
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* These can include:
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* <ul>
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* <li>{@link #EPSILON_PROP_NAME}</li>
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* <li>{@link #NORMALISE_PROP_NAME}</li>
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* </ul>
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*
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* @param propertyName
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* @param propertyValue
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* @throws Exception
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*/
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public void setProperty(String propertyName, String propertyValue) throws Exception {
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boolean propertySet = true;
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// TODO If we implement a dynamic correlation exclusion property,
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// then we will need to call getProbability(double, int) instead of
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// just getProbability(double) above.
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if (propertyName.equalsIgnoreCase(EPSILON_PROP_NAME)) {
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epsilon = Double.parseDouble(propertyValue);
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} else if (propertyName.equalsIgnoreCase(NORMALISE_PROP_NAME)) {
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normalise = Boolean.parseBoolean(propertyValue);
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svke.setNormalise(normalise);
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} else {
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// No property was set
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propertySet = false;
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}
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if (debug && propertySet) {
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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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}
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}
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