mirror of https://github.com/jlizier/jidt
Merge pull request #64 from pmediano/master
Improvements to Kozachenko multivariate entropy calculator. Looks great, thanks Pedro. Fixes issue #56, will close it
This commit is contained in:
Joseph Lizier
GitHub
commit
a9dac705a8
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@ -18,6 +18,10 @@
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package infodynamics.measures.continuous.kozachenko;
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import java.util.Random;
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import java.util.Iterator;
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import java.util.Vector;
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import infodynamics.measures.continuous.EntropyCalculator;
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import infodynamics.measures.continuous.EntropyCalculatorMultiVariate;
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import infodynamics.utils.EuclideanUtils;
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@ -60,14 +64,64 @@ import infodynamics.utils.MatrixUtils;
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public class EntropyCalculatorMultiVariateKozachenko
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implements EntropyCalculator, EntropyCalculatorMultiVariate {
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protected boolean debug = false;
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private int totalObservations;
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/**
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* Total number of observations supplied.
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*/
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private int totalObservations = 0;
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/**
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* Number of dimensions of our multivariate data set
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*/
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private int dimensions = 1;
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/**
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* The set of observations, retained in case the user wants to retrieve the local
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* entropy values of these.
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*/
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protected double[][] rawData;
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/**
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* Store the last computed average H
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*/
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private double lastAverage = 0.0;
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/**
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* Store the last computed local H
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*/
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private double[] lastLocalEntropy;
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/**
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* Track whether we've computed the average for the supplied
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* observations yet
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*/
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private boolean isComputed;
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/**
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* Storage for observations supplied via {@link #addObservations(double[][])}
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* type calls
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*/
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protected Vector<double[][]> vectorOfObservations;
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/**
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* Whether to report debug messages or not
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*/
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protected boolean debug = false;
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/**
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* Property name for whether to normalise the incoming data to
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* mean 0, standard deviation 1 (default true)
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*/
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public static final String PROP_NORMALISE = "NORMALISE";
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/**
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* Property name for an amount of random Gaussian noise to be
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* added to the data (default is 1e-8, matching the MILCA toolkit).
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*/
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public static final String PROP_ADD_NOISE = "NOISE_LEVEL_TO_ADD";
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/**
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* Whether to add an amount of random noise to the incoming data
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*/
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protected boolean addNoise = true;
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/**
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* Amount of random Gaussian noise to add to the incoming data
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*/
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protected double noiseLevel = (double) 1e-8;
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/**
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* Stored pre-computed value of the Euler-Mascheroni constant
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*/
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public static final double EULER_MASCHERONI_CONSTANT = 0.5772156;
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/**
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@ -91,15 +145,24 @@ public class EntropyCalculatorMultiVariateKozachenko
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isComputed = false;
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lastLocalEntropy = null;
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}
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@Override
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public void setProperty(String propertyName, String propertyValue)
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throws Exception {
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boolean propertySet = true;
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if (propertyName.equalsIgnoreCase(NUM_DIMENSIONS_PROP_NAME)) {
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dimensions = Integer.parseInt(propertyValue);
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} else if (propertyName.equalsIgnoreCase(PROP_ADD_NOISE)) {
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if (propertyValue.equals("0") ||
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propertyValue.equalsIgnoreCase("false")) {
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addNoise = false;
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noiseLevel = 0;
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} else {
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addNoise = true;
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noiseLevel = Double.parseDouble(propertyValue);
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}
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} else {
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// No property was set
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// No property was set, and no superclass to call.
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propertySet = false;
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}
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if (debug && propertySet) {
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@ -112,20 +175,65 @@ public class EntropyCalculatorMultiVariateKozachenko
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public String getProperty(String propertyName) throws Exception {
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if (propertyName.equalsIgnoreCase(NUM_DIMENSIONS_PROP_NAME)) {
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return Integer.toString(dimensions);
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} else if (propertyName.equalsIgnoreCase(PROP_ADD_NOISE)) {
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return Double.toString(noiseLevel);
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} else {
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// No property was set, and no superclass to call:
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// No property was set, and no superclass to call.
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return null;
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}
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}
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@Override
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public void setObservations(double[][] observations) {
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rawData = observations;
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totalObservations = observations.length;
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public void startAddObservations() {
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isComputed = false;
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totalObservations = 0;
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lastLocalEntropy = null;
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rawData = null;
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vectorOfObservations = new Vector<double[][]>();
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}
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public void finaliseAddObservations() {
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rawData = new double[totalObservations][dimensions];
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// Construct the joint vectors from the given observations
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// (removing redundant data which is outside any timeDiff)
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int startObservation = 0;
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for (double[][] obs : vectorOfObservations) {
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// Copy the data from these given observations into our master array
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MatrixUtils.arrayCopy(obs, 0, 0,
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rawData, startObservation, 0,
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obs.length, dimensions);
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startObservation += obs.length;
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}
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// We don't need to keep the vector of observation sets anymore:
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vectorOfObservations = null;
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if (addNoise) {
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Random random = new Random();
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// Add Gaussian noise of std dev noiseLevel to the data
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for (int r = 0; r < totalObservations; r++) {
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for (int c = 0; c < dimensions; c++) {
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rawData[r][c] += random.nextGaussian()*noiseLevel;
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}
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}
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}
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}
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@Override
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public void setObservations(double[][] observations) {
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startAddObservations();
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addObservations(observations);
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finaliseAddObservations();
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}
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public void setObservations(double[][] observations1, double[][] observations2)
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throws Exception {
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startAddObservations();
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addObservations(observations1, observations2);
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finaliseAddObservations();
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}
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/*
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* (non-Javadoc)
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* @see infodynamics.measures.continuous.EntropyCalculator#setObservations(double[])
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@ -134,9 +242,24 @@ public class EntropyCalculatorMultiVariateKozachenko
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* EntropyCalculator interface.
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*/
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@Override
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public void setObservations(double[] observations) {
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public void setObservations(double[] observations) {
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startAddObservations();
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addObservations(observations);
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finaliseAddObservations();
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}
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public void addObservations(double[][] observations) {
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if (vectorOfObservations == null) {
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// startAddObservations was not called first
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throw new RuntimeException("User did not call startAddObservations before addObservations");
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}
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vectorOfObservations.add(observations);
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totalObservations += observations.length;
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}
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public void addObservations(double[] observations) {
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rawData = MatrixUtils.reshape(observations, observations.length, 1);
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setObservations(rawData);
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addObservations(rawData);
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}
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/**
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@ -149,9 +272,9 @@ public class EntropyCalculatorMultiVariateKozachenko
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* @param data1 first few variables in the joint data
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* @param data2 the other variables in the joint data
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* @throws Exception When the length of the two arrays of observations do not match.
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* @see #setObservations(double[][])
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* @see #addObservations(double[][])
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*/
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public void setObservations(double[][] data1,
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public void addObservations(double[][] data1,
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double[][] data2) throws Exception {
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int timeSteps = data1.length;
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if ((data1 == null) || (data2 == null)) {
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@ -169,7 +292,7 @@ public class EntropyCalculatorMultiVariateKozachenko
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System.arraycopy(data2[t], 0, data[t], data1Variables, data2Variables);
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}
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// Now defer to the normal setObservations method
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setObservations(data);
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addObservations(data);
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}
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/**
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@ -181,7 +304,7 @@ public class EntropyCalculatorMultiVariateKozachenko
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return lastAverage;
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}
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double sdTermHere = sdTerm(totalObservations, dimensions);
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double emConstHere = eulerMacheroniTerm(totalObservations);
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double emConstHere = eulerMascheroniTerm(totalObservations);
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double[] minDistance = EuclideanUtils.computeMinEuclideanDistances(rawData);
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double entropy = 0.0;
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if (debug) {
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@ -221,7 +344,7 @@ public class EntropyCalculatorMultiVariateKozachenko
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}
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double sdTermHere = sdTerm(totalObservations, dimensions);
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double emConstHere = eulerMacheroniTerm(totalObservations);
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double emConstHere = eulerMascheroniTerm(totalObservations);
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double constantToAddIn = sdTermHere + emConstHere;
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double[] minDistance = EuclideanUtils.computeMinEuclideanDistances(rawData);
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@ -271,7 +394,7 @@ public class EntropyCalculatorMultiVariateKozachenko
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*
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* @return
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*/
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public double eulerMacheroniTerm(int N) {
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public double eulerMascheroniTerm(int N) {
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// Using natural units
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// return EULER_MASCHERONI_CONSTANT / Math.log(2);
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try {
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