mirror of https://github.com/jlizier/jidt
107 lines
3.3 KiB
Java
Executable File
107 lines
3.3 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.utils;
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/**
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*
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* Structure to hold a distribution of info-theoretic measurements,
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* and a significance value for how an original measurement compared
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* with these.
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*
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* @author Joseph Lizier
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*
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*/
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public class EmpiricalMeasurementDistribution extends MeasurementDistribution {
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/**
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* Distribution of surrogate measurement values
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*/
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public double[] distribution;
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/**
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* Whether the mean of the surrogate measurement distribution has
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* been computed
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*/
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protected boolean computedMean = false;
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/**
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* Computed mean of the surrogate measurement distribution
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*/
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protected double meanOfDist;
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/**
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* Computed mean of the surrogate measurement distribution
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*/
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protected double stdOfDist;
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public EmpiricalMeasurementDistribution(int size) {
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super(); // Creating the super class with mean and pValue 0
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// These value will be filled out by the caller later.
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distribution = new double[size];
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}
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public EmpiricalMeasurementDistribution(double[] distribution, double actualValue) {
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super(actualValue, 0); // Using pValue = 0 temporarily ...
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this.distribution = distribution;
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int countWhereActualIsNotGreater = 0;
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for (int i = 0; i < distribution.length; i++) {
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if (distribution[i] >= actualValue) {
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countWhereActualIsNotGreater++;
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}
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}
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pValue = (double) countWhereActualIsNotGreater / (double) distribution.length;
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}
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// TODO Compute the significance under the assumption of a Gaussian distribution
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/*
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public double computeGaussianSignificance() {
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// Need to conpute the significance based on the assumption of
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// an underlying Gaussian distribution.
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// Use the t distribution for analysis, since we have a finite
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// number of samples to comptue the mean and std from.
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return 0;
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}
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*/
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public double getTSscore() {
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if (! computedMean) {
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meanOfDist = MatrixUtils.mean(distribution);
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stdOfDist = MatrixUtils.stdDev(distribution, meanOfDist);
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computedMean = true;
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}
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double t = (actualValue - meanOfDist) / stdOfDist;
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return t;
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}
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public double getMeanOfDistribution() {
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if (! computedMean) {
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meanOfDist = MatrixUtils.mean(distribution);
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stdOfDist = MatrixUtils.stdDev(distribution, meanOfDist);
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computedMean = true;
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}
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return meanOfDist;
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}
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public double getStdOfDistribution() {
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if (! computedMean) {
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meanOfDist = MatrixUtils.mean(distribution);
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stdOfDist = MatrixUtils.stdDev(distribution, meanOfDist);
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computedMean = true;
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}
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return stdOfDist;
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}
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}
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