mirror of https://github.com/jlizier/jidt
81 lines
3.0 KiB
Java
Executable File
81 lines
3.0 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.utils;
|
|
|
|
import infodynamics.utils.commonsmath3.distribution.ChiSquaredDistribution;
|
|
|
|
/**
|
|
* Class to represent analytic distributions of info theoretic measurements under
|
|
* some null hypothesis of a relationship between the variables, where the
|
|
* distribution of <b>a function of</b> those information-theoretic measurements
|
|
* is a Chi Square distribution.
|
|
*
|
|
* @author Joseph Lizier (<a href="joseph.lizier at gmail.com">email</a>,
|
|
* <a href="http://lizier.me/joseph/">www</a>)
|
|
*/
|
|
public class ChiSquareMeasurementDistribution extends
|
|
AnalyticMeasurementDistribution {
|
|
|
|
/**
|
|
* Number of degrees of freedom for the distribution
|
|
*/
|
|
protected int degreesOfFreedom;
|
|
|
|
/**
|
|
* The number of observations that the information theoretic estimate
|
|
* is computed from
|
|
*/
|
|
protected int numObservations;
|
|
|
|
/**
|
|
* An object of the chi2dist class from commons.math3,
|
|
* which we'll use to access the distribution values
|
|
*/
|
|
protected ChiSquaredDistribution chi2dist;
|
|
|
|
/**
|
|
* Construct the distribution.
|
|
* Note: the Chi squared distribution is technically
|
|
* of 2*numObservations*(the info theoretic estimate), not
|
|
* of the info theoretic measurement itself.
|
|
*
|
|
* @param actualValue actual observed information-theoretic value
|
|
* @param numObservations the number of observations that the information theoretic estimate
|
|
* is computed from
|
|
* @param degreesOfFreedom degrees of freedom for the distribution
|
|
*/
|
|
public ChiSquareMeasurementDistribution(double actualValue,
|
|
int numObservations, int degreesOfFreedom) {
|
|
super(actualValue, 1 - MathsUtils.chiSquareCdf(2.0*((double)numObservations)*actualValue, degreesOfFreedom));
|
|
this.numObservations = numObservations;
|
|
this.degreesOfFreedom = degreesOfFreedom;
|
|
chi2dist = new ChiSquaredDistribution(degreesOfFreedom);
|
|
}
|
|
|
|
public double computePValueForGivenEstimate(double estimate) {
|
|
return 1 - MathsUtils.chiSquareCdf(2.0*((double)numObservations)*estimate, degreesOfFreedom);
|
|
}
|
|
|
|
public double computeEstimateForGivenPValue(double pValue) {
|
|
return chi2dist.inverseCumulativeProbability(1 - pValue) / (2.0*((double)numObservations));
|
|
// Could also call the following, but this doesn't re-use our objects:
|
|
// return MathsUtils.chiSquareInv(1 - pValue, degreesOfFreedom);
|
|
}
|
|
}
|