mirror of https://github.com/jlizier/jidt
86 lines
3.0 KiB
Java
Executable File
86 lines
3.0 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.discrete;
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import infodynamics.utils.MathsUtils;
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import infodynamics.utils.MatrixUtils;
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/**
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* <p>Computes the O-information of a given multivariate
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* <code>int[][]</code> set of
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* observations (extending {@link MultiVariateInfoMeasureCalculatorDiscrete}).</p>
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*
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* <p>Usage is as per the paradigm outlined for {@link MultiVariateInfoMeasureCalculatorDiscrete}.
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* </p>
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*
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* <p><b>References:</b><br/>
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* <ul>
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* <li>Rosas, F., Mediano, P., Gastpar, M, Jensen, H.,
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* <a href="http://dx.doi.org/10.1103/PhysRevE.100.032305">"Quantifying high-order
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* interdependencies via multivariate extensions of the mutual information"</a>,
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* Physical Review E 100, (2019) 032305.</li>
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* </ul>
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*
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* @author Pedro A.M. Mediano (<a href="pmediano at pm.me">email</a>,
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* <a href="http://www.doc.ic.ac.uk/~pam213">www</a>)
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*/
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public class OInfoCalculatorDiscrete
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extends MultiVariateInfoMeasureCalculatorDiscrete {
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/**
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* Construct an instance.
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*
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* @param base number of symbols for each variable.
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* E.g. binary variables are in base-2.
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* @param numVars numbers of joint variables that DTC
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* will be computed over.
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*/
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public OInfoCalculatorDiscrete(int base, int numVars) {
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super(base, numVars);
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}
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protected double computeLocalValueForTuple(int[] tuple, int jointValue) {
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if (jointCount[jointValue] == 0) {
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// This joint state does not occur, so it makes no contribution here
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return 0;
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}
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double jointProb = (double) jointCount[jointValue] / (double) observations;
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double logValue = (2 - numVars) * Math.log(jointProb);
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for (int i = 0; i < numVars; i++) {
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int bigMarginalState = computeBigMarginalState(jointValue, i, tuple[i]);
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double bigMarginalProb = (double) bigMarginalCounts[i][bigMarginalState] / (double) observations;
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double smallMarginalProb = (double) smallMarginalCounts[i][tuple[i]] / (double) observations;
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logValue += Math.log(bigMarginalProb) - Math.log(smallMarginalProb);
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}
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double localValue = logValue / log_2;
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if (jointProb > 0.0) {
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checkLocals(localValue);
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}
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return localValue;
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}
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}
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