mirror of https://github.com/jlizier/jidt
105 lines
4.5 KiB
Java
105 lines
4.5 KiB
Java
/*
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* Java Information Dynamics Toolkit (JIDT)
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* Copyright (C) 2017, Joseph T. Lizier, Ipek Oezdemir and Pedro Mediano
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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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* Calculators implementing this interface must provide
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* {@link #computeSignificance(int)} and {@link #computeSignificance(int[][])}
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* methods to compute
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* the statistical significance of their measurement, returning an empirically
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* determined distribution {@link EmpiricalMeasurementDistribution}.
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*
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* @author Joseph Lizier (<a href="joseph.lizier at gmail.com">email</a>,
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* <a href="http://lizier.me/joseph/">www</a>)
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*/
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public interface EmpiricalNullDistributionComputer {
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/**
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* Generate an <b>empirical</b> (bootstrapped) distribution of what the given measure would look like,
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* under a null hypothesis that the source values of our
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* samples had no relation to the destination value (possibly
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* in the context of a conditional value).
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*
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* <p>See Section II.E "Statistical significance testing" of
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* the JIDT paper below for a description of how this is done for MI,
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* conditional MI and TE.
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* </p>
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*
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* <p>Note that if several disjoint time-series have been added
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* as observations using addObservations methods etc.,
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* then these separate "trials" will be mixed up in the generation
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* of surrogates here.</p>
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*
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* <p><b>References:</b><br/>
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* <ul>
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* <li>J.T. Lizier, "JIDT: An information-theoretic
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* toolkit for studying the dynamics of complex systems", 2014.</li>
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* </ul>
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*
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* @param numPermutationsToCheck number of surrogate samples to bootstrap
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* to generate the distribution.
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* @see "J.T. Lizier, 'JIDT: An information-theoretic
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* toolkit for studying the dynamics of complex systems', 2014."
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* @return the empirical distribution of measure scores under this null hypothesis.
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*/
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public EmpiricalMeasurementDistribution computeSignificance(int numPermutationsToCheck) throws Exception;
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/**
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* Generate an <b>empirical</b> (bootstrapped) distribution of what the given measure would look like,
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* under a null hypothesis that the source values of our
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* samples had no relation to the destination value (possibly
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* in the context of a conditional value).
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*
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* <p>See Section II.E "Statistical significance testing" of
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* the JIDT paper below for a description of how this is done MI,
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* conditional MI and TE.
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* </p>
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*
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* <p>Note that if several disjoint time-series have been added
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* as observations using addObservations methods etc.,
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* then these separate "trials" will be mixed up in the generation
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* of surrogates here.</p>
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*
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* <p>This method (in contrast to {@link #computeSignificance(int)})
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* allows the user to specify how to construct the surrogates,
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* such that repeatable results may be obtained.</p>
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*
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* <p><b>References:</b><br/>
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* <ul>
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* <li>J.T. Lizier, "JIDT: An information-theoretic
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* toolkit for studying the dynamics of complex systems", 2014.</li>
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* </ul>
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*
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* @param newOrderings a specification of how to shuffle the next values
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* to create the surrogates to generate the distribution with. The first
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* index is the permutation number (i.e. newOrderings.length is the number
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* of surrogate samples we use to bootstrap to generate the distribution here.)
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* Each array newOrderings[i] should be an array of length N (where
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* would be the value returned by a call to getNumObservations() for the given measure)
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* containing a permutation of the values in 0..(N-1).
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* @see "J.T. Lizier, 'JIDT: An information-theoretic
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* toolkit for studying the dynamics of complex systems', 2014."
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* @return the empirical distribution of measure scores under this null hypothesis.
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* @throws Exception where e.g. the newOrderings don't supply arrays of the correct
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* length matching the number of observations that we have.
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*/
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public EmpiricalMeasurementDistribution computeSignificance(int[][] newOrderings) throws Exception;
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}
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