mirror of https://github.com/jlizier/jidt
126 lines
5.5 KiB
Java
Executable File
126 lines
5.5 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.continuous;
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/**
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* <p>Interface for implementations of the <b>transfer entropy</b> (TE),
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* which may be applied to multivariate continuous time-series data.
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* That is, it is applied to <code>double[][]</code> data, indexed
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* by time then by variable number.
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* See Schreiber below for the definition of transfer entropy,
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* Lizier et al. (2011) for the extension to multivariate source
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* and destination, and
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* and Lizier et al. (2008) for the definition of local transfer entropy.
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* Specifically, this class implements the pairwise or <i>apparent</i>
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* transfer entropy; i.e. we compute the transfer that appears to
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* come from a single source variable, without examining any other
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* potential sources
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* (see Lizier et al, PRE, 2008).</p>
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*
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* <p>
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* Usage of the child classes implementing this interface is intended to follow this paradigm:
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* </p>
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* <ol>
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* <li>Construct the calculator;</li>
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* <li>Set properties using {@link #setProperty(String, String)}
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* which at this level includes destination
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* history embedding length but not
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* delays or source embedding (TODO pull this up from TeMultivariateViaCondMi)</li>
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* <li>Initialise the calculator using
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* {@link #initialise()} or {@link #initialise(int, int)}
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* or {@link #initialise(int, int, int)};</li>
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* <li>Provide the observations/samples for the calculator
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* to set up the PDFs, using:
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* <ul>
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* <li>{@link #setObservations(double[][], double[][])} or
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* {@link #setObservations(double[][], double[][], boolean[], boolean[])} or
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* {@link #setObservations(double[][], double[][], boolean[][], boolean[][])}
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* for calculations based on single time-series, OR</li>
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* <li>The following sequence:<ol>
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* <li>{@link #startAddObservations()}, then</li>
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* <li>One or more calls to
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* {@link #addObservations(double[][], double[][])} or
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* {@link #addObservations(double[][], double[][], int, int)}, then</li>
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* <li>{@link #finaliseAddObservations()};</li>
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* </ol></li>
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* </ul>
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* <li>Compute the required quantities, being one or more of:
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* <ul>
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* <li>the average TE: {@link #computeAverageLocalOfObservations()};</li>
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* <li>the local TE values for these samples:
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* {@link #computeLocalOfPreviousObservations()}</li>
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* <li>local TE values for a specific set of samples:
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* {@link #computeLocalUsingPreviousObservations(double[][], double[][])} </li>
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* <li>the distribution of MI values under the null hypothesis
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* of no relationship between source and
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* destination values: {@link #computeSignificance(int)} or
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* {@link #computeSignificance(int[][])}.</li>
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* </ul>
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* </li>
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* <li>
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* Return to step 2 or 3 to re-use the calculator on a new data set.
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* </li>
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* </ol>
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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>T. Schreiber, <a href="http://dx.doi.org/10.1103/PhysRevLett.85.461">
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* "Measuring information transfer"</a>,
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* Physical Review Letters 85 (2) pp.461-464, 2000.</li>
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* <li>J. T. Lizier, M. Prokopenko and A. Zomaya,
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* <a href="http://dx.doi.org/10.1103/PhysRevE.77.026110">
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* "Local information transfer as a spatiotemporal filter for complex systems"</a>
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* Physical Review E 77, 026110, 2008.</li>
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* <li>J.T. Lizier, J. Heinzle, A. Horstmann, J.-D. Haynes, M. Prokopenko,
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* <a href="http://dx.doi.org/10.1007/s10827-010-0271-2">
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* "Multivariate information-theoretic measures reveal directed information
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* structure and task relevant changes in fMRI connectivity"</a>,
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* Journal of Computational Neuroscience, vol. 30, pp. 85-107, 2011.</li>
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* </ul>
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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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*/
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public interface TransferEntropyCalculatorMultiVariate extends ChannelCalculatorMultiVariate {
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/**
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* Property name to specify the history length k.
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* For calculators which implement both this and
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* {@link TransferEntropyCalculator}, they will need
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* to explicitly disambiguate between {@link #K_PROP_NAME}
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* and {@link TransferEntropyCalculator#K_PROP_NAME}
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*/
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public static final String K_PROP_NAME = "k_HISTORY";
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/**
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* Initialise the calculator for re-use with new observations.
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* History length k, source and destination dimensions are
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* specified here; all other parameters remain unchanged.
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*
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* @param k destination history embedding length to be considered.
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* @param sourceDimensions number of joint variables in the source
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* @param destDimensions number of joint variables in the destination
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* @throws Exception
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*/
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public void initialise(int k, int sourceDimensions, int destDimensions) throws Exception;
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}
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