mirror of https://github.com/jlizier/jidt
262 lines
11 KiB
Java
Executable File
262 lines
11 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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import infodynamics.utils.EmpiricalMeasurementDistribution;
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/**
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* A basic interface for calculators computing measures on a <i>channel</i> from a
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* source to a destination time-series (i.e. mutual information and transfer entropy).
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* In the following, we refer to the abstract measure computed by this calculator
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* as the <i>"channel measure"</i>.
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*
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* <p>The interface is <b>abstract</b> because further specification is required
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* for addObservations or setObservations methods showing whether
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* this is a univariate ({@link ChannelCalculator}) or multivariate
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* {@link ChannelCalculatorMultiVariate} measure.</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)};</li>
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* <li>Initialise the calculator using {@link #initialise()} or
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* other initialise methods defined by child classes;
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* </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>a "setObservations" method (defined by child classes)
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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 "addObservations" methods
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* (defined by children), 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>
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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 of the channel measure: {@link #computeAverageLocalOfObservations()};</li>
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* <li>the local channel measure values for these samples: {@link #computeLocalOfPreviousObservations()}</li>
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* <li>local channel measure values for a specific set of samples: "computeLocalUsingPreviousObservations"
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* (to be defined by child classes);</li>
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* <li>the distribution of the channel measure values under the null hypothesis
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* of no relationship between source and destination
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* 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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*
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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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* @see ChannelCalculator
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* @see ChannelCalculatorMultiVariate
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*
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*/
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public abstract interface ChannelCalculatorCommon {
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/**
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* Initialise the calculator for (re-)use, with the existing
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* (or default) values of parameters.
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*
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* @throws Exception
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*/
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public void initialise() throws Exception;
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/**
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* Set properties for the calculator.
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* New property values are not guaranteed to take effect until the next call
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* to an initialise method.
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*
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* <p>No general properties are defined at the interface level here, i.e.
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* there are only properties defined by child interfaces and classes.</p>
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*
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* @param propertyName name of the property
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* @param propertyValue value of the property
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* @throws Exception for invalid property values
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*/
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public void setProperty(String propertyName, String propertyValue) throws Exception;
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/**
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* Get current property values for the calculator.
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*
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* <p>Valid property names, and what their
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* values should represent, are the same as those for
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* {@link #setProperty(String, String)}</p>
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*
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* <p>Unknown property values are responded to with a null return value.</p>
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*
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* @param propertyName name of the property
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* @return current value of the property
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* @throws Exception for invalid property values
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*/
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public String getProperty(String propertyName) throws Exception;
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/**
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* Signal that we will add in the samples for computing the PDF
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* from several disjoint time-series or trials via calls to
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* "addObservations" rather than "setObservations" type methods
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* (defined by the child interfaces and classes).
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*
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*/
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public void startAddObservations();
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/**
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* Signal that the observations are now all added, PDFs can now be constructed.
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*
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* @throws Exception
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*/
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public void finaliseAddObservations() throws Exception;
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/**
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* Query whether the user has added more than a single observation set via the
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* {@link #startAddObservations()}, "addObservations" (defined by child interfaces
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* and classes), {@link #finaliseAddObservations()} sequence.
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*
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* @return true if more than a single observation set was supplied
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*/
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public boolean getAddedMoreThanOneObservationSet();
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/**
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* Compute the channel measure from the previously-supplied samples.
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*
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* @return the estimate of the channel measure
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*/
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public double computeAverageLocalOfObservations() throws Exception;
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/**
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* <p>Computes the local values of the implemented channel measure,
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* for each valid observation in the previously supplied observations
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* (with PDFs computed using all of the previously supplied observation sets).</p>
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*
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* <p>If the samples were supplied via a single call such as
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* {@link #setObservations(double[])},
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* then the return value is a single time-series of local
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* channel measure values corresponding to these samples.</p>
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*
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* <p>Otherwise where disjoint time-series observations were supplied using several
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* calls such as {@link addObservations(double[])}
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* then the local values for each disjoint observation set will be appended here
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* to create a single "time-series" return array.</p>
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*
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* @return the "time-series" of local channel measure values.
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*/
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public double[] computeLocalOfPreviousObservations() throws Exception;
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/**
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* Generate a bootstrapped distribution of what the channel 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.
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* (Precise null hypothesis varies between MI and TE).
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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 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 {@link #addObservations(double[])} 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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* creates <i>random</i> shufflings of the next values for the surrogate AIS
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* calculations.</p>
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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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* @return the distribution of channel measure scores under this null hypothesis.
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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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*/
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public EmpiricalMeasurementDistribution computeSignificance(int numPermutationsToCheck) throws Exception;
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/**
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* Generate a bootstrapped distribution of what the channel 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.
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* (Precise null hypothesis varies between MI and TE).
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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 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 {@link #addObservations(double[])} 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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* @param newOrderings a specification of how to shuffle the source 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 {@link #getNumObservations()}),
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* containing a permutation of the values in 0..(N-1).
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* @return the distribution of channel measure scores under this null hypothesis.
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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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* @throws Exception where the length of each permutation in newOrderings
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* is not equal to the number N samples that were previously supplied.
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*/
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public EmpiricalMeasurementDistribution computeSignificance(
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int[][] newOrderings) throws Exception;
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/**
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* Set or clear debug mode for extra debug printing to stdout
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*
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* @param debug new setting for debug mode (on/off)
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*/
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public void setDebug(boolean debug);
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/**
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* Return the channel measure last calculated in a call to {@link #computeAverageLocalOfObservations()}
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* or {@link #computeLocalOfPreviousObservations()} after the previous
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* {@link #initialise()} call.
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*
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* @return the last computed channel measure value
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*/
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public double getLastAverage();
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/**
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* Get the number of samples to be used for the PDFs here
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* which have been supplied by calls to
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* {@link #setObservations(double[])}, {@link #addObservations(double[])}
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* etc.
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*
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* <p>Note that the number of samples may not be equal to the length of time-series
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* supplied (e.g. for transfer entropy, where we need to accumulate
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* a number of samples for the past history of the destination).
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* </p>
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*
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* @return the number of samples to be used for the PDFs
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*/
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public int getNumObservations() throws Exception;
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}
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