mirror of https://github.com/jlizier/jidt
271 lines
11 KiB
Java
271 lines
11 KiB
Java
/*
|
|
* 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.measures.continuous;
|
|
|
|
import infodynamics.utils.EmpiricalNullDistributionComputer;
|
|
|
|
/**
|
|
* Interface for implementations of
|
|
* Active Information Storage estimators on continuous univariate data (ie double[] arrays).
|
|
*
|
|
* <p>See definition of Active Information Storage (AIS) by Lizier et al. below.
|
|
* Basically, AIS is the mutual information between the past <i>state</i>
|
|
* of a time-series process <i>X</i> and its next value. The past <i>state</i> at time <code>n</code>
|
|
* is represented by an embedding vector of <code>k</code> values from <code>X_n</code> backwards,
|
|
* each separated by <code>\tau</code> steps, giving
|
|
* <code><b>X^k_n</b> = [ X_{n-(k-1)\tau}, ... , X_{n-\tau}, X_n]</code>.
|
|
* We call <code>k</code> the embedding dimension, and <code>\tau</code>
|
|
* the embedding delay.
|
|
* AIS is then the mutual information between <b>X^k_n</b> and X_{n+1}.</p>
|
|
*
|
|
* <p>
|
|
* Usage of the child classes implementing this interface is intended to follow this paradigm:
|
|
* </p>
|
|
* <ol>
|
|
* <li>Construct the calculator;</li>
|
|
* <li>Set properties using {@link #setProperty(String, String)};</li>
|
|
* <li>Initialise the calculator using {@link #initialise()} or
|
|
* {@link #initialise(int)} or {@link #initialise(int, int)};
|
|
* </li>
|
|
* <li>Provide the observations/samples for the calculator
|
|
* to set up the PDFs, using:
|
|
* <ul>
|
|
* <li>{@link #setObservations(double[])} or
|
|
* {@link #setObservations(double[], boolean[])} for calculations
|
|
* based on single time-series, OR</li>
|
|
* <li>The following sequence:<ol>
|
|
* <li>{@link #startAddObservations()}, then</li>
|
|
* <li>One or more calls to {@link #addObservations(double[])} or
|
|
* {@link #addObservations(double[], int, int)}, then</li>
|
|
* <li>{@link #finaliseAddObservations()};</li>
|
|
* </ol></li>
|
|
* </ul>
|
|
* </li>
|
|
* <li>Compute the required quantities, being one or more of:
|
|
* <ul>
|
|
* <li>the average AIS: {@link #computeAverageLocalOfObservations()};</li>
|
|
* <li>the local AIS values for these samples: {@link #computeLocalOfPreviousObservations()}</li>
|
|
* <li>local AIS values for a specific set of samples: {@link #computeLocalUsingPreviousObservations(double[])}</li>
|
|
* <li>the distribution of AIS values under the null hypothesis
|
|
* of no relationship between past sequences in the series
|
|
* and the next value: {@link #computeSignificance(int)} or
|
|
* {@link #computeSignificance(int[][])}.</li>
|
|
* </ul>
|
|
* </li>
|
|
* <li>
|
|
* Return to step 2 or 3 to re-use the calculator on a new data set.
|
|
* </li>
|
|
* </ol>
|
|
*
|
|
* <p><b>References:</b><br/>
|
|
* <ul>
|
|
* <li>J.T. Lizier, M. Prokopenko and A.Y. Zomaya,
|
|
* <a href="http://dx.doi.org/10.1016/j.ins.2012.04.016">
|
|
* "Local measures of information storage in complex distributed computation"</a>,
|
|
* Information Sciences, vol. 208, pp. 39-54, 2012.</li>
|
|
* </ul>
|
|
*
|
|
* @author Joseph Lizier (<a href="joseph.lizier at gmail.com">email</a>,
|
|
* <a href="http://lizier.me/joseph/">www</a>)
|
|
*/
|
|
public interface ActiveInfoStorageCalculator extends
|
|
InfoMeasureCalculatorContinuous, EmpiricalNullDistributionComputer {
|
|
|
|
/**
|
|
* Property name for embedding length <code>k</code> of
|
|
* the past history vector (1 by default).
|
|
*/
|
|
public static final String K_PROP_NAME = "k_HISTORY";
|
|
/**
|
|
* Property name for embedding delay <code>\tau</code> of the past history vector
|
|
* (1 by default).
|
|
* The delay exists between points in the past k-vector
|
|
* but there is always only one time step between the
|
|
* past k-vector and the next observation.
|
|
*/
|
|
public static final String TAU_PROP_NAME = "TAU";
|
|
|
|
/**
|
|
* Initialise the calculator for (re-)use, with some parameters
|
|
* supplied here, and existing (or default) values of other parameters
|
|
* to be used.
|
|
*
|
|
* @param k embedding length of past history vector
|
|
*/
|
|
public void initialise(int k) throws Exception;
|
|
|
|
/**
|
|
* Initialise the calculator for (re-)use, with some parameters
|
|
* supplied here, and existing (or default) values of other parameters
|
|
* to be used.
|
|
*
|
|
* @param k embedding length of past history vector
|
|
* @param tau embedding delay of past history vector
|
|
*/
|
|
public void initialise(int k, int tau) throws Exception;
|
|
|
|
/**
|
|
* Set properties for the underlying calculator implementation.
|
|
* New property values are not guaranteed to take effect until the next call
|
|
* to an initialise method.
|
|
*
|
|
* <p>Property names defined at the interface level, and what their
|
|
* values should represent, include:</p>
|
|
* <ul>
|
|
* <li>{@link #K_PROP_NAME} -- embedding length <code>k</code> of
|
|
* the past history vector</li>
|
|
* <li>{@link #TAU_PROP_NAME} -- embedding delay between each of the
|
|
* <code>k</code> points in the past history vector.</li>
|
|
* </ul>
|
|
*
|
|
* <p>Unknown property values are ignored.</p>
|
|
*
|
|
* <p>Note that implementing classes may defined additional properties.</p>
|
|
*
|
|
* @param propertyName name of the property
|
|
* @param propertyValue value of the property
|
|
* @throws Exception for invalid property values
|
|
*/
|
|
@Override
|
|
public void setProperty(String propertyName, String propertyValue) throws Exception;
|
|
|
|
/**
|
|
* Sets a single time-series from which to compute the PDF for the AIS.
|
|
* Cannot be called in conjunction with other methods for setting/adding
|
|
* observations.
|
|
*
|
|
* @param observations time-series array of (univariate) samples,
|
|
* where the array index is time.
|
|
*/
|
|
public void setObservations(double observations[]) throws Exception;
|
|
|
|
/**
|
|
* Signal that we will add in the samples for computing the PDF
|
|
* from several disjoint time-series or trials via calls to
|
|
* {@link #addObservations(double[])} etc.
|
|
*
|
|
*/
|
|
public void startAddObservations();
|
|
|
|
/**
|
|
* Add more time-series for the computation of the PDF.
|
|
* The array observations must not be over-written by the user
|
|
* until after finaliseAddObservations() has been called.
|
|
*
|
|
* @param observations time-series array of (univariate) samples,
|
|
* where the array index is time.
|
|
*/
|
|
public void addObservations(double[] observations) throws Exception;
|
|
|
|
/**
|
|
* Add more time-series for the computation of the PDF, using
|
|
* only a sub-series of <code>observations</code>.
|
|
* The array observations must not be over-written by the user
|
|
* until after finaliseAddObservations() has been called.
|
|
*
|
|
* @param observations time-series array of (univariate) samples,
|
|
* where the array index is time.
|
|
* @param startTime first time index to extract samples from
|
|
* @param numTimeSteps number of time steps to extract starting from startTime
|
|
*/
|
|
public void addObservations(double[] observations,
|
|
int startTime, int numTimeSteps) throws Exception;
|
|
|
|
/**
|
|
* Signal that the observations are now all added, PDFs can now be constructed.
|
|
*
|
|
* @throws Exception
|
|
*/
|
|
public void finaliseAddObservations() throws Exception;
|
|
|
|
/**
|
|
* Sets a single time-series from which to compute the PDF for the AIS,
|
|
* subject to the validity of each sample in that series.
|
|
* Cannot be called in conjunction with other methods for setting/adding
|
|
* observations.
|
|
*
|
|
* @param observations time-series array of (univariate) samples,
|
|
* where the array index is time.
|
|
* @param valid a time series (with indices the same as observations)
|
|
* indicating whether the entry in observations at that index is valid;
|
|
* we only take vectors as samples to add to the observation set where
|
|
* all points in the time series (even between points in
|
|
* the embedded k-vector with embedding delays) are valid.
|
|
*/
|
|
public void setObservations(double[] observations,
|
|
boolean[] valid) throws Exception;
|
|
|
|
/**
|
|
* Add more time-series for the computation of the PDF,
|
|
* subject to the validity of each sample in that series.
|
|
* The array observations must not be over-written by the user
|
|
* until after finaliseAddObservations() has been called.
|
|
*
|
|
* @param observations time-series array of (univariate) samples,
|
|
* where the array index is time.
|
|
* @param valid a time series (with indices the same as observations)
|
|
* indicating whether the entry in observations at that index is valid;
|
|
* we only take vectors as samples to add to the observation set where
|
|
* all points in the time series (even between points in
|
|
* the embedded k-vector with embedding delays) are valid.
|
|
*/
|
|
public void addObservations(double[] observations, boolean[] valid) throws Exception;
|
|
|
|
/**
|
|
* Compute the local AIS values for each of the
|
|
* previously-supplied samples.
|
|
*
|
|
* <p>PDFs are computed using all of the previously supplied
|
|
* observations.</p>
|
|
*
|
|
* <p>If the samples were supplied via a single call such as
|
|
* {@link #setObservations(double[])},
|
|
* then the return value is a single time-series of local
|
|
* AIS values corresponding to these samples (with the first
|
|
* <code>(k-1)*tau + 1</code>
|
|
* values set to 0 since AIS is undefined there).</p>
|
|
*
|
|
* <p>Otherwise where disjoint time-series observations were supplied using several
|
|
* calls such as {@link addObservations(double[])}
|
|
* then the local values for each disjoint observation set will be appended here
|
|
* to create a single "time-series" return array (without any <code>(k-1)*tau + 1</code>
|
|
* leading 0 values).</p>
|
|
*
|
|
* @return the "time-series" of local AIS values.
|
|
*/
|
|
public double[] computeLocalOfPreviousObservations() throws Exception;
|
|
|
|
/**
|
|
* Compute the local AIS values for each of the
|
|
* supplied samples in <code>newObservations</code>.
|
|
*
|
|
* <p>PDFs are computed using all of the previously supplied
|
|
* observations, but not those in <code>newObservations</code> (unless they were
|
|
* some of the previously supplied samples).</p>
|
|
*
|
|
* @param newObservations time-series for which to compute local AIS values
|
|
* @return time-series of local AIS values corresponding to newObservations
|
|
* (the first <code>(k-1)*tau + 1</code>
|
|
* values are set to 0 since AIS is undefined there)
|
|
* @throws Exception for an invalid input array, e.g. of length less than <code>k+1</code>
|
|
*/
|
|
public double[] computeLocalUsingPreviousObservations(double[] newObservations) throws Exception;
|
|
|
|
}
|