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/*
* 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.kraskov;
import java.util.Hashtable;
import java.util.Iterator;
import java.util.Vector;
import infodynamics.measures.continuous.ActiveInfoStorageCalculator;
import infodynamics.measures.continuous.ConditionalMutualInfoCalculatorMultiVariate;
import infodynamics.measures.continuous.TransferEntropyCalculator;
import infodynamics.measures.continuous.TransferEntropyCalculatorViaCondMutualInfo;
/**
* <p>Computes the differential transfer entropy (TE) between two univariate
* <code>double[]</code> time-series of observations
* (implementing {@link TransferEntropyCalculator}),
* using Kraskov-Stoegbauer-Grassberger (KSG) estimation (see references below).
* This estimator is realised here by plugging in
* a {@link ConditionalMutualInfoCalculatorMultiVariateKraskov}
* as the calculator into the parent class {@link TransferEntropyCalculatorViaCondMutualInfo}.</p>
*
* <p>Crucially, the calculation is performed by examining
* neighbours in the full joint space (as specified by Frenzel and Pompe,
* and Gomez-Herrero et al.)
* rather than two MI calculators.</p>
*
* <p>Usage is as per the paradigm outlined for {@link TransferEntropyCalculator},
* with:
* <ul>
* <li>The constructor step is either a simple call to {@link #TransferEntropyCalculatorKraskov()},
* or else specifies which KSG algorithm to implement via
* {@link #TransferEntropyCalculatorKraskov(String)};</li>
* <li>{@link #setProperty(String, String)} allowing properties defined for both
* {@link TransferEntropyCalculator#setProperty(String, String)} and
* {@link ConditionalMutualInfoCalculatorMultiVariateKraskov#setProperty(String, String)}
* as outlined
* in {@link TransferEntropyCalculatorViaCondMutualInfo#setProperty(String, String)});
* as well as for {@link #PROP_KRASKOV_ALG_NUM}.
* Embedding parameters may be automatically determined as per the Ragwitz criteria
* by setting the property {@link #PROP_AUTO_EMBED_METHOD} to {@link #AUTO_EMBED_METHOD_RAGWITZ}
* or {@link #AUTO_EMBED_METHOD_RAGWITZ_DEST_ONLY},
* or as per the max. bias-corrected AIS criteria by
* setting the property {@link #PROP_AUTO_EMBED_METHOD} to {@link #AUTO_EMBED_METHOD_MAX_CORR_AIS}
* (as per Garland et al. in the reference list)
* plus additional parameter settings for these.</li>
* </li>
* <li>Computed values are in <b>nats</b>, not bits!</li>
* </ul>
* </p>
*
* <p><b>References:</b><br/>
* <ul>
* <li>T. Schreiber, <a href="http://dx.doi.org/10.1103/PhysRevLett.85.461">
* "Measuring information transfer"</a>,
* Physical Review Letters 85 (2) pp.461-464, 2000.</li>
* <li>Frenzel and Pompe, <a href="http://dx.doi.org/10.1103/physrevlett.99.204101">
* "Partial Mutual Information for Coupling Analysis of Multivariate Time Series"</a>,
* Physical Review Letters, <b>99</b>, p. 204101+ (2007).</li>
* <li>G. Gomez-Herrero, W. Wu, K. Rutanen, M. C. Soriano, G. Pipa, and R. Vicente,
* <a href="http://arxiv.org/abs/1008.0539">
* "Assessing coupling dynamics from an ensemble of time series"</a>,
* arXiv:1008.0539 (2010).</li>
* <li>Kraskov, A., Stoegbauer, H., Grassberger, P.,
* <a href="http://dx.doi.org/10.1103/PhysRevE.69.066138">"Estimating mutual information"</a>,
* Physical Review E 69, (2004) 066138.</li>
* <li>J. T. Lizier, M. Prokopenko and A. Zomaya,
* <a href="http://dx.doi.org/10.1103/PhysRevE.77.026110">
* "Local information transfer as a spatiotemporal filter for complex systems"</a>
* Physical Review E 77, 026110, 2008.</li>
* <li>Ragwitz and Kantz, "Markov models from data by simple nonlinear time series
* predictors in delay embedding spaces", Physical Review E, vol 65, 056201 (2002).</li>
* <li>J. Garland, R. G. James, E. Bradley, <a href="http://dx.doi.org/10.1103/physreve.93.022221">
* "Leveraging information storage to select forecast-optimal parameters for delay-coordinate reconstructions"</a>,
* Physical Review E, Vol. 93 (2016), 022221, doi:</li>
* </ul>
*
* @author Joseph Lizier (<a href="joseph.lizier at gmail.com">email</a>,
* <a href="http://lizier.me/joseph/">www</a>)
* @see TransferEntropyCalculator
* @see ConditionalMutualInfoCalculatorMultiVariateKraskov
*/
public class TransferEntropyCalculatorKraskov
extends TransferEntropyCalculatorViaCondMutualInfo {
/**
* Class name for KSG conditional MI estimator via KSG algorithm 1
*/
public static final String COND_MI_CALCULATOR_KRASKOV1 = ConditionalMutualInfoCalculatorMultiVariateKraskov1.class.getName();
/**
* Class name for KSG conditional MI estimator via KSG algorithm 2
*/
public static final String COND_MI_CALCULATOR_KRASKOV2 = ConditionalMutualInfoCalculatorMultiVariateKraskov2.class.getName();
/**
* Property for setting which underlying Kraskov-Grassberger algorithm to use (1 or 2).
* Will only be applied at the next initialisation.
*/
public final static String PROP_KRASKOV_ALG_NUM = "ALG_NUM";
/**
* Which Kraskov algorithm number we are using
*/
protected int kraskovAlgorithmNumber = 1;
protected boolean algChanged = false;
/**
* Storage for the properties ready to pass onto the underlying conditional MI calculators should they change
*/
protected Hashtable<String,String> props;
/**
* Property name for specifying which (if any) auto-embedding method to use.
* Valid values include {@link #AUTO_EMBED_METHOD_RAGWITZ}, {@link #AUTO_EMBED_METHOD_RAGWITZ_DEST_ONLY},
* {@link #AUTO_EMBED_METHOD_MAX_CORR_AIS}, {@link #AUTO_EMBED_METHOD_MAX_CORR_AIS_DEST_ONLY},
* {@link #AUTO_EMBED_METHOD_MAX_CORR_AIS_AND_TE} and {@link #AUTO_EMBED_METHOD_NONE}.
* Defaults to {@link #AUTO_EMBED_METHOD_NONE}
*/
public static final String PROP_AUTO_EMBED_METHOD = "AUTO_EMBED_METHOD";
/**
* Valid value for the property {@link #PROP_AUTO_EMBED_METHOD} indicating that
* no auto embedding should be done (i.e. to use manually supplied parameters)
*/
public static final String AUTO_EMBED_METHOD_NONE = "NONE";
/**
* Valid value for the property {@link #PROP_AUTO_EMBED_METHOD} indicating that
* the Ragwitz optimisation technique should be used for automatic embedding
* for both source and destination time-series
*/
public static final String AUTO_EMBED_METHOD_RAGWITZ = "RAGWITZ";
/**
* Valid value for the property {@link #PROP_AUTO_EMBED_METHOD} indicating that
* the Ragwitz optimisation technique should be used for automatic embedding
* for the destination time-series only
*/
public static final String AUTO_EMBED_METHOD_RAGWITZ_DEST_ONLY = "RAGWITZ_DEST_ONLY";
/**
* Valid value for the property {@link #PROP_AUTO_EMBED_METHOD} indicating that
* the automatic embedding should be done by maximising the bias corrected
* AIS, for both source and destination time series
*/
public static final String AUTO_EMBED_METHOD_MAX_CORR_AIS = "MAX_CORR_AIS";
/**
* Valid value for the property {@link #PROP_AUTO_EMBED_METHOD} indicating that
* the automatic embedding should be done by maximising the bias corrected
* AIS for the target and subsequently maximising the TE over source embeddings,
* given a fixed source-target delay.
*/
public static final String AUTO_EMBED_METHOD_MAX_CORR_AIS_AND_TE = "MAX_CORR_AIS_AND_TE";
/**
* Valid value for the property {@link #PROP_AUTO_EMBED_METHOD} indicating that
* the automatic embedding should be done by maximising the bias corrected
* AIS, for destination time series only
*/
public static final String AUTO_EMBED_METHOD_MAX_CORR_AIS_DEST_ONLY = "MAX_CORR_AIS_DEST_ONLY";
/**
* Internal variable tracking what type of auto embedding (if any)
* we are using
*/
protected String autoEmbeddingMethod = AUTO_EMBED_METHOD_NONE;
/**
* Property name for maximum embedding lengths (i.e. k for destination, and l for source if we're auto-embedding
* the source as well) for the auto-embedding search. Defaults to 1
*/
public static final String PROP_K_SEARCH_MAX = "AUTO_EMBED_K_SEARCH_MAX";
/**
* Internal variable for storing the maximum embedding length to search up to for
* automating the parameters.
*/
protected int k_search_max = 1;
/**
* Property name for maximum embedding delay (i.e. k_tau for destination, and l_tau for source if we're auto-embedding
* the source as well) for the auto-embedding search. Defaults to 1
*/
public static final String PROP_TAU_SEARCH_MAX = "AUTO_EMBED_TAU_SEARCH_MAX";
/**
* Internal variable for storing the maximum embedding delay to search up to for
* automating the parameters.
*/
protected int tau_search_max = 1;
/**
* Property name for the number of nearest neighbours to use for the auto-embedding search (Ragwitz criteria).
* Defaults to match the value in use for {@link MutualInfoCalculatorMultiVariateKraskov#PROP_K}
*/
public static final String PROP_RAGWITZ_NUM_NNS = "AUTO_EMBED_RAGWITZ_NUM_NNS";
/**
* Internal variable for storing the number of nearest neighbours to use for the
* auto embedding search (Ragwitz criteria)
*/
protected int ragwitz_num_nns = 1;
/**
* Internal variable to track whether the property {@link #PROP_RAGWITZ_NUM_NNS} has been
* set yet
*/
protected boolean ragwitz_num_nns_set = false;
/**
* Creates a new instance of the Kraskov-estimate style transfer entropy calculator
*
* Uses algorithm 1 by default, as per Gomez-Herro et al.
*
* @throws ClassNotFoundException
* @throws IllegalAccessException
* @throws InstantiationException
*
*/
public TransferEntropyCalculatorKraskov() throws InstantiationException, IllegalAccessException, ClassNotFoundException {
super(COND_MI_CALCULATOR_KRASKOV1);
kraskovAlgorithmNumber = 1;
props = new Hashtable<String,String>();
}
/**
* Creates a new instance of the Kraskov-Grassberger style transfer entropy calculator,
* with the supplied conditional MI calculator name
*
* @param calculatorName fully qualified name of the underlying MI class.
* Must be {@link #COND_MI_CALCULATOR_KRASKOV1} or {@link #COND_MI_CALCULATOR_KRASKOV2}
* @throws ClassNotFoundException
* @throws IllegalAccessException
* @throws InstantiationException
*
*/
public TransferEntropyCalculatorKraskov(String calculatorName) throws InstantiationException, IllegalAccessException, ClassNotFoundException {
super(calculatorName);
// Now check that it was one of our Kraskov-Grassberger calculators:
if (calculatorName.equalsIgnoreCase(COND_MI_CALCULATOR_KRASKOV1)) {
kraskovAlgorithmNumber = 1;
} else if (calculatorName.equalsIgnoreCase(COND_MI_CALCULATOR_KRASKOV2)) {
kraskovAlgorithmNumber = 2;
} else {
throw new ClassNotFoundException("Must be an underlying Kraskov-Grassberger conditional MI calculator");
}
props = new Hashtable<String,String>();
}
/**
* Creates a new instance of the Kraskov-Grassberger style transfer entropy calculator,
* with the CMI Kraskov calculator using the given Kraskov algorithm number
*
* @param calculatorName fully qualified name of the underlying MI class.
* Must be {@link #COND_MI_CALCULATOR_KRASKOV1} or {@link #COND_MI_CALCULATOR_KRASKOV2}
* @throws ClassNotFoundException
* @throws IllegalAccessException
* @throws InstantiationException
*
*/
public TransferEntropyCalculatorKraskov(int algorithm) throws InstantiationException, IllegalAccessException, ClassNotFoundException {
super((algorithm == 1) ? COND_MI_CALCULATOR_KRASKOV1 : COND_MI_CALCULATOR_KRASKOV2);
if ((algorithm != 1) && (algorithm != 2)) {
throw new ClassNotFoundException("Algorithm must be 1 or 2");
}
props = new Hashtable<String,String>();
}
/* (non-Javadoc)
* @see infodynamics.measures.continuous.TransferEntropyCalculatorViaCondMutualInfo#initialise(int, int, int, int, int)
*/
@Override
public void initialise(int k, int k_tau, int l, int l_tau, int delay)
throws Exception {
if (algChanged) {
// The algorithm number was changed in a setProperties call:
String newCalcName = COND_MI_CALCULATOR_KRASKOV1;
if (kraskovAlgorithmNumber == 2) {
newCalcName = COND_MI_CALCULATOR_KRASKOV2;
}
@SuppressWarnings("unchecked")
Class<ConditionalMutualInfoCalculatorMultiVariate> condMiClass =
(Class<ConditionalMutualInfoCalculatorMultiVariate>) Class.forName(newCalcName);
ConditionalMutualInfoCalculatorMultiVariate newCondMiCalc = condMiClass.newInstance();
construct(newCondMiCalc);
// Set the properties for the Kraskov MI calculators (may pass in properties for our super class
// as well, but they should be ignored)
for (String key : props.keySet()) {
newCondMiCalc.setProperty(key, props.get(key));
}
algChanged = false;
}
super.initialise(k, k_tau, l, l_tau, delay);
}
/**
* Sets properties for the TE calculator.
* New property values are not guaranteed to take effect until the next call
* to an initialise method.
*
* <p>Valid property names, and what their
* values should represent, include:</p>
* <ul>
* <li>{@link #PROP_KRASKOV_ALG_NUM} -- which Kraskov algorithm number to use (1 or 2).</li>
* <li>{@link #PROP_AUTO_EMBED_METHOD} -- method by which the calculator
* automatically determines the embedding history length ({@link #K_PROP_NAME})
* and embedding delay ({@link #TAU_PROP_NAME}) for destination and potentially source.
* Default is {@link #AUTO_EMBED_METHOD_NONE} meaning
* values are set manually; other accepted values include: {@link #AUTO_EMBED_METHOD_RAGWITZ} for use
* of the Ragwitz criteria for both source and destination (searching up to {@link #PROP_K_SEARCH_MAX} and
* {@link #PROP_TAU_SEARCH_MAX}), and {@link #AUTO_EMBED_METHOD_RAGWITZ_DEST_ONLY} for use
* of the Ragwitz criteria for the destination only;
* {@link #AUTO_EMBED_METHOD_MAX_CORR_AIS} for use of the max bias corrected AIS criteria
* for both source and destination (searching up to {@link #PROP_K_SEARCH_MAX} and
* {@link #PROP_TAU_SEARCH_MAX}), {@link #AUTO_EMBED_METHOD_MAX_CORR_AIS_DEST_ONLY} for use of
* this criteria for the destination only and {@link #AUTO_EMBED_METHOD_MAX_CORR_AIS_AND_TE} for
* use of this criteria for the target, plus the max bias corrected TE for source embeddings.
* Use of any value other than {@link #AUTO_EMBED_METHOD_NONE}
* will lead to previous settings for embedding lengths and delays (via e.g. {@link #initialise(int, int)} or
* auto-embedding during previous calculations) for the destination and perhaps source to
* be overwritten after observations are supplied.</li>
* <li>{@link #PROP_K_SEARCH_MAX} -- maximum embedded history length to search
* up to if automatically determining the embedding parameters (as set by
* {@link #PROP_AUTO_EMBED_METHOD}) for the time-series to be embedded; default is 1</li>
* <li>{@link #PROP_TAU_SEARCH_MAX} -- maximum embedded history length to search
* up to if automatically determining the embedding parameters (as set by
* {@link #PROP_AUTO_EMBED_METHOD}) for the time-series to be embedded; default is 1</li>
* <li>{@link #PROP_RAGWITZ_NUM_NNS} -- number of nearest neighbours to use
* in the auto-embedding if the property {@link #PROP_AUTO_EMBED_METHOD}
* has been set to {@link #AUTO_EMBED_METHOD_RAGWITZ} or {@link #AUTO_EMBED_METHOD_RAGWITZ_DEST_ONLY}.
* Defaults to the property value
* set for {@link ConditionalMutualInfoCalculatorMultiVariateKraskov#PROP_K}</li>
* <li>Any properties accepted by {@link TransferEntropyCalculatorViaCondMutualInfo#setProperty(String, String)}</li>
* <li>Or properties accepted by the underlying
* {@link ConditionalMutualInfoCalculatorMultiVariateKraskov#setProperty(String, String)} implementation.</li>
* </ul>
* <p><b>Note:</b> further properties may be defined by child classes.</p>
*
* <p>Unknown property values are ignored.</p>
*
* @param propertyName name of the property
* @param propertyValue value of the property.
* @throws Exception if there is a problem with the supplied value).
*/
public void setProperty(String propertyName, String propertyValue)
throws Exception {
if (propertyName.equalsIgnoreCase(PROP_KRASKOV_ALG_NUM)) {
int previousAlgNumber = kraskovAlgorithmNumber;
kraskovAlgorithmNumber = Integer.parseInt(propertyValue);
if ((kraskovAlgorithmNumber != 1) && (kraskovAlgorithmNumber != 2)) {
throw new Exception("Kraskov algorithm number (" + kraskovAlgorithmNumber
+ ") must be either 1 or 2");
}
if (kraskovAlgorithmNumber != previousAlgNumber) {
algChanged = true;
}
if (debug) {
System.out.println(this.getClass().getSimpleName() + ": Set property " + propertyName +
" to " + propertyValue);
}
} else if (propertyName.equalsIgnoreCase(PROP_AUTO_EMBED_METHOD)) {
// New method set for determining the embedding parameters
autoEmbeddingMethod = propertyValue;
} else if (propertyName.equalsIgnoreCase(PROP_K_SEARCH_MAX)) {
// Set max embedding history length for auto determination of embedding
k_search_max = Integer.parseInt(propertyValue);
} else if (propertyName.equalsIgnoreCase(PROP_TAU_SEARCH_MAX)) {
// Set maximum embedding delay for auto determination of embedding
tau_search_max = Integer.parseInt(propertyValue);
} else if (propertyName.equalsIgnoreCase(PROP_RAGWITZ_NUM_NNS)) {
// Set the number of nearest neighbours to use in case of Ragwitz auto embedding:
ragwitz_num_nns = Integer.parseInt(propertyValue);
ragwitz_num_nns_set = true;
} else {
// Assume it was a property for the parent class or underlying conditional MI calculator
super.setProperty(propertyName, propertyValue);
props.put(propertyName, propertyValue); // This will keep properties for the super class as well as the cond MI calculator, but this is ok
}
}
@Override
public String getProperty(String propertyName) throws Exception {
if (propertyName.equalsIgnoreCase(PROP_KRASKOV_ALG_NUM)) {
return Integer.toString(kraskovAlgorithmNumber);
} else if (propertyName.equalsIgnoreCase(PROP_AUTO_EMBED_METHOD)) {
return autoEmbeddingMethod;
} else if (propertyName.equalsIgnoreCase(PROP_K_SEARCH_MAX)) {
return Integer.toString(k_search_max);
} else if (propertyName.equalsIgnoreCase(PROP_TAU_SEARCH_MAX)) {
return Integer.toString(tau_search_max);
} else if (propertyName.equalsIgnoreCase(PROP_RAGWITZ_NUM_NNS)) {
if (ragwitz_num_nns_set) {
return Integer.toString(ragwitz_num_nns);
} else {
return condMiCalc.getProperty(ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_K);
}
} else {
// Assume it was a property for the parent class or underlying conditional MI calculator
return super.getProperty(propertyName);
}
}
@Override
public void preFinaliseAddObservations() throws Exception {
// Automatically determine the embedding parameters for the given time series
if (autoEmbeddingMethod.equalsIgnoreCase(AUTO_EMBED_METHOD_NONE)) {
return;
}
// Else we need to auto embed
// If we need to check which embedding method later:
if (autoEmbeddingMethod.equalsIgnoreCase(AUTO_EMBED_METHOD_RAGWITZ) ||
autoEmbeddingMethod.equalsIgnoreCase(AUTO_EMBED_METHOD_RAGWITZ_DEST_ONLY) ||
autoEmbeddingMethod.equalsIgnoreCase(AUTO_EMBED_METHOD_MAX_CORR_AIS) ||
autoEmbeddingMethod.equalsIgnoreCase(AUTO_EMBED_METHOD_MAX_CORR_AIS_DEST_ONLY) ||
autoEmbeddingMethod.equalsIgnoreCase(AUTO_EMBED_METHOD_MAX_CORR_AIS_AND_TE)) {
// Use a Kraskov AIS calculator to embed both time-series individually:
ActiveInfoStorageCalculatorKraskov aisCalc = new ActiveInfoStorageCalculatorKraskov();
// Set the properties for the underlying MI Kraskov calculator here to match ours:
for (String key : props.keySet()) {
aisCalc.setProperty(key, props.get(key));
}
// Set the auto-embedding properties as we require:
if (autoEmbeddingMethod.equalsIgnoreCase(AUTO_EMBED_METHOD_RAGWITZ) ||
autoEmbeddingMethod.equalsIgnoreCase(AUTO_EMBED_METHOD_RAGWITZ_DEST_ONLY)) {
// We're doing Ragwitz auto-embedding
aisCalc.setProperty(ActiveInfoStorageCalculatorKraskov.PROP_AUTO_EMBED_METHOD,
ActiveInfoStorageCalculatorKraskov.AUTO_EMBED_METHOD_RAGWITZ);
// In case !ragwitz_num_nns_set and our condMiCalc has a different default number of
// kNNs for Kraskov search than miCalc, we had best supply the number directly here:
aisCalc.setProperty(ActiveInfoStorageCalculatorKraskov.PROP_RAGWITZ_NUM_NNS,
getProperty(PROP_RAGWITZ_NUM_NNS));
} else {
// We're doing max bias-corrected AIS embeding:
aisCalc.setProperty(ActiveInfoStorageCalculatorKraskov.PROP_AUTO_EMBED_METHOD,
ActiveInfoStorageCalculatorKraskov.AUTO_EMBED_METHOD_MAX_CORR_AIS);
}
aisCalc.setProperty(ActiveInfoStorageCalculatorKraskov.PROP_K_SEARCH_MAX,
Integer.toString(k_search_max));
aisCalc.setProperty(ActiveInfoStorageCalculatorKraskov.PROP_TAU_SEARCH_MAX,
Integer.toString(tau_search_max));
// Embed the destination:
if (debug) {
System.out.println("Starting embedding of destination:");
}
prepareAISCalculator(aisCalc, vectorOfDestinationTimeSeries, vectorOfValidityOfDestination);
// Set the auto-embedding parameters for the destination:
k = Integer.parseInt(aisCalc.getProperty(ActiveInfoStorageCalculator.K_PROP_NAME));
k_tau = Integer.parseInt(aisCalc.getProperty(ActiveInfoStorageCalculator.TAU_PROP_NAME));
if (debug) {
System.out.printf("Embedding parameters for destination set to k=%d,k_tau=%d\n",
k, k_tau);
}
if (autoEmbeddingMethod.equalsIgnoreCase(AUTO_EMBED_METHOD_RAGWITZ) ||
autoEmbeddingMethod.equalsIgnoreCase(AUTO_EMBED_METHOD_MAX_CORR_AIS)) {
// Embed the source also:
if (debug) {
System.out.println("Starting embedding of source:");
}
prepareAISCalculator(aisCalc, vectorOfSourceTimeSeries, vectorOfValidityOfSource);
// Set the auto-embedding parameters for the source:
l = Integer.parseInt(aisCalc.getProperty(ActiveInfoStorageCalculator.K_PROP_NAME));
l_tau = Integer.parseInt(aisCalc.getProperty(ActiveInfoStorageCalculator.TAU_PROP_NAME));
if (debug) {
System.out.printf("Embedding parameters for source set to l=%d,l_tau=%d\n",
l, l_tau);
}
} else if (autoEmbeddingMethod.equalsIgnoreCase(AUTO_EMBED_METHOD_MAX_CORR_AIS_AND_TE)) {
if (debug) {
System.out.println("Starting embedding of source:");
}
double bestTE = Double.NEGATIVE_INFINITY;
int l_candidate_best = 1;
int l_tau_candidate_best = 1;
// Iterate over all possible embeddings
for (int l_candidate = 1; l_candidate <= k_search_max; l_candidate++) {
for (int l_tau_candidate = 1; l_tau_candidate <= tau_search_max; l_tau_candidate++) {
// Use our internal CMI calculator in case it has any particular
// properties we need to have been set already
prepareCMICalculator(condMiCalc, k, k_tau, l_candidate, l_tau_candidate, delay);
double thisTE = condMiCalc.computeAverageLocalOfObservations();
if (debug) {
System.out.printf("TE for l=%d, l_tau=%d is %.3f\n",
l_candidate, l_tau_candidate, thisTE);
}
if (thisTE > bestTE) {
// This parameter setting is the best so far:
bestTE = thisTE;
l_candidate_best = l_candidate;
l_tau_candidate_best = l_tau_candidate;
}
if (l_candidate == 1) {
// tau is irrelevant, so no point testing other values
break;
}
}
}
l = l_candidate_best;
l_tau = l_tau_candidate_best;
if (debug) {
System.out.printf("Embedding parameters for source set to l=%d,l_tau=%d\n",
l, l_tau);
}
}
// Now that embedding parameters are finalised:
startTimeForFirstDestEmbedding =
computeStartTimeForFirstDestEmbedding(k, k_tau, l, l_tau, delay);
}
}
/**
* Prepare the given pre-instantiated (and properties supplied)
* Active information storage calculator with the given data set,
* for a calculation of auto-embedding parameters.
*
* @param aisCalc_in_use AIS calculator to supply
* @param setOfTimeSeriesSamples set of times series samples for the calculation
* @param setOfValidities set of time series of validity indications. Each can be a null array if all are valid
* @throws Exception
*/
protected static void prepareAISCalculator(ActiveInfoStorageCalculator aisCalc,
Vector<double[]> setOfTimeSeriesSamples, Vector<boolean[]> setOfValidities)
throws Exception {
aisCalc.initialise();
aisCalc.startAddObservations();
Iterator<boolean[]> validityIterator = setOfValidities.iterator();
for (double[] timeSeries : setOfTimeSeriesSamples) {
boolean[] validity = validityIterator.next();
if (validity == null) {
aisCalc.addObservations(timeSeries);
} else {
aisCalc.addObservations(timeSeries, validity);
}
}
aisCalc.finaliseAddObservations();
}
}
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