mirror of https://github.com/jlizier/jidt
Added proper full definitions for the methods of the AIS multivariate interface; much of this is achieved by extending the univariate AIS interface (which mandates that the univariate methods must be supported). No current child classes required any changes as they all supported these already. Removed superfluous documentation in the AIS multivariate via MI class (as well as fixing whitespace inconsistencies therein)
This commit is contained in:
jlizier
parent
da418c31a0
commit
e1bad39e4e
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@ -185,7 +185,7 @@ public interface ActiveInfoStorageCalculator extends
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* @param numTimeSteps number of time steps to extract starting from startTime
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*/
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public void addObservations(double[] observations,
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int startTime, int numTimeSteps) throws Exception ;
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int startTime, int numTimeSteps) throws Exception;
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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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@ -51,8 +51,9 @@ package infodynamics.measures.continuous;
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* <li>{@link #startAddObservations()}, then</li>
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* <li>One or more calls to {@link #addObservations(double[][])} or
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* {@link #addObservations(double[][], int, int)}, then</li>
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* <li>{@link #finaliseAddObservations()};</li>
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* <li>{@link #finaliseAddObservations()}; OR</li>
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* </ol></li>
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* <li>univariate calls as supported by {@link ActiveInfoStorageCalculator} </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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@ -87,10 +88,10 @@ package infodynamics.measures.continuous;
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*
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* @see ActiveInfoStorageCalculator
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*/
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public interface ActiveInfoStorageCalculatorMultiVariate {
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public interface ActiveInfoStorageCalculatorMultiVariate extends ActiveInfoStorageCalculator {
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/**
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* Initialise the calculator for re-use with new observations.
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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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@ -101,6 +102,135 @@ public interface ActiveInfoStorageCalculatorMultiVariate {
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*/
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public void initialise(int dimensions, int k, int tau) throws Exception;
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// TODO We seem to be missing a lot of functionality which should be defined here,
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// as implemented in the univariate measure. will have to go back and do this.
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/**
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* <p>Sets a single set of <b>univariate</b> observations to compute the PDFs from.
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* Can only be called on this multivariate calculator if the dimension of the system
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* is 1, otherwise throws an exception</p>
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*
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* {@inheritDoc}
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*
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* @throws Exception if initialised dimensions were not 1
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*/
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@Override
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public void setObservations(double observations[]) throws Exception;
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/**
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* As per {@link #setObservations(double[])} only with multivariate
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* observations
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*
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* @param observations time-series array of (multivariate) samples,
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* where the first index is time.
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* @throws Exception
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*/
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public void setObservations(double[][] observations) throws Exception;
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/**
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* <p>Adds a new set of <b>univariate</b> observations to compute the PDFs from.
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* Can only be called on this multivariate calculator if the dimension of the system
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* is 1, otherwise throws an exception</p>
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*
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* {@inheritDoc}
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*
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* @throws Exception if initialised dimensions were not 1
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*/
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@Override
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public void addObservations(double[] observations) throws Exception;
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/**
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* As per {@link #addObservations(double[])} only with multivariate
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* observations
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*
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* @param observations time-series array of (multivariate) samples,
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* where the first index is time.
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* @throws Exception
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*/
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public void addObservations(double[][] observations) throws Exception;
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/**
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* This method will return an exception if this class was not initialised
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* for univariate data only.
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*
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* {@inheritDoc}
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*
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* @throws Exception if initialised dimensions were not 1
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*/
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@Override
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public void addObservations(double[] observations,
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int startTime, int numTimeSteps) throws Exception;
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/**
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* As per {@link #addObservations(double[], int, int)} only with multivariate
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* observations
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*
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* @param observations time-series array of (multivariate) samples,
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* where the first index is time.
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* @throws Exception
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*/
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public void addObservations(double[][] observations, int startTime,
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int numTimeSteps) throws Exception;
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/**
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* This method will return an exception if this class was not initialised
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* for univariate data only.
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*
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* {@inheritDoc}
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*
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* @throws Exception if initialised dimensions were not 1
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*/
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@Override
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public void setObservations(double[] observations,
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boolean[] valid) throws Exception;
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/**
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* As per {@link #setObservations(double[], boolean[])} only with multivariate
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* observations
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*
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* @param observations time-series array of (multivariate) samples,
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* where the first index is time.
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* @throws Exception
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*/
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public void setObservations(double[][] observations,
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boolean[] valid) throws Exception;
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/**
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* This method will return an exception if this class was not initialised
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* for univariate data only.
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*
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* {@inheritDoc}
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*
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* @throws Exception if initialised dimensions were not 1
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*/
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@Override
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public void addObservations(double[] observations, boolean[] valid) throws Exception;
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/**
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* As per {@link #addObservations(double[], boolean[])} only with multivariate
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* observations
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*
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* @param observations time-series array of (multivariate) samples,
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* where the first index is time.
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* @throws Exception
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*/
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public void addObservations(double[][] observations, boolean[] valid) throws Exception;
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/**
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* This method will return an exception if this class was not initialised
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* for univariate data only.
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*
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* {@inheritDoc}
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*
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* @throws Exception if initialised dimensions were not 1
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*/
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@Override
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public double[] computeLocalUsingPreviousObservations(double[] newObservations) throws Exception;
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/**
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* As per {@link #computeLocalUsingPreviousObservations(double[])} only with multivariate
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* observations
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*
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* @param newObservations time-series array of (multivariate) samples,
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* where the first index is time.
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* @throws Exception
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*/
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public double[] computeLocalUsingPreviousObservations(double[][] newObservations) throws Exception;
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}
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@ -156,16 +156,6 @@ public class ActiveInfoStorageCalculatorMultiVariateViaMutualInfo
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// miCalc.initialise(k*dimensions, dimensions);
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}
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/**
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* <p>Sets a single set of <b>univariate</b> observations to compute the PDFs from.
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* Can only be called on this multivariate calculator if the dimension of the system
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* is 1, otherwise throws an exception</p>
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*
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* {@inheritDoc}
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*
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* @param observations univariate observations
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* @throws Exception if initialised dimensions were not 1
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*/
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@Override
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public void setObservations(double[] observations) throws Exception {
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if (dimensions != 1) {
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@ -175,55 +165,47 @@ public class ActiveInfoStorageCalculatorMultiVariateViaMutualInfo
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super.setObservations(observations);
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}
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/**
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* <p>Sets a single set of <b>multivariate</b> observations to compute the
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* PDFs from. Cannot be called in conjunction with other methods for
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* setting/adding observations.
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*
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* @param observations time-series array of (multivariate) samples,
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* where the first index is time.
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*/
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public void setObservations(double[][] observations) throws Exception {
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public void setObservations(double[][] observations) throws Exception {
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startAddObservations();
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addObservations(observations);
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finaliseAddObservations();
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// if (observations.length <= timeForFirstEmbedding + 1) {
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// if (observations.length <= timeForFirstEmbedding + 1) {
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// // There are no observations to add here, the time series is too short
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// throw new Exception("Not enough observations to set here given k and tau parameters");
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// }
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// double[][] past = MatrixUtils.makeDelayEmbeddingVector(observations,
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// k, tau, tau*(k-1), observations.length - (k-1)*tau - 1);
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// double[][] next = MatrixUtils.makeDelayEmbeddingVector(observations,
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// 1, (k-1)*tau + 1, observations.length - (k-1)*tau - 1);
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// double[][] past = MatrixUtils.makeDelayEmbeddingVector(observations,
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// k, tau, tau*(k-1), observations.length - (k-1)*tau - 1);
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// double[][] next = MatrixUtils.makeDelayEmbeddingVector(observations,
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// 1, (k-1)*tau + 1, observations.length - (k-1)*tau - 1);
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// miCalc.setObservations(past, next);
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}
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// miCalc.setObservations(past, next);
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}
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/* (non-Javadoc)
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* @see infodynamics.measures.continuous.ActiveInfoStorageCalculator#startAddObservations()
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*/
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public void startAddObservations() {
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if (dimensions == 1) {
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super.startAddObservations();
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} else {
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miCalc.startAddObservations();
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vectorOfMultiVariateObservationTimeSeries = new Vector<double[][]>();
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vectorOfValidityOfObservations = new Vector<boolean[]>();
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}
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if (dimensions == 1) {
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super.startAddObservations();
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} else {
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miCalc.startAddObservations();
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vectorOfMultiVariateObservationTimeSeries = new Vector<double[][]>();
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vectorOfValidityOfObservations = new Vector<boolean[]>();
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}
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}
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/* (non-Javadoc)
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* @see infodynamics.measures.continuous.ActiveInfoStorageCalculator#finaliseAddObservations()
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*/
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public void finaliseAddObservations() throws Exception {
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super.finaliseAddObservations();
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public void finaliseAddObservations() throws Exception {
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super.finaliseAddObservations();
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vectorOfMultiVariateObservationTimeSeries = null; // No longer required
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vectorOfValidityOfObservations = null;
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}
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vectorOfMultiVariateObservationTimeSeries = null; // No longer required
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vectorOfValidityOfObservations = null;
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}
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/**
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* Prepare the given pre-instantiated (and properties supplied)
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@ -266,16 +248,6 @@ public class ActiveInfoStorageCalculatorMultiVariateViaMutualInfo
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miCalc_in_use.finaliseAddObservations();
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}
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/**
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* <p>Adds a new set of <b>univariate</b> observations to compute the PDFs from.
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* Can only be called on this multivariate calculator if the dimension of the system
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* is 1, otherwise throws an exception</p>
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*
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* {@inheritDoc}
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*
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* @param observations univariate observations
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* @throws Exception if initialised dimensions were not 1
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*/
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@Override
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public void addObservations(double[] observations) throws Exception {
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@ -286,21 +258,18 @@ public class ActiveInfoStorageCalculatorMultiVariateViaMutualInfo
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super.addObservations(observations);
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}
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/* (non-Javadoc)
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* @see infodynamics.measures.continuous.ActiveInfoStorageCalculator#addObservations(double[])
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*/
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public void addObservations(double[][] observations) throws Exception {
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public void addObservations(double[][] observations) throws Exception {
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if (dimensions == 1) {
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if ((observations.length > 0) && (observations[0].length != dimensions)) {
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throw new Exception("Observations with dimension > 1 supplied when calculator only initialised for dimension 1");
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}
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addObservations(MatrixUtils.selectColumn(observations, 0));
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} else {
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// Store these observations in our vector for now
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vectorOfMultiVariateObservationTimeSeries.add(observations);
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vectorOfValidityOfObservations.add(null); // All observations were valid
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}
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if (dimensions == 1) {
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if ((observations.length > 0) && (observations[0].length != dimensions)) {
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throw new Exception("Observations with dimension > 1 supplied when calculator only initialised for dimension 1");
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}
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addObservations(MatrixUtils.selectColumn(observations, 0));
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} else {
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// Store these observations in our vector for now
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vectorOfMultiVariateObservationTimeSeries.add(observations);
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vectorOfValidityOfObservations.add(null); // All observations were valid
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}
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// if (observations.length <= timeForFirstEmbedding + 1) {
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@ -310,13 +279,13 @@ public class ActiveInfoStorageCalculatorMultiVariateViaMutualInfo
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// return;
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// }
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// double[][] past = MatrixUtils.makeDelayEmbeddingVector(observations,
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// k, tau, tau*(k-1), observations.length - (k-1)*tau - 1);
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// double[][] next = MatrixUtils.makeDelayEmbeddingVector(observations,
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// 1, (k-1)*tau + 1, observations.length - (k-1)*tau - 1);
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// double[][] past = MatrixUtils.makeDelayEmbeddingVector(observations,
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// k, tau, tau*(k-1), observations.length - (k-1)*tau - 1);
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// double[][] next = MatrixUtils.makeDelayEmbeddingVector(observations,
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// 1, (k-1)*tau + 1, observations.length - (k-1)*tau - 1);
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// miCalc.addObservations(past, next);
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}
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// miCalc.addObservations(past, next);
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}
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/* (non-Javadoc)
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* @see infodynamics.measures.continuous.ActiveInfoStorageCalculator#addObservations(double[], int, int)
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@ -325,34 +294,31 @@ public class ActiveInfoStorageCalculatorMultiVariateViaMutualInfo
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public void addObservations(double[] observations, int startTime,
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int numTimeSteps) throws Exception {
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if (dimensions != 1) {
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if (dimensions != 1) {
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throw new Exception("Cannot call the univariate addObservations if you " +
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"have initialised with dimension > 1");
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}
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}
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super.addObservations(observations, startTime, numTimeSteps);
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super.addObservations(observations, startTime, numTimeSteps);
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}
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/* (non-Javadoc)
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* @see infodynamics.measures.continuous.ActiveInfoStorageCalculator#addObservations(double[], int, int)
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*/
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public void addObservations(double[][] observations, int startTime,
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int numTimeSteps) throws Exception {
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public void addObservations(double[][] observations, int startTime,
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int numTimeSteps) throws Exception {
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if (observations.length < startTime + numTimeSteps) {
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if (observations.length < startTime + numTimeSteps) {
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// There are not enough observations given the arguments here
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throw new Exception("Not enough observations to set here given startTime and numTimeSteps parameters");
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}
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if (dimensions == 1) {
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if ((observations.length > 0) && (observations[0].length != dimensions)) {
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throw new Exception("Observations with dimension > 1 supplied when calculator only initialised for dimension 1");
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}
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super.addObservations(MatrixUtils.selectColumn(observations, 0), startTime, numTimeSteps);
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} else {
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addObservations(MatrixUtils.selectRows(observations, startTime, numTimeSteps));
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}
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}
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if (dimensions == 1) {
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if ((observations.length > 0) && (observations[0].length != dimensions)) {
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throw new Exception("Observations with dimension > 1 supplied when calculator only initialised for dimension 1");
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}
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super.addObservations(MatrixUtils.selectColumn(observations, 0), startTime, numTimeSteps);
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} else {
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addObservations(MatrixUtils.selectRows(observations, startTime, numTimeSteps));
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}
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}
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@Override
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public void addObservations(double[] observations, boolean[] valid)
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@ -388,17 +354,14 @@ public class ActiveInfoStorageCalculatorMultiVariateViaMutualInfo
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public void setObservations(double[] observations, boolean[] valid)
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throws Exception {
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if (dimensions != 1) {
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if (dimensions != 1) {
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throw new Exception("Cannot call the univariate addObservations if you " +
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"have initialised with dimension > 1");
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}
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}
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super.setObservations(observations, valid);
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super.setObservations(observations, valid);
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}
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/**
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* TODO: docs
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*/
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public void setObservations(double[][] observations, boolean[] valid)
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throws Exception {
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startAddObservations();
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@ -487,11 +450,8 @@ public class ActiveInfoStorageCalculatorMultiVariateViaMutualInfo
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}
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/* (non-Javadoc)
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* @see infodynamics.measures.continuous.ActiveInfoStorageCalculator#computeLocaUsingPreviousObservations(double[])
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*/
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public double[] computeLocalUsingPreviousObservations(double[][] newObservations) throws Exception {
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// TODO: perhaps throw exception if time series is too short
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public double[] computeLocalUsingPreviousObservations(double[][] newObservations) throws Exception {
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// TODO: perhaps throw exception if time series is too short
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double[][] newDestPastVectors =
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MatrixUtils.makeDelayEmbeddingVector(newObservations, k, tau, (k-1)*tau, newObservations.length - (k-1)*tau - 1);
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double[][] newDestNextVectors =
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@ -501,7 +461,6 @@ public class ActiveInfoStorageCalculatorMultiVariateViaMutualInfo
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double[] localsToReturn = new double[local.length + (k-1)*tau + 1];
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System.arraycopy(local, 0, localsToReturn, (k-1)*tau + 1, local.length);
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return localsToReturn;
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}
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}
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}
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