jidt/java/source/infodynamics/measures/continuous/ConditionalMutualInfoCalcul...

304 lines
12 KiB
Java
Executable File

package infodynamics.measures.continuous;
import infodynamics.utils.EmpiricalMeasurementDistribution;
/**
* <p>Interface for multivariate implementations of the
* conditional mutual information.</p>
*
* <p>
* Intended usage of the child classes:
* <ol>
* <li>Construct</li>
* <li>Set properties using {@link #setProperty(String, String)}</li>
* <li>{@link #initialise(int, int)} or {@link #initialise(int, int, double)}</li>
* <li>Provide the observations to the calculator using:
* {@link #setObservations(double[][], double[][])}, or
* {@link #setCovariance(double[][])}, or
* a sequence of:
* {@link #startAddObservations()},
* multiple calls to either {@link #addObservations(double[][], double[][])}
* or {@link #addObservations(double[][], double[][], int, int)}, and then
* {@link #finaliseAddObservations()}.</li>
* <li>Compute the required information-theoretic results, primarily:
* {@link #computeAverageLocalOfObservations()} to return the average
* value based on the supplied observations; or other calls to compute
* local values or statistical significance.</li>
* </ol>
* </p>
*
* @author Joseph Lizier, <a href="mailto:joseph.lizier at gmail.com">joseph.lizier at gmail.com</>
*
* @see "T. M. Cover and J. A. Thomas, 'Elements of Information
Theory' (John Wiley & Sons, New York, 1991)."
*/
public interface ConditionalMutualInfoCalculatorMultiVariate {
/**
* Initialise the calculator
*
* @param var1Dimensions the number of joint variables in variable 1
* @param var2Dimensions the number of joint variables in variable 2
* @param condDimensions the number of joint variables in the conditional
*/
public void initialise(int var1Dimensions, int var2Dimensions, int condDimentions) throws Exception;
/**
* Allows the user to set properties for the underlying calculator implementation
* New property values are not guaranteed to take effect until the next call
* to an initialise method.
*
* @param propertyName
* @param propertyValue
* @throws Exception
*/
public void setProperty(String propertyName, String propertyValue) throws Exception;
/**
* <p>Sets the single set of observations to compute the PDFs from.
* Cannot be called in conjunction with
* {@link #startAddObservations()}/{@link #addObservations(double[], double[], double[])} /
* {@link #finaliseAddObservations()}.</p>
*
* @param var1 multivariate observations for variable 1
* (first index is time, second is variable number)
* @param var2 multivariate observations for variable 2
* (first index is time, second is variable number)
* @param cond multivariate observations for the conditional
* (first index is time, second is variable number)
* @throws Exception
*/
public void setObservations(double[][] var1, double[][] var2,
double[][] cond) throws Exception;
/**
* <p>Sets the single set of observations to compute the PDFs from.
* Cannot be called in conjunction with
* {@link #startAddObservations()}/{@link #addObservations(double[], double[])} /
* {@link #finaliseAddObservations()}.</p>
*
* @param var1 multivariate observations for variable 1
* (first index is time, second is variable number)
* @param var2 multivariate observations for variable 2
* (first index is time, second is variable number)
* @param cond multivariate observations for the conditional
* (first index is time, second is variable number)
* @param var1Valid time series (with time indices the same as var1)
* indicating whether var1 at that point is valid.
* @param var2Valid time series (with time indices the same as var2)
* indicating whether var2 at that point is valid.
* @param condValid time series (with time indices the same as cond)
* indicating whether cond at that point is valid.
*/
public void setObservations(double[][] var1, double[][] var2,
double[][] cond,
boolean[] var1Valid, boolean[] var2Valid,
boolean[] condValid) throws Exception;
/**
* <p>Sets the single set of observations to compute the PDFs from.
* Cannot be called in conjunction with
* {@link #startAddObservations()}/{@link #addObservations(double[], double[])} /
* {@link #finaliseAddObservations()}.</p>
*
* @param var1 multivariate observations for variable 1
* (first index is time, second is variable number)
* @param var2 multivariate observations for variable 2
* (first index is time, second is variable number)
* @param cond multivariate observations for the conditional
* (first index is time, second is variable number)
* @param var1Valid time series (with time indices the same as var1)
* indicating whether each variable of var1 at that point is valid.
* @param var2Valid time series (with time indices the same as var2)
* indicating whether each variable of var2 at that point is valid.
* @param condValid time series (with time indices the same as cond)
* indicating whether each variable of cond at that point is valid.
*/
public void setObservations(double[][] var1, double[][] var2,
double[][] cond,
boolean[][] var1Valid, boolean[][] var2Valid,
boolean[][] condValid) throws Exception;
/**
* Elect to add in the observations from several disjoint time series.
*
*/
public void startAddObservations();
/**
* <p>Adds a new set of observations to update the PDFs with - is
* intended to be called multiple times.
* Must be called after {@link #startAddObservations()}; call
* {@link #finaliseAddObservations()} once all observations have
* been supplied.</p>
*
* <p>Note that the arrays must not be over-written by the user
* until after finaliseAddObservations() has been called
* (they are not copied by this method necessarily, but the method
* may simply hold a pointer to them).</p>
*
* @param var1 multivariate observations for variable 1
* (first index is time, second is variable number)
* @param var2 multivariate observations for variable 2
* (first index is time, second is variable number)
* @param cond multivariate observations for the conditional
* (first index is time, second is variable number)
* @throws Exception
*/
public void addObservations(double[][] var1, double[][] var2,
double[][] cond) throws Exception;
/**
* <p>Adds a new set of observations to update the PDFs with - is
* intended to be called multiple times.
* Must be called after {@link #startAddObservations()}; call
* {@link #finaliseAddObservations()} once all observations have
* been supplied.</p>
*
* <p>Note that the arrays must not be over-written by the user
* until after finaliseAddObservations() has been called
* (they are not copied by this method necessarily, but the method
* may simply hold a pointer to them).</p>
*
* @param var1 multivariate observations for variable 1
* (first index is time, second is variable number)
* @param var2 multivariate observations for variable 2
* (first index is time, second is variable number)
* @param cond multivariate observations for the conditional
* (first index is time, second is variable number)
* @param startTime first time index to take observations on
* @param numTimeSteps number of time steps to use
* @throws Exception
*/
public void addObservations(double[][] var1, double[][] var2,
double[][] cond,
int startTime, int numTimeSteps) throws Exception;
/**
* Flag that the observations are complete, probability distribution functions can now be built.
* @throws Exception
*
*/
public void finaliseAddObservations() throws Exception;
/**
*
* @return the average value of the conditional mutual information measure,
* computed using all of the previously supplied observation sets.
* @throws Exception
*/
public double computeAverageLocalOfObservations() throws Exception;
/**
* <p>Computes the local values of the conditional mutual information,
* for each valid observation in the previously supplied observations
* (with PDFs computed using all of the previously supplied observation sets).</p>
*
* <p>If disjoint observations were supplied using several
* calls such as {@link ChannelCalculator#addObservations(double[], double[])}
* then the local values for each disjoint observation set will be appended here
* to create a single return array,
* though of course the time series for these disjoint observations were
* not appended in computing the required PDFs).</p>
*
* @return array of local values.
* @throws Exception
*/
public double[] computeLocalOfPreviousObservations() throws Exception;
/**
* <p>Compute the significance of obtaining the given average
* measure from the given observations.</p>
*
* <p>This is in the spirit of Chavez et. al., "Statistical assessment of nonlinear causality:
* application to epileptic EEG signals", Journal of Neuroscience Methods 124 (2003) 113-128.
* </p>
*
* <p>Basically, we shuffle the observations of the named variable
* against the other tuples.
* This keeps the marginal and joint PDFs of the unshuffled variables the same
* but destroys any correlation between the named variable and the others.
* </p>
*
* @param variableToReorder 1 for variable 1, 2 for variable 2
* @param numPermutationsToCheck number of new orderings of the source values to compare against
* @see "Chavez et. al., 'Statistical assessment of nonlinear causality:
* application to epileptic EEG signals', Journal of Neuroscience Methods 124 (2003) 113-128"
*/
public EmpiricalMeasurementDistribution computeSignificance(int variableToReorder,
int numPermutationsToCheck) throws Exception;
/**
* <p>As per {@link #computeSignificance(int, int)} but supplies
* the re-orderings of the observations of the named variable.</p>
*
* @param variableToReorder 1 for variable 1, 2 for variable 2
* @param newOrderings first index is permutation number, i.e. newOrderings[i]
* is an array of 1 permutation of 0..n-1, where there were n observations.
* If the length of each permutation in newOrderings
* is not equal to numObservations, an Exception is thrown.
* @return
* @throws Exception
*/
public EmpiricalMeasurementDistribution computeSignificance(
int variableToReorder, int[][] newOrderings) throws Exception;
/**
* Compute the conditional mutual information if the given variable were ordered as per the ordering
* specified in newOrdering
*
* @param variableToReorder 1 for variable 1, 2 for variable 2
* @param newOrdering permutation of the indices for the given variable
* @return
* @throws Exception
*/
public double computeAverageLocalOfObservations(int variableToReorder, int[] newOrdering) throws Exception;
/**
* Compute the local mutual information for the given states, using the
* PDFs from the previously supplied observations.
*
* @param states1
* @param states2
* @param condStates
* @return
* @throws Exception
*/
public double[] computeLocalUsingPreviousObservations(double states1[][], double states2[][], double[][] condStates)
throws Exception;
/**
* Set whether to print debug messages or not
*
* @param debug whether to print debug messages or not
*/
public void setDebug(boolean debug);
/**
* Get the last computed average of the measure
*
* @return the last computed average
*/
public double getLastAverage();
/**
* Get the number of observations that have been supplied for
* computation of the PDFs
*
* @return number of observations
* @throws Exception
*/
public int getNumObservations() throws Exception;
/**
* Get whether the user has added more than one observation set
* via the {@link #addObservations(double[][], double[][], double[][])}
* and {@link #addObservations(double[][], double[][], double[][], int, int)}
* methods.
*
* @return whether the user has added more than one observation set.
*/
public boolean getAddedMoreThanOneObservationSet();
}