Making continuous MultiInfoCalculator classes implement the InfoMeasureCalculatorContinuous interface. For the interfaces, this means removing methods where duplicated. For implementing classes, this means adding the missing methods.

java/source/infodynamics/measures/continuous/MultiInfoCalculator.java

@@ -76,7 +76,8 @@ import infodynamics.utils.EmpiricalMeasurementDistribution;
  * @author Joseph Lizier (<a href="joseph.lizier at gmail.com">email</a>,
  * <a href="http://lizier.me/joseph/">www</a>)
  */
-public interface MultiInfoCalculator {
+public interface MultiInfoCalculator
+	extends InfoMeasureCalculatorContinuous {
 
 	/**
 	 * Property name for whether to normalise incoming values to mean 0,
@@ -120,6 +121,7 @@ public interface MultiInfoCalculator {
 	 * @param propertyValue value of the property
 	 * @throws Exception for invalid property values
 	 */
+	@Override
 	public void setProperty(String propertyName, String propertyValue) throws Exception;
 	
 	/**
@@ -189,14 +191,6 @@ public interface MultiInfoCalculator {
 	 */
 	public void finaliseAddObservations() throws Exception;
 
-	/**
-	 * Compute the multi-information from the previously-supplied samples.
-	 * 
-	 * @return the estimate of the multi-information
-	 * @throws Exception
-	 */
-	public double computeAverageLocalOfObservations() throws Exception;
-
 	/**
 	 * <p>Computes the local values of the multi-information,
 	 *  for each valid observation in the previously supplied observations
@@ -337,20 +331,4 @@ public interface MultiInfoCalculator {
 	 *   is not equal to the number N samples that were previously supplied.
 	 */
 	public EmpiricalMeasurementDistribution computeSignificance(int[][][] newOrderings) throws Exception;
-	
-	/**
-	 * Set or clear debug mode for extra debug printing to stdout
-	 * 
-	 * @param debug new setting for debug mode (on/off)
-	 */
-	public void setDebug(boolean debug);
-	
-	/**
-	 * Return the multi-information last calculated in a call to {@link #computeAverageLocalOfObservations()}
-	 * or {@link #computeLocalOfPreviousObservations()} after the previous
-	 * {@link #initialise(int)} call.
-	 * 
-	 * @return the last computed multi-information value
-	 */
-	public double getLastAverage();
 }

java/source/infodynamics/measures/continuous/MultiInfoCalculatorCommon.java

@@ -45,7 +45,7 @@ public abstract class MultiInfoCalculatorCommon implements MultiInfoCalculator {
 	/**
 	 * Number of joint variables to consider
 	 */
-	protected int dimensions = 0;
+	protected int dimensions = 1;
 	/**
 	 * Number of samples supplied
 	 */
@@ -81,6 +81,11 @@ public abstract class MultiInfoCalculatorCommon implements MultiInfoCalculator {
 	private double samplingFactor = 0.1;
 	private Random rand;
 
+	@Override
+	public void initialise() {
+		initialise(dimensions);
+	}
+	
 	@Override
 	public void initialise(int dimensions) {
 		this.dimensions = dimensions;
@@ -127,6 +132,17 @@ public abstract class MultiInfoCalculatorCommon implements MultiInfoCalculator {
 		}
 	}
 
+	@Override
+	public String getProperty(String propertyName) throws Exception {
+		if (propertyName.equalsIgnoreCase(PROP_NORMALISE)) {
+			return Boolean.toString(normalise);
+		} else if (propertyName.equalsIgnoreCase(SAMPLING_FACTOR_PROP_NAME)) {
+			return Double.toString(samplingFactor);
+		} else {
+			return null;
+		}
+	}
+
 	@Override
 	public void setObservations(double[][] observations) throws Exception {
 		startAddObservations();
@@ -258,6 +274,11 @@ public abstract class MultiInfoCalculatorCommon implements MultiInfoCalculator {
 		return miSurrogateCalculator.computeAverageLocalOfObservations();
 	}
 
+	@Override
+	public int getNumObservations() throws Exception {
+		return totalObservations;
+	}
+
 	@Override
 	public void setDebug(boolean debug) {
 		this.debug = debug;

java/source/infodynamics/measures/continuous/gaussian/MultiInfoCalculatorGaussian.java

@@ -2,7 +2,6 @@ package infodynamics.measures.continuous.gaussian;
 
 import infodynamics.measures.continuous.MultiInfoCalculatorCommon;
 import infodynamics.utils.MatrixUtils;
-import infodynamics.utils.MathsUtils;
 
 /**
  * <p>Computes the differential multi-information of a given multivariate

java/source/infodynamics/measures/continuous/kernel/MultiInfoCalculatorKernel.java

@@ -209,6 +209,19 @@ public class MultiInfoCalculatorKernel
 		}
 	}
 
+	@Override
+	public String getProperty(String propertyName) throws Exception {
+		if (propertyName.equalsIgnoreCase(KERNEL_WIDTH_PROP_NAME) ||
+				propertyName.equalsIgnoreCase(EPSILON_PROP_NAME)) {
+			return Double.toString(kernelWidth);
+		} else if (propertyName.equalsIgnoreCase(DYN_CORR_EXCL_TIME_NAME)) {
+			return Integer.toString(dynCorrExclTime);
+		} else {
+			// Try the superclass, including for PROP_NORMALISE
+			return super.getProperty(propertyName);
+		}
+	}
+
 	@Override
 	public void startAddObservations() {
 		if (dynCorrExcl) {

java/source/infodynamics/measures/continuous/kraskov/MultiInfoCalculatorKraskov.java

@@ -183,7 +183,7 @@ public abstract class MultiInfoCalculatorKraskov
 	 * <ul>
 	 *  <li>{@link #PROP_K} -- number of k nearest neighbours to use in joint kernel space
 	 *      in the KSG algorithm (default is 4).</li>
-	 *  <li>{@link #PROP_NORM_TYPE} -- normalization type to apply to 
+	 *  <li>{@link #PROP_NORM_TYPE} -- norm type to apply to 
 	 *      working out the norms between the points in each marginal space.
 	 * 	    Options are defined by {@link KdTree#setNormType(String)} -
 	 * 	    default is {@link EuclideanUtils#NORM_MAX_NORM}.</li>
@@ -198,7 +198,9 @@ public abstract class MultiInfoCalculatorKraskov
 	 *      so can be considered as a number of standard deviations of the data.
 	 *      (Recommended by Kraskov. MILCA uses 1e-8; but adds in
 	 *      a random amount of noise in [0,noiseLevel) ).
-	 *      Default 1e-8 to match the noise order in MILCA toolkit..</li>
+	 *      Default 1e-8 to match the noise order in MILCA toolkit.</li>
+	 *   <li>Any property accepted by the superclass
+	 *      {@link MultiInfoCalculatorCommon#setProperty(String, String)}</li>
 	 * </ul>
 	 * 
 	 * <p>Unknown property values are ignored.</p>
@@ -244,6 +246,24 @@ public abstract class MultiInfoCalculatorKraskov
 		}
 	}
 
+	@Override
+	public String getProperty(String propertyName) throws Exception {
+		if (propertyName.equalsIgnoreCase(PROP_K)) {
+			return Integer.toString(k);
+		} else if (propertyName.equalsIgnoreCase(PROP_NORM_TYPE)) {
+			return KdTree.convertNormTypeToString(normType);
+		} else if (propertyName.equalsIgnoreCase(PROP_DYN_CORR_EXCL_TIME)) {
+			return Integer.toString(dynCorrExclTime);
+		} else if (propertyName.equalsIgnoreCase(PROP_ADD_NOISE)) {
+			return Double.toString(noiseLevel);
+		} else if (propertyName.equalsIgnoreCase(PROP_NUM_THREADS)) {
+			return Integer.toString(numThreads);
+		} else {
+			// try the superclass, including for PROP_NORMALISE:
+			return super.getProperty(propertyName);
+		}
+	}
+
 	/**
 	 * Set observations from two separate time series: join the rows at each time step
 	 *  together to make a joint vector, then effectively call {@link #setObservations(double[][])}