Minor corrections to Javadocs for the continuous package

java/source/infodynamics/measures/continuous/ChannelCalculator.java

@@ -19,8 +19,9 @@
 package infodynamics.measures.continuous;
 
 /**
- * A basic interface for calculators computing measures on a univariate <i>channel</i> from a
- * source to a destination time-series (i.e. mutual information and transfer entropy).
+ * A basic interface for calculators computing measures on a univariate <i>channel</i>
+ * for continuous (ie double[]) data from a
+ * source to a destination time-series (ie mutual information and transfer entropy).
  * In the following, we refer to the abstract measure computed by this calculator
  * as the <i>"channel measure"</i>.
  * 
@@ -38,7 +39,7 @@ package infodynamics.measures.continuous;
  * 
  * @author Joseph Lizier (<a href="joseph.lizier at gmail.com">email</a>,
  * <a href="http://lizier.me/joseph/">www</a>)
- * @see ChannelCalculator
+ * @see ChannelCalculatorCommon
  */
 public interface ChannelCalculator extends ChannelCalculatorCommon {
 

java/source/infodynamics/measures/continuous/ChannelCalculatorCommon.java

@@ -129,7 +129,6 @@ public abstract interface ChannelCalculatorCommon {
 	 * Compute the channel measure from the previously-supplied samples.
 	 * 
 	 * @return the estimate of the channel measure
-	 * @throws Exception
 	 */
 	public double computeAverageLocalOfObservations() throws Exception;
 
@@ -149,7 +148,6 @@ public abstract interface ChannelCalculatorCommon {
 	 *  to create a single "time-series" return array.</p>
 	 *  
 	 * @return the "time-series" of local channel measure values.
-	 * @throws Exception
 	 */
 	public double[] computeLocalOfPreviousObservations() throws Exception;
 
@@ -177,7 +175,6 @@ public abstract interface ChannelCalculatorCommon {
 	 * @return the distribution of channel measure scores under this null hypothesis.
 	 * @see "J.T. Lizier, 'JIDT: An information-theoretic
 	 *    toolkit for studying the dynamics of complex systems', 2014."
-	 * @throws Exception
 	 */
 	public EmpiricalMeasurementDistribution computeSignificance(int numPermutationsToCheck) throws Exception;
 	
@@ -244,8 +241,6 @@ public abstract interface ChannelCalculatorCommon {
 	 * </p>
 	 * 
 	 * @return the number of samples to be used for the PDFs
-	 * @throws Exception
 	 */
 	public int getNumObservations() throws Exception;
-
 }

java/source/infodynamics/measures/continuous/TransferEntropyCalculator.java

@@ -54,14 +54,14 @@ package infodynamics.measures.continuous;
  * 							{@link #addObservations(double[], double[], int, int)}, then</li>
  * 						<li>{@link #finaliseAddObservations()};</li>
  * 					</ol></li>
- * 			</ul>
+ * 			</ul></li>
  * 		<li>Compute the required quantities, being one or more of:
  * 			<ul>
  * 				<li>the average TE: {@link #computeAverageLocalOfObservations()};</li>
  * 				<li>the local TE values for these samples: {@link #computeLocalOfPreviousObservations()}</li>
  * 				<li>local TE values for a specific set of samples:
  * 				{@link #computeLocalUsingPreviousObservations(double[], double[])} </li>
- * 				<li>the distribution of MI values under the null hypothesis
+ * 				<li>the distribution of TE values under the null hypothesis
  * 					of no relationship between source and
  * 					destination values: {@link #computeSignificance(int)} or
  * 					{@link #computeSignificance(int[][])}.</li>

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

@@ -263,12 +263,12 @@ public abstract class MutualInfoCalculatorMultiVariateKraskov
 	 *  (with PDFs computed using all of the previously supplied observation sets).</p>
 	 *  
 	 * <p>If the samples were supplied via a single call such as
-	 * {@link #setObservations(double[])},
+	 * {@link #setObservations(double[][], double[][])},
 	 * then the return value is a single time-series of local
 	 * channel measure values corresponding to these samples.</p>
 	 * 
 	 * <p>Otherwise where disjoint time-series observations were supplied using several 
-	 *  calls such as {@link addObservations(double[])}
+	 *  calls such as {@link #addObservations(double[][], double[][])}
 	 *  then the local values for each disjoint observation set will be appended here
 	 *  to create a single "time-series" return array.</p>
 	 *