[marf-cvs] marf/src/marf/Classification/Similarity CosineSimilarityMeasure.java, 1.6, 1.7

[Serguei A. M. <mo...@us...>]

Update of /cvsroot/marf/marf/src/marf/Classification/Similarity
In directory sfp-cvs-1.v30.ch3.sourceforge.com:/tmp/cvs-serv30456/marf/Classification/Similarity

Modified Files:
	CosineSimilarityMeasure.java 
Log Message:
Implement configurable options for mean, median, single, and
no-clustering based trained data sets. Clean up a bit.

Index: CosineSimilarityMeasure.java
===================================================================
RCS file: /cvsroot/marf/marf/src/marf/Classification/Similarity/CosineSimilarityMeasure.java,v
retrieving revision 1.6
retrieving revision 1.7
diff -C2 -d -r1.6 -r1.7
*** CosineSimilarityMeasure.java	9 Jul 2012 03:53:32 -0000	1.6
--- CosineSimilarityMeasure.java	8 Mar 2015 19:47:21 -0000	1.7
***************
*** 80,126 ****
  				ITrainingSample oTrainingSample = oTrainingSamples.get(i);
  
! 				double[] adMeanVector = oTrainingSample.getMeanVector();
  //				double[] adMeanVector = oTrainingSample.getMedianVector();
! 
! 				// Sanity check: stored mean vector must never be null
! 				if(adMeanVector == null)
! 				{
! 					throw new ClassificationException
! 					(
! 						"similarity() - Stored mean vector is null for subject (" + oTrainingSample.getSubjectID() +
! 						", preprocessing method: " + this.oTrainingSet.getPreprocessingMethod() +
! 						", feature extraction methods: "  + this.oTrainingSet.getFeatureExtractionMethod()
! 					);
! 				}
! 
! 				// Sanity check: vectors must be of the same length
! 				if(adMeanVector.length != adIncomingFeatures.length)
! 				{
! 					throw new ClassificationException
! 					(
! 						"similarity() - Mean vector length (" + adMeanVector.length +
! 						") is not same as of incoming feature vector (" + adIncomingFeatures.length + ")"
! 					);
! 				}
! 
! 				/*
! 				 * We have a mean vector of the samples for this iCurrentSubjectID
! 				 * Compare using whatever distance classifier it is...
! 				 */
! 				double dCurrentSimilarity = similarity(adMeanVector, adIncomingFeatures);
! 
! 				Debug.debug("Similarity for subject " + oTrainingSample.getSubjectID() + " = " + dCurrentSimilarity);
! 
! 				// XXX: What should we do in this (very rare and subtle) case?
! 				if(dCurrentSimilarity == dMaxSimilarity)
! 				{
! 					Debug.debug("This similarity had happened before!");
! 				}
! 
! 				if(dCurrentSimilarity > dMaxSimilarity)
  				{
! 					dMaxSimilarity = dCurrentSimilarity;
  				}
! 
  				// Collect for stats
  				// XXX: Move to StatsCollector
--- 80,128 ----
  				ITrainingSample oTrainingSample = oTrainingSamples.get(i);
  
! 				//double[] adDataVector = oTrainingSample.getMeanVector();
  //				double[] adMeanVector = oTrainingSample.getMedianVector();
! 				for(double[] adDataVector: oTrainingSample.getDataVectors())
  				{
! 					// Sanity check: stored mean vector must never be null
! 					if(adDataVector == null)
! 					{
! 						throw new ClassificationException
! 						(
! 							"similarity() - Stored mean vector is null for subject (" + oTrainingSample.getSubjectID() +
! 							", preprocessing method: " + this.oTrainingSet.getPreprocessingMethod() +
! 							", feature extraction methods: "  + this.oTrainingSet.getFeatureExtractionMethod()
! 						);
! 					}
! 	
! 					// Sanity check: vectors must be of the same length
! 					if(adDataVector.length != adIncomingFeatures.length)
! 					{
! 						throw new ClassificationException
! 						(
! 							"similarity() - Mean vector length (" + adDataVector.length +
! 							") is not same as of incoming feature vector (" + adIncomingFeatures.length + ")"
! 						);
! 					}
! 	
! 					/*
! 					 * We have a mean vector of the samples for this iCurrentSubjectID
! 					 * Compare using whatever distance classifier it is...
! 					 */
! 					double dCurrentSimilarity = similarity(adDataVector, adIncomingFeatures);
! 	
! 					Debug.debug("Similarity for subject " + oTrainingSample.getSubjectID() + " = " + dCurrentSimilarity);
! 	
! 					// XXX: What should we do in this (very rare and subtle) case?
! 					if(dCurrentSimilarity == dMaxSimilarity)
! 					{
! 						Debug.debug("This similarity had happened before!");
! 					}
! 	
! 					if(dCurrentSimilarity > dMaxSimilarity)
! 					{
! 						dMaxSimilarity = dCurrentSimilarity;
! 					}
  				}
! 				
  				// Collect for stats
  				// XXX: Move to StatsCollector
***************
*** 128,132 ****
  				(
  					oTrainingSample.getSubjectID(),
! 					dCurrentSimilarity
  				);
  			}
--- 130,134 ----
  				(
  					oTrainingSample.getSubjectID(),
! 					dMaxSimilarity
  				);
  			}