[marf-cvs] marf/src/marf/Classification/NeuralNetwork NeuralNetwork.java, 1.58, 1.59

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

Update of /cvsroot/marf/marf/src/marf/Classification/NeuralNetwork
In directory sc8-pr-cvs16.sourceforge.net:/tmp/cvs-serv18589/src/marf/Classification/NeuralNetwork

Modified Files:
	NeuralNetwork.java 
Log Message:
Introduce the ITrainingSample interface to represent the common
training sample beans. Have the training sample hierarchy to
implement this initial version of the interface and integrate it
into the Classification framework. Provide first implementation
of the FeatureSet bean that actually works and allows not only
mean vector-based classification, but also median, which maxizes
the amount of better classifications. The refactors out a complete
dependency on the Cluster class among the Classification modules,
and allow any of the TrainingSample, Cluster, or TrainingSet
beans to be used. As a refactor the beans' implementation to
allow the TrainingSet to be the extension of the Cluster and
reduce amount of the duplicated code. In the implementation of
the FeatureSet employ dynamic programming to cache intermediate
results of computed mean and median vetors and recompute them
only if the set is modified to allow efficient run-time
performance by computing mean or median only once.

Enhance error reporting in places and clean up some comments.


Index: NeuralNetwork.java
===================================================================
RCS file: /cvsroot/marf/marf/src/marf/Classification/NeuralNetwork/NeuralNetwork.java,v
retrieving revision 1.58
retrieving revision 1.59
diff -C2 -d -r1.58 -r1.59
*** NeuralNetwork.java	31 Jul 2006 14:33:47 -0000	1.58
--- NeuralNetwork.java	5 Aug 2007 22:01:32 -0000	1.59
***************
*** 19,23 ****
  import marf.Classification.ClassificationException;
  import marf.FeatureExtraction.IFeatureExtraction;
! import marf.Storage.Cluster;
  import marf.Storage.Result;
  import marf.Storage.StorageException;
--- 19,23 ----
  import marf.Classification.ClassificationException;
  import marf.FeatureExtraction.IFeatureExtraction;
! import marf.Storage.ITrainingSample;
  import marf.Storage.Result;
  import marf.Storage.StorageException;
***************
*** 277,282 ****
  				for(int i = 0; i < oTrainingSamples.size(); i++)
  				{
! 					Cluster oCluster = (Cluster)oTrainingSamples.get(i);
! 					train(oCluster.getMeanVector(), oCluster.getSubjectID(), dTrainConst);
  
  					// Commit modified weight
--- 277,283 ----
  				for(int i = 0; i < oTrainingSamples.size(); i++)
  				{
! //					Cluster oCluster = (Cluster)oTrainingSamples.get(i);
! 					ITrainingSample oTrainingSample = (ITrainingSample)oTrainingSamples.get(i);
! 					train(oTrainingSample.getMeanVector(), oTrainingSample.getSubjectID(), dTrainConst);
  
  					// Commit modified weight
***************
*** 291,297 ****
  				for(iCount = 0; iCount < oTrainingSamples.size(); iCount++)
  				{
! 					Cluster oCluster = (Cluster)oTrainingSamples.get(iCount);
  
! 					setInputs(oCluster.getMeanVector());
  					runNNet();
  
--- 292,300 ----
  				for(iCount = 0; iCount < oTrainingSamples.size(); iCount++)
  				{
! //					Cluster oCluster = (Cluster)oTrainingSamples.get(iCount);
! 					ITrainingSample oTrainingSample = (ITrainingSample)oTrainingSamples.get(iCount);
  
! 					// XXX: can be median and feature vectors
! 					setInputs(oTrainingSample.getMeanVector());
  					runNNet();
  
***************
*** 299,305 ****
  
  //					dError += Math.abs(oCluster.getSubjectID() - iID);
! 					dError += dMinErr * Math.abs(oCluster.getSubjectID() - iID);
  
! 					Debug.debug("Expected: " + oCluster.getSubjectID() + ", Got: " + iID + ", Error: " + dError);
  				}
  
--- 302,308 ----
  
  //					dError += Math.abs(oCluster.getSubjectID() - iID);
! 					dError += dMinErr * Math.abs(oTrainingSample.getSubjectID() - iID);
  
! 					Debug.debug("Expected: " + oTrainingSample.getSubjectID() + ", Got: " + iID + ", Error: " + dError);
  				}