TEST_SC: apply statistical and SVM classifier to test data 

  R = test_sc(CC,D,TYPE [,target_Classlabel]) 
       R.output     	output: "signed" distance for each class. 
		This represents the distances between sample D and the separating hyperplane
		The "signed distance" is possitive if it matches the target class, and 
		and negative if it lays on the opposite side of the separating hyperplane. 
       R.classlabel 	class for output data
  The target class is optional. If it is provided, the following values are returned. 
       R.kappa 	Cohen's kappa coefficient
       R.ACC   	Classification accuracy 
       R.H     	Confusion matrix 

 The classifier CC is typically obtained by TRAIN_SC. If a statistical 
 classifier is used, TYPE can be used to modify the classifier. 
    TYPE = 'MDA'    mahalanobis distance based classifier
    TYPE = 'MD2'    mahalanobis distance based classifier
    TYPE = 'MD3'    mahalanobis distance based classifier
    TYPE = 'GRB'    Gaussian radial basis function 
    TYPE = 'QDA'    quadratic discriminant analysis
    TYPE = 'LD2'    linear discriminant analysis
    TYPE = 'LD3', 'LDA', 'FDA, 'FLDA'   (Fisher's) linear discriminant analysis
    TYPE = 'LD4'    linear discriminant analysis
    TYPE = 'GDBC'   general distance based classifier
 
 see also: TRAIN_SC

 References: 
 [1] R. Duda, P. Hart, and D. Stork, Pattern Classification, second ed. 
       John Wiley & Sons, 2001.

Package: nan