perClass Documentation
version 5.1 (31-May-2017)
 SDKNN k-nearest neighbor classifier

    P=SDKNN(DATA,options)
    P=SDKNN(DATA,K)

 INPUT
   DATA      training dataset
   K         neighborhood size (def: k=1)

 OPTIONS
   k         number of neighbors (def: 1)
   proto     number of prototypes to select per class (def: [] = use all samples)
   protosel  prototype selection method (def: 'random')
              'kcentres','random'
   method    method to compute k-NN with k>1 (def: 'distance')
              'distance' - outputs per-class distance to k-th neighbor (one/multi class)
              'classfrac' - outputs class fraction between k neighbors (only multi class)
 OUTPUT
   P         pipeline object

 DESCRIPTION
 SDKNN trains a k-NN classifier using Euclidean distance. By default, k=1.
 SDKNN may be trained on one class - by default it will accept all
 training examples.  By default all provided examples are used as
 prototypes. Prototype selection may be performed by setting number of
 desired prototypes using the 'proto' option.

 READ MORE
http://perclass.com/doc/guide/classifiers.html#sdknn

 SEE ALSO
 SDKMEANS, SDKCENTRES, SDPARZEN

sdknn is referenced in examples: