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CLSnn.m in standardmodelrelease

			
function [Model,looerrors] = CLSnn(X,y,sPARAMS);
%function [Model,looerrors] = CLSnn(X,y,sPARAMS);
%
%Builds a NN classifier
%X contains the data-points as COLUMNS, i.e., X is nfeatures \times nexamples
%y is a column vector of all the labels. y is nexamples \times 1
%sPARAMS is a structure of parameters:
%sPARAMS.k is the k for knn
%sPARAMS.deg determines the p-norm to be used as distance
%Model contains the parameters of the nn classifier 

if nargin<3
  sPARAMS.k = 1;
end

if ~isfield(sPARAMS,'deg')
  sPARAMS.deg = 2;
end

Model.k = sPARAMS.k;
Model.deg = sPARAMS.deg;
Model.trainX = X;
Model.trainy = y;

if isfield(sPARAMS,'numindeces')
  Model.numindeces = sPARAMS.numindeces;
else
  Model.numindeces = inf;
end

if nargout>1
  deg = Model.de			

			...
			...
			... to be continued.

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