%GENDATK K-Nearest neighbor data generation% %   B = GENDATK(A,N,K,S)%% INPUT%   A  Dataset%   N  Number of points (optional; default: 50)%   K  Number of nearest neighbors (optional; default: 1)%   S  Standard deviation (optional; default: 1)%% OUTPUT%   B  Generated dataset%% DESCRIPTION % Generation of N points using the K-nearest neighbors of objects in the % dataset A. First, N points of A are chosen in a random order. Next, to each % of these points and for each direction (feature), a Gaussian-distributed % offset is added with the zero mean and the standard deviation: S * the mean % signed difference between the point of A under consideration and its K % nearest neighbors in A. %% The result of this procedure is that the generated  points follow the local% density properties of the point from which they originate.%% If A is a multi-class dataset the above procedure is followed class by% class, neglecting objects of other classes and possibly unlabeled objects.% % If N is a vector of sizes, exactly N(I) objects are generated% for class I. Default N is 100 objects per class.%% SEE ALSO% DATASETS, GENDATP, GENDATT% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl% Faculty of Applied Physics, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlands% $Id: gendatk.m,v 1.7 2003/10/29 14:11:12 davidt Exp $function B = gendatk(A,N,k,stdev)	prtrace(mfilename);	if (nargin < 4) 				prwarning(3,'Standard deviation of the added Gaussian noise is not specified, assuming 1.');		stdev = 1; 	end	if (nargin < 3) 		prwarning(3,'Number of nearest neighbors to be used is not specified, assuming 1.');		k = 1; 	end	if (nargin < 2)		prwarning(3,'Number of samples to generate is not specified, assuming 50.');		N = [];   % This happens some lines below.	end	if (nargin < 1)		error('No dataset found.');	end	A = dataset(A);	A = setlablist(A); % remove empty classes first	[m,n,c] = getsize(A);	prior = getprior(A);	if isempty(N), 		N = repmat(50,1,c); 				% 50 samples are generated.  			end	N = genclass(N,prior);				% Generate class frequencies according to the priors.				lablist = getlablist(A);	B = [];	labels = [];	% Loop over classes.	for j=1:c		a = getdata(A,j); 					% The j-th class.		[D,I] = sort(distm(a)); 		I = I(2:k+1,:); 						% Indices of the K nearest neighbors.		alf = randn(k,N(j))*stdev;	% Normally distributed 'noise'.		nu = ceil(N(j)/size(a,1));	% It is possible that NU > 1 if many objects have to be generated. 		J = randperm(size(a,1));				J = repmat(J,nu,1)';						J = J(1:N(j));							% Combine the NU repetitions of J into one column vector.		b = zeros(N(j),n);		% Loop over features.		for f = 1:n%      Take all objects given by J, consider feature F.%      Their K nearest neighbors are given by I(:,J)%      We reshape them as a N(j) by K matrix (N(j) is the length of J)%      Compute all differences between them and the original objects%      Multiply these differences by the std dev stored in alf%      Transpose and sum over the K neighbors, normalize by K%      Transpose again and add to the original objects 			 b(:,f) = a(J,f) + sum(( ( a(J,f)*ones(1,k) - ...								reshape(+a(I(:,J),f),k,N(j))' ) .* alf' )' /k, 1)';		end		B = [B;b];		labels = [labels; repmat(lablist(j,:),N(j),1)];	end	B = dataset(B,labels,'prior',prior);	%B = set(B,'featlab',getfeatlab(A),'name',getname(A),'featsize',getfeatsize(A));	%DXD. Added this exception, because else it's going to complain	%     that the name is not a string.	B = set(B,'featlab',getfeatlab(A),'featsize',getfeatsize(A));	if ~isempty(getname(A))		B = setname(B,getname(A));	endreturn;