%DKNNDD Distance K-Nearest neighbour data description method.% %       W = DKNNDD(D,FRACREJ,K,METHOD)% % Calculates the K-Nearest neighbour data description on distance% dataset D.  Two methods are defined to compute a distance to the% dataset using the k-nearest neighbours:%% METHOD     does:% 'kappa'      use distance to the k-th nearest neighbor% 'gamma'      average distance to the k-nn's%% When no K is defined, it will be optimized using knn_optk, when it% is smaller than 0, sqrt(n) will be used.% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org% Faculty EWI, Delft University of Technology% P.O. Box 5031, 2600 GA Delft, The Netherlandsfunction W = dknndd(D,fracrej,k,method)if nargin < 4, method = 'kappa'; endif nargin < 3, k = []; endif nargin < 2 | isempty(fracrej), fracrej = 0.05; endif nargin < 1 | isempty(D) % empty knndd	W = mapping(mfilename,{fracrej,k,method});	W = setname(W,'Dist. K-Nearest neighbour data description');	returnendif ~ismapping(fracrej)           %training	% some checking of datatypes and sizes:	%D = +target_class(D);  % make sure we have a OneClass dataset	[m,d] = size(D);%	if (m~=d)%		error('In this version I expect a square distance matrix');%	end	if (m<2)		warning([mfilename ': Dataset contains less than 2 objects']);	end	if (k>=d)		error(['More neighbors than training samples are requested! (max=',...                num2str(d-1),')']);	end   if isa(k,'char')      error('Argument k should define the number of neighbors');   end	% is k is not defined, find the optimal k optimizing the loglikelihood:	if isempty(k)		k = knn_optk(+D,d);	else  %tricky, when k<=0 we use the default sqrt(n) solution...		if (k<=0)			k = round(sqrt(m));		end	end	if (k<1)		warning([mfilename ': K must be positive (>0)']);	end	[sD,I] = sort(D,2);	% different treatment by different methods:	switch method	case 'kappa'		fit = sD(:,k+1);  	case 'gamma'		fit = mean(sD(:,(2:(k+1))),2);	otherwise		error([mfilename,': Unknown method']);	end	%now obtain the threshold:	thresh = dd_threshold(fit,1-fracrej);	%and save all useful data:	W.k = k;	W.method = method;	W.threshold = thresh;	W = mapping(mfilename,'trained',W,str2mat('target','outlier'),d,2);	W = setname(W,'K-Nearest neighbour data description');else                               %testing	W = getdata(fracrej);  % unpack	[m,d] = size(D);	%compute:	[sD,I] = sort(D,2);	% different treatment by different methods:	switch W.method	case 'kappa'		ind = sD(:,W.k);		%ind = sD(:,W.k+1);	case 'gamma'		ind = mean(sD(:,(1:(W.k))),2);	otherwise		error([mfilename,': Unknown method']);	end	% store the results in the final dataset:	out = [ind repmat(W.threshold,[m,1])];	% and use the distance as output:	W = setdat(D,-out,fracrej);	W = setfeatdom(W,{[-inf 0] [-inf 0]});endreturn