%DNNDD Distance nearest neighbour data description method.% %       W = dnndd(D,fracrej)% % Calculates the Nearest neighbour data description on distance data.% Training only consists of the computation of the resemblance of all% training objects to the training data using Leave-one-out.% % See also datasets, mappings, dd_roc, nndd% 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 Netherlands  function W = dnndd(D,fracrej)if nargin < 2 | isempty(fracrej), fracrej = 0.05; endif nargin < 1 | isempty(D) % empty nndd	W = mapping(mfilename,{fracrej});	W = setname(W,'Distance Nearest neighbour dd');	returnendif ~ismapping(fracrej)           %training%	D = +target_class(D);      % make sure we have a OneClass dataset	[m,k] = size(D);	% Apply leave-one-out on the training set:	fit = zeros(m,1);	for i=1:m		tmpD = D;		[minD minI] = min(tmpD(i,:));  % dist. from z to 1NN in A		tmpD(i,minI) = inf;		intdist = min(tmpD(:,minI));   % dist. from 1NN to NN(1NN)		fit(i) = minD./intdist;	end	% Now we can obtain the threshold:	thresh = dd_threshold(fit,1-fracrej);	% and save all useful data:	W.threshold = thresh;	W.fit = fit;	W.D = min(D,[],2);	W.scale = mean(fit);	W = mapping(mfilename,'trained',W,str2mat('target','outlier'),k,2);	W = setname(W,'Nearest neighbour data description');else                               %testing	W = getdata(fracrej);  % unpack	m = size(D,1);	%compute:	[mindist I] = min(D,[],2); % find the closest dist.	out = [mindist./(W.D(I)) repmat(W.threshold,m,1)];	% Store the distance as output:	W = setdat(D,-out,fracrej);	W = setfeatdom(W,{[-inf 0] [-inf 0]});endreturn