function e = dd_fp(w,z,err)%DD_FP%%    E = DD_FP(W,Z,ERR)%% Change the threshold of a (trained) classifier W, such that the error% on the target class (the fraction false negative) is set to ERR. The% error on the outlier class, the false positive fraction, is then% returned. The target and outlier data is extracted from dataset Z.% 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% first find out where the output for the target objects are stored:tcolumn = strmatch('target ',getlabels(w));if isempty(tcolumn)	error('Cannot find target objects in dataset.');end% compute the classifier output:wz = +(z*w);% sometimes it happens...wz = real(wz);if tcolumn~=1	% then we are probably using 'normal' prtools classifiers, and in	% that case, the outputs should be normalized	if abs(sum(sum(wz)) - size(wz,1)) > 1e-9		error('Are the classifier outputs normalized?');  endend%find target and outliers[It,Io] = find_target(z);if isempty(It)|isempty(Io)	error('Both target and outlier objects should be available!');end% set error on target set:out = wz(It,:);thr = dd_threshold(out(:,tcolumn),err);% and compute error on outlier set:out = wz(Io,:);e = sum(out(:,tcolumn)>=thr)/length(Io);return