%REJECT Compute the error-reject trade-off curve% %   E = REJECT(D);%   E = REJECT(A,W);%	% INPUT%   D   Classification result, D = A*W%   A   Dataset%   W   Cell array of trained classifiers%% OUTPUT%   E   Structure storing the error curve and information needed for plotting%% DESCRIPTION% E = REJECT(D) computes the error-reject curve of the classification % result D = A*W, in which A is a dataset and W is a classifier. E is % a structure storing the error curve in E.ERROR. Use PLOTR(E) for % plotting the result.%%	E = REJECT(A,W) computes a set of error-reject curves for all trained % classifiers stored in the cell array W.%% EXAMPLES% A - training set, B - test set:% D = B*NMC(A); E = REJECT(D); PLOTR(E);   % Plots a single curve% E = REJECT(B,A*{NMC,UDC,QDC}); PLOTR(E); % Plots 3 curves% % REFERENCES% 1. R.O. Duda, P.E. Hart, and D.G. Stork, Pattern classification, 2nd edition, % John Wiley and Sons, New York, 2001.% 2. A. Webb, Statistical Pattern Recognition, John Wiley & Sons, New York, 2002.%% SEE ALSO% DATASETS, MAPPINGS, PLOTR, ROC, TESTC% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl% Faculty of Applied Sciences, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlands% $Id: reject.m,v 1.5 2003/12/19 09:14:38 bob Exp $function e = reject(A,W)	prtrace(mfilename);	% No data, return an untrained mapping.	if (nargin == 0) | (isempty(A))		e = mapping('reject','fixed');		return;	end	compute = 0;					% COMPUTE is 1 if the classier W should be applied to A.	name = [];	if (nargin == 2)		if iscell(W)			classfno = length(W); 	% Number of classifiers, hence error curves.			compute = 1;		elseif ismapping(W) % W is one classifier only.			if ~istrained(W)				error('Classifier should be trained.')			end			D = A*W*classc;   % Normalize the outputs of W and apply it to the data A.			classfno = 1;						name = getname(W);		else			error('Second parameter should be a classifier or a cell array of classifiers.')		end	else									% Only one input argument. 		D = A;		classfno = 1; 				end	% Fill the structure E with information needed for plotting.	m = size(A,1);	e.error = zeros(classfno,m+1);  % m+1 for storing error of 0 size dataset	e.std   = zeros(classfno,m+1);	e.xvalues = [0:m]/m;;	e.n = 1;	datname = getname(A);	if ~isempty(datname)		e.title = ['Reject curve for the ' datname];	end	e.xlabel= 'Reject';	e.ylabel= 'Error';	e.plot  = 'plot';	e.names = name;	for j=1:classfno		if (compute)			w = W{j};			if ~istrained(w)				error('Classifier should be trained.')			end			D = A*w*classc; 		% Normalize the outputs of W and apply it to A.			e.names = char(e.names,getname(w));		end		% Compare the classification result with the actual labels.		% N is a 0/1 vector pointing to all distinct/equal labels.		[err,n] = nlabcmp(labeld(D),getlab(D)); 		% A trick: if D consists of one column, as before returned by 		% FISHERC in case of a 2-class problem. 		% May be they don't exist anymore in PRTools, but once they did		% and may be they pop up again.		if (size(D,2) == 1)			D = [D 1-D];		end		% Sort the objects, starting from the closest to the decision 		% boundary to the furthest away.		[y,J] = sort(max(+D,[],2));		% 1/0 in N corresponds now to objects correctly/wrongly classified.		n = 1-n(J)';				 		e.error(j,:) = [err err-cumsum(n)]/m;	end	if (classfno > 1)		e.names(1,:) = [];	endreturn;