%COSTM  Cost mapping, classification using costs%%   Y = COSTM(X,C,LABLIST)%   W = COSTM([],C,LABLIST)%% DESCRIPTION% Maps the classifier output X (assumed to be posterior probability% estimates) to the cost-outputs, defined by the cost-matrix C:%%   C(i,j) = cost of misclassifying an object from class i as class j.%% Default C is the cost matrix stored in the dataset X.% The order of the classes is defined by LABLIST. When no lablist is% given, the order as given by GETFEATLAB(X) is assumed.% In order to apply this mapping, it is assumed that the dataset X% represents posterior class probabilities (i.e. normalized by% classc).%% EXAMPLE% x = gendatb(100);% w1 = x*ldc;                  % standard classifier% C = [0 2; 1 0];              % new cost matrix% w2 = w1*classc*costm([],C);  % classifier using this cost-matrix%% SEE ALSO% MAPPINGS, CLASSC, TESTD, TESTCOST, SETCOST% Copyright: D.M.J. Tax, 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 Netherlandsfunction w = costm(a,C,lablist)	prtrace(mfilename);	% costm is implemented as a trained mapping to enable proper handling% of constructs like W = fisherc(train)*costm([],C,Clab); test*W;if nargin < 3, lablist = []; endif nargin < 2, C = []; end[s_in,s_out] = size(C);if nargin < 1 | isempty(a)	w = mapping(mfilename,'combiner',{C,lablist});	w = setname(w,'Mis-classification-cost');	returnendif ismapping(a)  % we do something like   w*costm()	if ~isempty(C) & ~isempty(lablist)		% match the labels with that of the previous mapping		[C,lablist] = matchcost(a.labels,C,lablist);	end                  % and now we have a trained mapping:	w = mapping(mfilename,'trained',{a,C,lablist},lablist,size(a,1),s_out);elseif isdataset(a) & ismapping(C)                  % we deal now with  d*costm()	w = feval(mfilename, a*C.data{1}, C.data{2}, C.data{3});	elseif isdataset(a) & ~ismapping(C)                  % the case that   d*costm([],C,lablist)	if ~isempty(C) % store in dataset for standard error checking handling						 % I doubt whether it is really the right thing to do								 % as it may disturb a different labellingsystem in case								 % we are dealing with a testset.		a = setcost(a,C,lablist);	end	C = a.cost; % retrieve from dataset	if isempty(C) % still empty, no costs		w = a;	else			% Get the data size		d = size(a,2);		if ( (size(C,1) ~= d) | (size(C,2) < d) )			error('Cost matrix has wrong size');		end			% Map the dataset using the costs:		w = a*C;			% Now we have a problem: PRTools expects that the estimated class			% has the highest output, and here we have calculated the cost			% (which should be low!)			% Thus we patch:		w = -w;		w = setfeatlab(w,a.lablist);	endelse	error('Input should be mapping or dataset');endreturn