%CLASSC Convert mapping to classifier%%  W = CLASSC(W)%  W = W*CLASSC%% INPUT%  W  Any mapping or dataset%	% OUTPUT%  W  Classifier mapping or normalized dataset: outputs/features sum to 1%% DESCRIPTION% The mapping W is converted into a classifier by normalizing the outputs:% the sum of the outputs for one sample equals 1. It is assumed that W % already generates densities or non-normalized posterior probability % estimates. This is true for neural networks or for two-class discriminants % calling CNORMC.% A one-dimensional map is converted into a two-class classifier, provided% that during the construction a class label was supplied. If not, the map% cannot be converted and an error is generated.% If D = A*W is the result of a mapping, it is normalized (the features of% each sample sum to 1). This is identical to D = NORMM(D).%% SEE ALSO% MAPPINGS, DATASETS% 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: classc.m,v 1.9 2004/02/20 12:50:29 bob Exp $function w = classc(w)	prtrace(mfilename);	if (nargin == 0)		% Untrained mapping.		w = mapping('classc','combiner');				elseif (ismapping(w))		% If mapping is stacked or parallel, recurse over the individual		% sub-mappings and call CLASSC for each of them.		if (isstacked(w)) | (isparallel(w))			v = cell(1,length(w.data));			for j = 1:length(w.data)				if ismapping(w.data{j}) % the parallel combiner may have nonmapping data					v{j} = feval(mfilename,w.data{j});				else					v{j} = w.data{j};				end			end			w = setdata(w,v);		else			conv = get(w,'out_conv');			if (conv < 1)				% Set the "normalization" bit in the mapping's output conversion flag				w = set(w,'out_conv',conv+2);						else				prwarning(3,'mapping is already a classifier');			end;		end	elseif (isdataset(w))		w = w*normm;		w = w*costm;	else		error('input should be mapping or dataset');	endreturn