%LABELD Find labels of classification dataset (perform crisp classification)% %   LABELS = LABELD(Z)%   LABELS = Z*LABELD%   LABELS = LABELD(A,W)%   LABELS = A*W*LABELD%   LABELS = LABELD(Z,THRESH)%   LABELS = Z*LABELD([],THRESH)%   LABELS = LABELD(A,W,THRESH)%   LABELS = A*W*LABELD([],THRESH)%% INPUT%		Z        Classification dataset, or%		A,W      Dataset and classifier mapping%     THRESH   Rejection threshold%% OUTPUT%		LABELS	List of labels%% DESCRIPTION % Returns the labels of the classification dataset Z (typically the result% of a mapping or classification A*W). For each object in Z (i.e. each row)% the feature label or class label (i.e. the column label) of the maximum % column value is returned.%% Effectively, this performs the classification. It can also be considered% as a conversion from soft labels to crisp labels.%% When the parameter THRESH is supplied, then all objects which% classifier output falls below this value are rejected. The returned% label is then NaN or a string with spaces (depending if the labels are% numeric or string). Because the output of the classifier is used, it% is recommended to convert the output to a posterior prob. output using% CLASSC.                                    (David Tax,  27-12-2004)% % SEE ALSO% MAPPINGS, DATASETS, TESTC, PLOTC% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org% Faculty EWI, Delft University of Technology% P.O. Box 5031, 2600 GA Delft, The Netherlandsfunction labels = labeld(a,w,thresh)	prtrace(mfilename);	% Add the possibility to reject objects for which the posterior is	% too low:	if (nargin < 3)		thresh = [];	end	if (nargin == 2) & isa(w,'double') % we did something like labeld(z,0.3)		thresh = w;		w = [];	end	if (nargin < 2)		w = [];	end	if (nargin == 0) | isempty(a)		% Untrained mapping.		labels = mapping(mfilename,'fixed',{thresh});	elseif isempty(w)		% In a classified dataset, the feature labels contain the output		% of the classifier.		[m,k] = size(a); featlist = getfeatlab(a);		Jrej = []; % as a start, we don't reject objects		if (k == 1)			% If there is one output, assume it's a 2-class discriminant: 			% decision boundary = 0. 			J = 2 - (double(a) >= 0); 			if ~isempty(thresh)				warning('I cannot apply the thresh to this 2-class dataset, please use classc.');			end		else			% Otherwise, pick the column containing the maximum output.			[dummy,J] = max(+a,[],2);			% Reject the objects which have posteriors lower than the			% threshold			if ~isempty(thresh)				Jrej = find(dummy<thresh);			end		end		labels = featlist(J,:);		% Take care for the rejected objects:		if ~isempty(Jrej)			if isa(featlist,'double')				labels(Jrej) = NaN;			elseif isa(featlist,'char')				labels(Jrej,:) = repmat(' ',size(featlist(1,:)) ); % Trick!:-)			else				error('I am confused by the featlist from A. Cannot make a reject label');			end		end	else		% Just construct classified dataset and call again.		labels = feval(mfilename,a*w,thresh);	endreturn