%MATCHLAB Compare two labellings and rotate the labels for an optimal match%%  LABELS = MATCHLAB(LAB1,LAB2)%% INPUT%   LAB1,LAB2  Label lists of the same objects   %% OUTPUT%   LABELS     A rotated version of LAB2, optimally matched with LAB1%% DESCRIPTION% LAB1 and LAB2 are label lists for the same objects. The returned LABELS % constitute a rotated version of LAB2, such that the difference with % LAB1 is minimized. This can be used for the alignment of cluster results.%% EXAMPLES% See PREX_MATCHLAB.%% SEE ALSO% DATASETS, HCLUST, MODESEEK, KMEANS, EMCLUST% $Id: matchlab.m,v 1.5 2003/10/17 10:37:56 bob Exp $function [lab,L] = matchlab(lab1,lab2)	prtrace(mfilename);  [ok1,lab1] = iscolumn(lab1);  [ok2,lab2] = iscolumn(lab2);	if (~ok1) | (~ok2)		prwarning(5,'Label lists should be column lists. LAB1 and LAB2 are made so.');	end	% Compute the confusion matrix and renumber the labels.	C = confmat(lab1,lab2); 				[nl1,nl2,lablist] = renumlab(lab1,lab2);	% N1 and N2 describe the number of distinct labels (classes) in LAB1 and LAB2.	[n1,n2] = size(C);				L = zeros(n2,1);			% Label list for the rotated LAB2	K = [1:n1]; 					% Class labels of LAB1	% Find the best match based on the confusion numbers.		for i=1:n1		[NN,r] = min(sum(C(:,K),1) - max(C(:,K),[],1)); 			j      = K(r);							% J is the actual class of LAB2 		[nn,s] = max(C(:,j)); 			% to be assigned as S.		L(j)   = s;		C(:,j) = zeros(n1,1); 			% J is already processed, remove it 		K(r)   = [];								% from further considerations.	end	lab = lablist(L(nl2),:);return;