% KCENTRES Find k centres objects from distance matrix%%	[labels,J,dmin] = kcentres(D,k,n)%% If D is a square distance matrix between m objects then J is the set of centre% points, i.e. the subset of k objects that minimizes dmin, the maximum of the% distances over all objects to the nearest centre point. For k > 1 the results% depend on a random initialisation. The procedure is repeated n times and the% best result is returned. In labels a set of m object labels is stored in% which for each object the nearest of the centre points is returned. %%	J = kcentres(D)%	J = kcentres(D,1)%% In case k = 1 just the centre point is returned.%% Default: k = 1, n = 1.%% See also soset, kmeans% Copyright: 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 [labels,Jopt,dopt] = mykcentres(d,k,n);if nargin < 3, n = 1; endif nargin < 2, k = 1; end[m,m] = size(d);if k == 1	dmax = max(d);	[dm,labels] = min(dmax);	returnenddopt = inf;for try = 1:nM = randperm(m); M = M(1:k);J = zeros(1,k);while 1	[dm,I] = min(d(M,:));	for i = 1:k		JJ = find(I==i);		j = kcentres(d(JJ,JJ));    if size(J(i))~=size(JJ(j))      JJ      j      J(i)      JJ(j)    end    if ~isempty(j)      J(i) = JJ(j);    else      J(i) = floor(m*rand)+1;    end	end	if all(M == J)		[dmin,labs] = min(d(J,:));		dmin = max(dmin);		break;	end	M = J;endif dmin < dopt	dopt = dmin;	labels = labs';	Jopt = J;endend