% clusters.m
% function cb = clusters(input,ref,maxIt)
% clusters(randn(2,400)/sqrt(12),randn(2,1)/sqrt(12));
% 17.01.2003, Jens Ahrensfeld

function [indices, output] = eudist(input,ref)

[nDim, nData] = size(input);
[nDimr, nClasses] = size(ref);

if (nDim ~= nDimr),
    error('Dimensions must match!');
end;

cnt = zeros(nClasses,1);
for j=1:nClasses,
   indices{j} = [];
end;
for i=1:nData,
   for j=1:nClasses,
     d(j) = sqrt(sum((input(:,i)-ref(:,j)).^2));			% Calc distances between references and input
   end;  
   [value nearestClass] = min(d);
   cnt(nearestClass) = cnt(nearestClass) + 1;
   indices{nearestClass} = [ indices{nearestClass} i ];
end;   

% Separate data
for j=1:nClasses,
	output{j} = input(:,indices{j});
end;