% 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 cb = clusters(input,ref,maxIt)
eps   = 0.4;
epse  = 0.001;

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

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

d = zeros(nDatar,1);

for i=1:maxIt,
    n = round(rand(1,1)*(nData-1))+1;						% Randomly pick input vector
    for k=1:nDatar,
        d(k) = sqrt(sum((input(:,n)-ref(:,k)).^2));			% Calc distances between references and input
    end;
    [min, winidx] = min(d);			    					% Calc best reference vector - the winner
    winner = ref(:,winidx);
    epsi = eps*(1-i/maxIt)+epse;
    ref(:,winidx) = winner + epsi*(input(:,n)-winner);      % Move winner towards input
end;
cb = ref;