- refactored

common/clusters/clusters.m

@@ -0,0 +1,29 @@
+% 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;