clusmse.m

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% ---------------------------------------------------------------- 
% FUNCTION   clusmse.m    CLUSterin algorithm, MSE metric.
% ---------------------------------------------------------------- 
% 		builds a block training set. 
% Usage: 
%   cluster_centers = clusmse(orig_cluster_centers, training set, precision);
% 
% orig_cluster: matrix, each ROW is a cluster center.
% training set: matrix, each ROW is a sample.
% Precision = stopping criterion: If the difference between two following
%		mse's is less than epsilon, then stop.
%
% __________________________
%  Author: Vittorio Castelli
%  Copyright IBM T. J. Watson Research Center
%      January 30, 1995;	Last modified: January 30, 1995

function clCen =  clusmse(origCen, trSet,prec);

learning_rate = .07;

n_dist = 32;
maxcount = 128;
count = 0;
contatore = 0;
dis = 1;


[m,n] = size(trSet);
[n_cent,n1] = size(origCen);
if n ~= n1,
   errormes(4);
   return
end

aaa = ones(1,n);
aa = ones(1,m);
clCen = origCen;

figure(1);
clg;
figure(2);
clg;
figure(3);
clg;
asdfg = max(size(trSet));
plot3(trSet(1:20:asdfg,1),trSet(1:20:asdfg,2),trSet(1:20:asdfg,3),'.');
grid;
hold;
oldplCen = clCen;
plot3(clCen(:,1),clCen(:,2),clCen(:,3),'mx');

while dis > prec,
  oldCen=clCen;
  contatore = contatore +1;

  for ii = 1:n_cent,
      distan(ii,:) = sum((trSet'-((clCen(ii,:))'*aa)).^2);
  end;

  

  [distan,ord] = sort(distan);
  ord = ord';

  ord = ord(:,1);
  dis=0;
  for i=1:n_cent,
        nelcloud(i) = sum(ord==i);
        if nelcloud(i) ==0,
             fprintf(' %g',i)
        else
           aver = sum((((ord==i)*aaa).*trSet))/nelcloud(i);
           clCen(i,:) = clCen(i,:).*(1-learning_rate)+aver.*learning_rate;
           disper(i) =  sum((ord == i).* ...
                   (sum(((clCen(i,:)'*aa)-trSet').^2))')/nelcloud(i);
        end
        d_i = sum((clCen(i,:)-oldCen(i,:)).^2);
        dis=max(dis, d_i);
        disi(i) = d_i;         
   end
 
   figure(1);
   subplot(211)
   plot(sqrt(disper),'+');
   title('Dispersion');
   subplot(212)
   plot(disi)
   titlestr = sprintf('Changes, iter. %g',contatore);
   title(titlestr);

   count = count+1;
   distance(count) = dis;


   if rem(count,n_dist)==0,
      figure(2);
      plot(distance,'.');
      grid
      figure(3)
      plot3(oldplCen(:,1),oldplCen(:,2),oldplCen(:,3),'cx');
      plot3(clCen(:,1),clCen(:,2),clCen(:,3),'mx');
      oldplCen = clCen;
   end
   if count==maxcount,
       distance(1:count-n_dist+1) = distance(n_dist:count);
       count = count-n_dist+1,
   end
end
fprintf('\n');