function cluster_N_plot(data,means,stds,method)
% CLUSTER_N_PLOT - 
% 
% Inputs:
%   method: 1=> DON'T update starndard deviations, 2=> DO update standard deviations
% 
% Written by:
% -- 
% John L. Weatherwax                2005-08-14
% 
% email: wax@alum.mit.edu
% 
% Please send comments and especially bug reports to the
% above email address.
%
% Iterative adjustment of clusters (must have a preconsieved
% notion of what the cluster centers are):
% 
%-----

Niters = 10; 

figure; plot( data(:,1), data(:,2), 'o' ); axis( [ 0 7 -1 5 ] ); hold on; 
grid on; title( 'raw data' ); 

R = size(means,1); 
meansOld=means; 

for ni=1:Niters,
  plot( means(:,1), means(:,2), 'rx', 'MarkerSize', 10*ni ); 
  pause(1); 

  % Find the closest cluster for each datum: 
  %
  clust=zeros(size(data,1),1);
  for di=1:size(data,1)
    yt=data(di,:);
    clust(di) = findNearestCluster( yt, means, stds );
  end
  
  % Calculate new cluster centers/stardard deviations:
  % 
  for ri=1:R,
    inds = find(clust==ri); 
    means(ri,:) = mean(data(inds,:)); 
  end
  switch method
   case 1
    stds = ones(size(means)); 
   case 2
    for ri=1:R,
      inds = find(clust==ri); 
      stds(ri,:) = std(data(inds,:)); 
    end
   otherwise
    error( 'Unknown value of METHOD' ); 
  end

  if( norm(meansOld-means) < 10^(-10) )
    % We are done...exit
    break; 
  else
    meansOld=means; 
  end
end
plot( means(:,1), means(:,2), 'kx' );