% learncl1.m: competitive learning example with clustering
% with annimation
% copyright (c) 1996-2000 by Yu Hen Hu
% created: 9/3/96
% last modified: 2/3/2000
%
% mfiles needed:  datagen1.m
%
clear all, close all
disp('Enter 1 to pause .5 second after each iteration (default), ');
pmode=input('Otherwise hit return key for continuous annimation: ');
if isempty(pmode), pmode=1; end

N=input('# of samples in each cluster (default = 30) = ');
if isempty(N), N=30; end

N2=N+N; B1=ceil(N/2); B2=N+B1;
eta=0.2;
means=[.7  -.8
       .7  -.8];
var= [0.2  0.2];
x=datagen1([N N],[means;var]); % x is 2N by 2
x=randomize(x);  % randomize the ordering of clusters
ncenter=input('# of clusters (neurons) to use (Default = 2): ');
if isempty(ncenter), ncenter=2; end
w=rand(ncenter,2)-0.5*ones(ncenter,2);  % both neuron initially at random place

figure(1),clf
plot(w(:,1),w(:,2),'*',x(:,1),x(:,2),'g.')
title('initial')
pause(1)
disp('competitive learning in action ....');

if isempty(pmode),  pmode=0; end
i=1; iter=1; converge=0;
while converge==0,
   dn=ones(ncenter,1)*x(i,:)-w;
   ddn=sum((dn.*dn)')'; % ddn: ncenter by 1
   [tmp,istar]=min(ddn);
   w(istar,:)=w(istar,:)+eta*(x(i,:)-w(istar,:));
   plot(w(:,1),w(:,2),'b*',x(:,1),x(:,2),'g.',x(i,1),x(i,2),'ro')
   drawnow
   if pmode==1,
      pause(.5)
   end
   i=rem(i+1,N2); iter=iter+1;
   if i==0,
      x=randomize(x);  % randomize the ordering of clusters
      i=1;             % start with a reordered input 
   end
   if rem(iter,500)==0,
      converge=input('Enter return key to continue, 1 to quit! ')
      if isempty(converge), converge=0; end
   end
 end
plot(w(:,1),w(:,2),'b*',x(:,1),x(:,2),'g.')
title(['at end of ' num2str(iter) ' iterations'])