avq.m

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% ==========================================================
% 
%           Neural Networks A Classroom Approach
%                     Satish Kumar
%             Copyright Tata McGraw Hill, 2004
%
%       MATLAB code that implements the AVQ algorithm
%             Reference: Table 12.4;Page 544
%
% ==========================================================

% Program for AVQ Clustering: m clusters in n dimensions

m = 2; % Generate two clusters
fid=fopen('./avqtest.dat','r');	% Open data file
pat = fscanf(fid,'%f %f',[3 inf]);
fclose(fid);
[n,Q]=size(pat);  % Dimension n, and number of data Q

w = [3 -2			% Initial weight matrix w
     0  3 
     0  5]; 

figure 				% Plot the clusters
plot3(pat(1,:), pat(2,:), pat(3,:), 'b.');
grid on;
hold on;
axis([-2 4 -2 5 -2 5]);

% For weight storage
xindex = 1;
yindex = 1;

for i=1:Q	 			% for each data point
   minindex = -1; 	% reset the minimum distance index
   mindist = 1000;	% set the mindist variable to a large number

   for j=1:m 			% check the distance to each cluster codebook
      dist = 0;
      for k=1:n
         dist = dist + (pat(k,i)-w(k,j))^2;
      end
      dist = sqrt(dist);
      if dist < mindist
         minindex = j;
         mindist = dist;
      end
   end

   eta = 0.1*(1-(i/(2*Q)));	% Update the learning rate

   for k=1:n						% Update the winning weight vector
      w(k,minindex) = w(k,minindex) + eta*(pat(k,i) - w(k,minindex));
   end
   
   if minindex == 1
     w1(xindex,1) =  w(1,minindex);
     w1(xindex,2) =  w(2,minindex);
     w1(xindex,3) =  w(3,minindex);
     xindex = xindex + 1;
   elseif minindex == 2
     w2(yindex,1) =  w(1,minindex);
     w2(yindex,2) =  w(2,minindex);
     w2(yindex,3) =  w(3,minindex);
     yindex = yindex + 1;
   end
  
end

 plot3(w1(:,1),w1(:,2),w1(:,3),'k-');
 plot3(w2(:,1),w2(:,2),w2(:,3),'k-');

for k=1:m
   plot3(w(1,k),w(2,k),w(3,k),'r^'); 
end
xlabel('x');
ylabel('y');
zlabel('z');
rotate3d on