www.pudn.com > Classification_toolbox.part01.rar > DSLVQ.m

function [patterns, targets, w] = DSLVQ(train_patterns, train_targets, Nmu, plot_on) 
 
%Reduce the number of data points using distinction sensitive linear vector quantization  
%Inputs: 
%	train_patterns	- Input patterns 
%	train_targets	- Input targets 
%	Nmu				- Number of output data points 
%   plot_on         - Plot stages of the algorithm 
% 
%Outputs 
%	patterns		- New patterns 
%	targets			- New targets 
%	w				- Weights vector 
 
if (nargin < 4), 
    plot_on = 0; 
end 
 
Ndim    = size(train_patterns, 1); 
alpha   = 0.9; 
beta	= 0.1; 
L		= length(train_targets); 
dist	= zeros(Nmu,L); 
label   = zeros(1,L); 
 
%Initialize the mu's 
mu			= randn(Ndim,Nmu); 
mu			= sqrtm(cov(train_patterns',1))*mu + mean(train_patterns')'*ones(1,Nmu); 
mu_target   = rand(1,Nmu)>.5; 
old_mu	    = zeros(Ndim,Nmu); 
 
%Initialize the weight vector 
w			= ones(size(train_patterns,1),1); 
 
while (sum(sum(abs(mu - old_mu))) > 0.1), 
   old_mu = mu; 
    
   %Classify all the patterns to one of the mu's 
   for i = 1:Nmu, 
      dist(i,:) = sum(((w*ones(1,L)).*(train_patterns - mu(:,i)*ones(1,L))).^2);       
   end 
       
   %For each sample, ... 
   for i = 1:L, 
      %Find the nearest neighbor classified correctly, and the nearest one classified 
      %incorrectly 
      d	= dist(:,i).*(mu_target'-.5)*2; 
      dp = d;dn = d; 
      dp(find(dp<0)) = nan; 
      dn(find(dn>0)) = nan; 
      ci = find(dp == min(dp)); 
      cj = find(dn == max(dn)); 
      if (isempty(ci) | isempty(cj)), 
         break 
      end 
      di = abs(train_patterns(:,i) - mu(:,ci)); 
  	   dj = abs(train_patterns(:,i) - mu(:,cj)); 
      wn = (di-dj)/sum(abs(di-dj)); 
  	   nw	= w + beta*(wn - w); 
     	nw(find(nw>1)) 	= 1; 
      nw(find(nw<1e-4)) = 1e-4;       
      w	= nw./sum(abs(nw)); 
   end 
       
   %Label the points 
   [m,label] = min(dist); 
 
   %Label the mu's 
	for i = 1:Nmu, 
   	if (length(train_targets(:,find(label == i))) > 0), 
      	mu_target(i) = (sum(train_targets(:,find(label == i)))/length(train_targets(:,find(label == i))) > .5); 
	   end 
	end	 
    
   %Recompute the mu's 
   for i = 1:Nmu, 
      indices = find(label == i); 
      if ~isempty(indices), 
         Q		  = ones(Ndim,1) * (2*(train_targets(indices) == mu_target(i)) - 1); 
         mu(:,i) = mu(:,i) + mean(((train_patterns(:,indices)-mu(:,i)*ones(1,length(indices))).*Q)')'*alpha; 
      end 
       
   end 
    
   alpha = 0.95 * alpha; 
   beta	= 0.95 * beta; 
    
   %Plot centers during training 
   plot_process(mu, plot_on) 
 
end 
 
%Label the points 
[m,label] = min(dist); 
targets   = zeros(1,Nmu); 
Uc        = unique(train_targets); 
for i = 1:Nmu, 
    N = hist(train_targets(:,find(label == i)), Uc); 
    [m, max_l] = max(N); 
    targets(i) = Uc(max_l); 
end 
 
patterns = mu;