k_means.m

function [features, targets, label] = k_means(train_features, train_targets, Nmu, region, plot_on) 
 
%Reduce the number of data points using the k-means algorithm 
%Inputs: 
%	train_features	- Input features 
%	train_targets	- Input targets 
%	Nmu				- Number of output data points 
%	region			- Decision region vector: [-x x -y y number_of_points] 
%   plot_on         - Plot stages of the algorithm 
% 
%Outputs 
%	features			- New features 
%	targets			- New targets 
%	label				- The labels given for each of the original features 
 
if (nargin < 5), 
    plot_on = 0; 
end 
 
[D,L]	= size(train_features); 
dist	= zeros(Nmu,L); 
label = zeros(1,L); 
 
%Initialize the mu's 
mu			= randn(D,Nmu); 
mu			= sqrtm(cov(train_features',1))*mu + mean(train_features')'*ones(1,Nmu); 
old_mu	= zeros(D,Nmu); 
 
switch Nmu, 
case 0, 
    mu      = []; 
    label   = []; 
case 1, 
   mu		= mean(train_features')'; 
   label	= ones(1,L); 
otherwise 
   while (sum(sum(mu == old_mu)) == 0), 
      old_mu = mu; 
       
      %Classify all the features to one of the mu's 
      for i = 1:Nmu, 
         dist(i,:) = sum((train_features - mu(:,i)*ones(1,L)).^2); 
      end 
       
      %Label the points 
      [m,label] = min(dist); 
       
      %Recompute the mu's 
      for i = 1:Nmu, 
         mu(:,i) = mean(train_features(:,find(label == i))')'; 
      end 
 
      if (plot_on == 1), 
        plot_process(mu) 
      end 
 
   end 
end 
    
%Make the decision region 
targets = zeros(1,Nmu); 
if (Nmu > 1), 
	for i = 1:Nmu, 
   	if (length(train_targets(:,find(label == i))) > 0), 
      	targets(i) = (sum(train_targets(:,find(label == i)))/length(train_targets(:,find(label == i))) > .5); 
   	end 
	end 
else 
   %There is only one center 
   targets = (sum(train_targets)/length(train_targets) > .5); 
end 
 
features = mu;