function [features, targets] = LVQ1(train_features, train_targets, Nmu, region, plot_on)%Reduce the number of data points using linear vector quantization%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%OR%	D					- Decision regionif (nargin < 5),    plot_on = 0;endalpha = 0.9;L		= length(train_targets);dist	= zeros(Nmu,L);label = zeros(1,L);%Initialize the mu'smu			= randn(2,Nmu);mu			= sqrtm(cov(train_features'))*mu + mean(train_features')'*ones(1,Nmu);mu_target= rand(1,Nmu)>0.5;old_mu	= zeros(2,Nmu);while (sum(sum(abs(mu - old_mu))) > 0.1),   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);      %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(2,1) * (2*(train_targets(indices) == mu_target(i)) - 1);         mu(:,i) = mu(:,i) + mean(((train_features(:,indices)-mu(:,i)*ones(1,length(indices))).*Q)')'*alpha;      end         end      alpha = 0.95 * alpha;      if (plot_on == 1),       plot_process(mu)   endend%Make the decision regiontargets = 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	endelse   %There is only one center   targets = (sum(train_targets)/length(train_targets) > .5);endfeatures = mu;if (nargout == 1),   features = Nearest_Neighbor(features, targets, 1, region);end