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

function [patterns, targets, label, J] = BIMSEC(train_patterns, train_targets, params, plot_on) 
 
%Reduce the number of data points using the basic iterative MSE clustering algorithm 
%Inputs: 
%	train_patterns	- Input patterns 
%	train_targets	- Input targets 
%	params			- Algorithm parameters: [Number of output data points, Number of attempts] 
%   plot_on         - Plot stages of the algorithm 
% 
%Outputs 
%	patterns		- New patterns 
%	targets			- New targets 
%	label			- The labels given for each of the original patterns 
 
if (nargin < 4), 
    plot_on = 0; 
end 
[Nmu, Ntries] = process_params(params); 
 
[D,L]	= size(train_patterns); 
dist	= zeros(Nmu,L); 
label   = zeros(1,L); 
Uc      = unique(train_targets); 
 
%Initialize the mu's 
mu			= randn(D,Nmu); 
mu			= sqrtm(cov(train_patterns',1))*mu + mean(train_patterns')'*ones(1,Nmu); 
ro          = zeros(1,Nmu); 
n           = zeros(1,Nmu); 
Ji          = zeros(1,Nmu); 
J           = 1; 
iter        = 1; 
 
if (Nmu == 1), 
   mu		= mean(train_patterns')'; 
   label	= ones(1,L); 
else   
    %Assign each example to one of the mu's 
    %Compute distances 
    dist    = zeros(Nmu, L); 
    for i = 1:Nmu, 
        dist(i,:) = sqrt(sum((mu(:,i)*ones(1,L) - train_patterns).^2)); 
    end 
    [m, label]  = min(dist); 
    n           = hist(label, Nmu); 
 
    while (Ntries > 0), 
         
        iter        = iter + 1; 
        J(iter)     = 0; 
         
        %Select a sample x_hat   
        r     = randperm(L); 
        x_hat = train_patterns(:,r(1)); 
         
        %i <- argmin||mi - x_hat|| 
        dist  = sqrt(sum((mu - x_hat * ones(1,Nmu)).^2)); 
        i     = find(dist == min(dist)); 
         
        %Compute ro if n(i) ~= 1 
        if (n(i) ~=1), 
            for j = 1:Nmu, 
                if (i ~= j), 
                    ro(j) = n(j)/(n(j)+1)*dist(j)^2; 
                else 
                    ro(j) = n(j)/(n(j)-1)*dist(j)^2; 
                end 
            end 
             
            %Transfer x_hat if needed 
            [m, k] = find(min(ro) == ro); 
            if (k ~= i), 
                label(r(1)) = k; 
                n(i)        = n(i) - 1; 
                n(k)        = n(k) + 1; 
                 
                %Recompute Je, and the mu's 
                for j = 1:Nmu, 
                    indexes = find(label == j); 
                    mu(:,j) = mean(train_patterns(:,indexes)')'; 
                    Ji(j)   = sum(sum((mu(:,j)*ones(1,length(indexes)) - train_patterns(:,indexes)).^2)); 
                end 
                 
                J(iter)     = sum(Ji); 
            end 
                 
        end 
                  
        %Plot the centers during the process  
        plot_process(mu, plot_on) 
  
        if (J(iter) == J(iter-1)), 
            Ntries = Ntries - 1; 
        end 
 
    end 
end 
 
%Classify all the patterns to one of the mu's (1-NN) 
dist = zeros(Nmu,L); 
for i = 1:Nmu, 
   dist(i,:) = sum((train_patterns - mu(:,i)*ones(1,L)).^2); 
end 
    
%Label the points 
[m,label] = min(dist); 
targets   = zeros(1,Nmu); 
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;