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

function [patterns, targets] = deterministic_annealing(train_patterns, train_targets, params, plot_on) 
 
%Reduce the number of data points using the deterministic annealing algorithm 
%Inputs: 
%	train_patterns	- Input patterns 
%	train_targets	- Input targets 
%	params			- [Number of output data points, Cooling rate] 
%   plot_on         - Plot stages of the algorithm 
% 
%Outputs 
%	patterns		- New patterns 
%	targets			- New targets 
 
if (nargin < 4), 
    plot_on = 0; 
end 
 
%Parameters: 
[Nmu, epsi] = process_params(params); 
T		    = max(eig(cov(train_patterns',1)'))/2;    %Initial temperature 
Tmin        = 0.01;                                 %Stopping temperature 
 
[d,L]	    = size(train_patterns); 
Ncent       = 1; 
label       = zeros(1,L); 
dist	    = zeros(Ncent,L); 
iter        = 0; 
max_change  = 1e-3; 
 
%Initialize the mu's 
mu			= mean(train_patterns')'; 
 
while (T > Tmin),   
    iter = iter + 1; 
     
    %Find the distances from mu's to patterns  
    for i = 1:Ncent, 
       dist(i,:) = sum((train_patterns - mu(:,i)*ones(1,L)).^2); 
    end 
    dist = exp(-dist/T); 
    
    %Compute Gibbs distribution 
    P = dist ./ (ones(Ncent,1) * sum(dist)); 
    
    %Recompute the mu's 
    old_mu = mu;    
    for i = 1:Ncent, 
       mu(:,i) = sum(((ones(d,1)*P(i,:)).*train_patterns)')'./(sum(P(i,:)));  
    end 
 
    if (sum(sum(abs(old_mu-mu))) <= max_change) 
        %Minimum reached, so decrease temperature ... 
        T = epsi * T; 
        if (Ncent >= Nmu), 
            %There are enough partitions 
            break 
        end 
         
        %...and add a center near the center that has the most variance 
        if (Ncent > 1), 
            for i = 1:Ncent, 
               dist(i,:) = sum((train_patterns - mu(:,i)*ones(1,L)).^2); 
            end 
            [m,label] = min(dist); 
            %Find the variance of the patterns around all the centers 
            Smu = zeros(1,Nmu); 
            for i = 1:Ncent, 
                Smu(i) = sum(std(train_patterns(:,find(label == i))')); 
            end 
            [m, max_std]  = max(Smu); 
        else 
            max_std = 1; 
        end 
        Ncent         = Ncent + 1; 
        mu(:,Ncent)   = mu(:,max_std) + randn(d,1).*std(mu')'/10; 
        mu(:,max_std) = mu(:,max_std) + randn(d,1).*std(mu')'/10; 
        %mu(:,Ncent) = randn(2,1); 
    end 
     
    %Plot centers during training 
    plot_process(mu, plot_on) 
end 
 
%Label the data 
dist	= zeros(Ncent,L); 
for i = 1:Ncent, 
   dist(i,:) = sum((train_patterns - mu(:,i)*ones(1,L)).^2); 
end 
[m,label] = min(dist); 
 
%Label the points 
[m,label] = min(dist); 
targets   = zeros(1,Ncent); 
Uc        = unique(train_targets); 
for i = 1:Ncent, 
    N = hist(train_targets(:,find(label == i)), Uc); 
    [m, max_l] = max(N); 
    targets(i) = Uc(max_l); 
end 
 
patterns = mu;