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

function [patterns, targets] = Deterministic_SA(train_patterns, train_targets, params, plot_on) 
 
%Reduce the number of data points using the deterministic simulated annealing algorithm 
%Inputs: 
%	train_patterns	- Input patterns 
%	train_targets	- Input targets 
%	params	    	- [Number of output data points, cooling rate (Between 0 and 1)] 
%   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    = T/500;                                 %Stopping temperature 
 
[d,L]	= size(train_patterns); 
label   = zeros(1,L); 
dist	= zeros(Nmu,L); 
iter    = 0; 
max_change = 1e-3; 
 
%Init the inclusion matrix 
inclusion_mat   = rand(Nmu, L); 
inclusion_mat   = inclusion_mat ./ (ones(Nmu,1)*sum(inclusion_mat)); 
 
if (Nmu == 1), 
    %Initialize the mu's 
    mu			= mean(train_patterns')'; 
else 
    %Initialize the P 
    P   = rand(Nmu,L); 
    P   = P ./ (ones(Nmu,1)*sum(P)); 
     
    while (T > Tmin),   
        iter    = iter + 1; 
        T = epsi * T; 
         
        for i = 1:L, 
            %For each node (example): 
            %Recompute the mu's 
            for i = 1:Nmu, 
               mu(:,i) = sum(((ones(d,1)*P(i,:)).*train_patterns)')'./(sum(P(i,:)));  
            end 
             
            %Find the distances from mu's to patterns  
            for i = 1:Nmu, 
               dist(i,:) = sum((train_patterns - mu(:,i)*ones(1,L)).^2); 
            end 
            dist = exp(-dist/T); 
            %In this implementation, s_i is equal to dist! 
                
            %Compute Gibbs distribution 
            P = dist ./ (ones(Nmu,1) * sum(dist)); 
            if (~isfinite(sum(sum(P)))) 
                disp('P is infinite. Stopping.') 
                break 
            end 
        end 
         
        %Plot centers during training 
        plot_process(mu, plot_on) 
     
    end     
end 
 
 
%Label the data 
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); 
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;