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

function [test_targets, updates] = Deterministic_Boltzmann(train_patterns, train_targets, test_patterns, params); 
 
% Classify using the deterministic Boltzmann algorithm 
% Inputs: 
% 	train_patterns	- Train patterns 
%	train_targets	- Train targets 
%   test_patterns   - Test  patterns 
%	Params  - [Ni, Nh, eta, Type, param], where: 
%						Ni 	- Number of input units 
%						Nh 	- Number of hidden units 
%						eta	- Cooling rate 
%						Type	- Type of weak learner 
%                       params  - Weak learner parameters 
% 
% Outputs 
%	test_targets	- Predicted targets 
%   updates         - The updates to the network through the training iterations 
% 
% In this implementation, we train Ni weak learners, and find the best combination of  
% these classifiers 
 
[Nio,M]                     = size(train_patterns); 
No			                = 1; 
[Ni, Nh, eta, type, Wparams]= process_params(params); 
 
eta_anneal  = 0.95;	%Eta for the anneal process for the examples 
iter		= 0; 
max_iter    = 1e5; 
DispIter    = 50; 
update      = 0; 
err		    = 1; 
min_err	    = 0.01; 
 
%First, build the weak learners and find the appropriate label for each input pattern  
%for these classifiers 
weak_test_targets   = zeros(Ni, size(test_patterns,2)); 
weak_train_targets  = zeros(Ni, M); 
for i = 1:Ni, 
    weak_targets            = feval(type, train_patterns, train_targets, [train_patterns, test_patterns], Wparams); 
    weak_train_targets(i,:) = weak_targets(1:M); 
    weak_test_targets(i,:)  = weak_targets(M+1:end); 
end 
 
patterns = weak_train_targets*2-1;	%Inputs have to be {-1,+1} 
targets  = train_targets*2-1; 
 
%BEGIN BOLTZMANN LEARNING 
Tini	    = max(eig(cov(patterns',1)'))/2;    %Initial temperature 
Tmin        = 0.01;                             %Stopping temperature 
Tb		    = Tini; 
 
%Make a symmetric weight matrix with a zero diagonal 
zero_diag   = ones(Ni+Nh+No) - eye(Ni+Nh+No); 
W		    = rand(Ni+Nh+No)*2*sqrt(3/(Ni+Nh+No))-sqrt(3/(Ni+Nh+No)); 
W		    = W.*zero_diag; 
for i = 1:Ni+Nh+No, 
    for j = i:Ni+Nh+No, 
        W(i,j) = W(j,i); 
    end 
end 
 
 
while ((iter < max_iter) & (Tb > Tmin)), 
   %Randomally select a training pattern 
   in	= floor(rand(1)*M)+1; 
   x	= patterns(:,in); 
   t  = targets(in); 
    
   %Randomize input states Si 
   Sin = (rand(Nh,1)>0.5)*2-1; 
    
   %Anneal network with input and output clamped 
   T = Tini; 
   while (T > Tmin), 
      %Select a node randomally 
      i	= floor(rand(1)*Nh)+1; 
       
      %li<-sigma(w_ij*s_j) 
      S	= [x; Sin; t];  
      l	= W(:,i+Ni)'*S; 
       
      %Si<-f(li, T(k)) 
      Sin(i)	= tanh(l/T); 
       
      %Lower the temperature 
      T = T*eta_anneal;    
   end 
    
   %At final, low T, calculate [SiSj]alpha_i,alpha_o clamped 
   S	= [x; Sin; t];  
   SiSj_io_clamped = S*S'; 
    
   %Randomize input states Si 
   Sin= (rand(Nh+No,1)>0.5)*2-1; 
    
   %Anneal network with input clamped but output free 
   T = Tini; 
   while (T > Tmin), 
      %Select a node randomally 
      i	= floor(rand(1)*(Nh+No))+1; 
       
      %li<-sigma(w_ij*s_j) 
      S	= [x; Sin];  
      l	= W(:,i+Ni)'*S; 
       
      %Si<-f(li, T(k)) 
      Sin(i)	= tanh(l/T); 
       
      %Lower the temperature 
      T = T*eta_anneal;    
   end 
    
   %At final, low T, calculate [SiSj]alpha_i clamped 
   S	= [x; Sin]; 
   SiSj_i_clamped = S*S'; 
    
   %Wij<-Wij + eta/T([SiSj]alpha_i,alpha_o clamped - [SiSj]alpha_i clamped) 
   dW = (SiSj_io_clamped - SiSj_i_clamped).*zero_diag; 
   W  = W + eta*Tb*dW; 
    
   Tb 	= Tb * eta; 
   iter	= iter + 1; 
   updates(iter) = sum(sum(abs(dW)))/size(dW,1)^2; 
   if (floor(iter/DispIter) == iter/DispIter), 
      disp(['Iteration ' num2str(iter) ': Temperature is ' num2str(Tb), ' Average weight update is: ' num2str(update/DispIter)]) 
      update = 0; 
  else 
      update = update + updates(iter); 
   end 
    
end 
 
%Classify test patterns  
weak_test_targets = 2*weak_test_targets - 1; 
 
%Randomize input states Si 
Sin= (rand(Nh+No,size(test_patterns,2))>0.5)*2-1; 
 
%Anneal network with input clamped but output free 
T = Tini; 
while (T > Tmin), 
   %Select a node randomally 
   i	= floor(rand(1)*(Nh+No))+1; 
    
   %li<-sigma(w_ij*s_j) 
   S	= [weak_test_targets; Sin];  
   l	= W(:,i+Ni)'*S; 
    
   %Si<-f(li, T(k)) 
   Sin(i,:)	= tanh(l/T); 
    
   %Lower the temperature 
   T = T*eta_anneal;    
end 
 
So = Sin(Nh+1:end,:) > 0; 
 
test_targets = So;