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

function [test_targets, E] = ada_boost(train_patterns, train_targets, test_patterns, params) 
 
% Classify using the AdaBoost algorithm 
% Inputs: 
% 	train_patterns	- Train patterns 
%	train_targets	- Train targets 
%   test_patterns   - Test  patterns 
%	Params	- [NumberOfIterations, Weak Learner Type, Learner's parameters] 
% 
% Outputs 
%	test_targets	- Predicted targets 
%   E               - Errors through the iterations 
% 
% NOTE: Suitable for only two classes 
% 
 
[k_max, weak_learner, alg_param] = process_params(params); 
 
[Ni,M]			= size(train_patterns); 
W			 	= ones(1,M)/M; 
IterDisp		= 10; 
 
full_patterns   = [train_patterns, test_patterns]; 
test_targets    = zeros(1, size(test_patterns,2)); 
 
%Do the AdaBoosting 
for k = 1:k_max, 
   %Train weak learner Ck using the data sampled according to W: 
   %...so sample the data according to W 
   randnum = rand(1,M); 
   cW	   = cumsum(W); 
   indices = zeros(1,M); 
   for i = 1:M, 
      %Find which bin the random number falls into 
      loc = max(find(randnum(i) > cW))+1; 
      if isempty(loc) 
         indices(i) = 1; 
      else 
         indices(i) = loc; 
      end 
   end 
    
   %...and now train the classifier 
   Ck 	= feval(weak_learner, train_patterns(:, indices), train_targets(indices), full_patterns, alg_param); 
 
   %Ek <- Training error of Ck  
   E(k) = sum(W.*(Ck(1:M) ~= train_targets)); 
    
   if (E(k) == 0), 
      break 
   end 
    
   %alpha_k <- 1/2*ln(1-Ek)/Ek) 
   alpha_k = 0.5*log((1-E(k))/E(k)); 
    
   %W_k+1 = W_k/Z*exp(+/-alpha) 
   W  = W.*exp(alpha_k*(xor(Ck(1:M),train_targets)*2-1)); 
   W  = W./sum(W); 
    
   %Update the test targets 
   test_targets  = test_targets + alpha_k*(2*Ck(M+1:end)-1); 
    
   if (k/IterDisp == floor(k/IterDisp)), 
      disp(['Completed ' num2str(k) ' boosting iterations']) 
   end 
    
end 
 
test_targets = test_targets > 0;