www.pudn.com > maldicode.zip > peak_ACO_S2N.m

function [S_best, best, err_best, err_S_best, err_S2N] = ACO_S2N(y,S2N,m,na,p,objf) 
 
%% Ant Colony Optimization 
 
%% Data 
% features  : y.features 
% data      : y.sample 
% labels    : y.label 
 
%% Step 1: Define the terms 
 
row         = size(y.sample_tr,1); 
const       = 1; 
Trail       = const * (ones(row,1));    % contstant 
DeltaTrail  = 0 * (ones(row,1));        % initially zero. 
 
Niter       = 500;  % The ants tend to converge around 700 - 800 in some  
                     % iterations and sometimes may need up to 1000. 
k           = round(na/3);      
kbest       = 100; 
eta         = 1; 
kappa       = 1; 
rho         = 0.1;     % (1- rho) represents the evaporation of pheromone trial 
Snew = zeros(na,m); 
    
%% Step 2: If in the first iteration 
 
  S = [];  
  for j = 1:na 
      R = randperm(row); 
      S = [S;R(1:m)]; 
  end 
St= {}; 
 for iter=1:Niter 
%      iter 
       % Step 3     
       for mm     = m-p+1:m 
           MaxUSM = []; 
           kk     = 1; 
           for j=1:na 
               U   = []; 
               Tg  = []; 
               LI  = []; 
               USM = []; 
                           
               for i=1:row      % Given subset Sj, Choose feature fi that maximizes USM_i^sj 
                   U(i) = 1;    % U(i) =1 if i is not an element of Sj, 0 otherwise 
                   for jj=1:mm-1 
                    if i==Snew(j,jj) 
                       U(i)  = 0; 
                       Tg(i) = 0; 
                       LI(i) = 0; 
                    end 
                   end 
 
                   if U(i) == 1 
                    LI(i) = S2N(i);                                   
                    Tg(i) = Trail(i); 
                   end 
               end 
                
               for i=1:row 
                    if U(i) == 0 
                       USM(i) = 0; 
                    else 
                       USM(i) = ((Tg(i)^eta)*(LI(i)^kappa))/sum(((Tg.^eta).*(LI.^kappa))); 
                    end 
               end 
                
               [USMmax,Indexmax] = max(USM); 
                
               % PROBABILITY!!! 
                
               % (e.g. q=0.8 implies 80% chance to Indexmax and 20% to 
               % chance to others according to their USM(i)) 
 
               qq = rand; 
               if qq <= 0.4 
                   sadd = Indexmax; 
               else 
                  features = USM;    
                  sumfea   = sum(features); 
                  rsumfea  = rand*sumfea; 
                  aa=0; sadd=1; 
                  for i=1:row 
                    aa=aa+features(i); 
                    if aa <= rsumfea 
                       sadd = i+1; 
                    end 
                  end 
               end 
                
               Snew(j,mm) = sadd; 
           end 
           %Snew = [Snew MaxUSM];     
       end 
                  
       if iter ~= 1 
        S = Snew; 
       end 
  
%% Step 4: Evaluate the selected subset of each ant using LOO-SVM 
 
        vindex = S'; 
        err_tr = feval(objf,vindex,y); 
        Ssets  = [S err_tr'];           % selected subsets with error as the last column 
        S_sort = sortbycol(Ssets,m+1);  % Sort the set based on error. 
        S      = S_sort(:,1:m);         % updated S 
         
        if S_sort(1,m+1) < kbest 
         best  = S_sort(1,:);          % Best features so far 
         kbest = S_sort(1,m+1); 
        end                           
    
        %% Step 5: Update Pheromone Trail 
 
        %S_best = S_sort(1:k,:); % the best k ants 
        S_best      = S_sort(:,:); 
        DeltaTrail  = zeros(row,1); 
         
        for j = 1:na 
             
            if S_best(j,m+1)== 0 
                iter = Niter; 
            else 
                DT            = 1/S_best(j,m+1); 
                i             = S_best(j,1:m); 
                DeltaTrail(i) = DeltaTrail(i) + DT; 
            end 
   
        end 
   
        Trail = rho*Trail + DeltaTrail;  
        Snew  = zeros(na,m); 
       for j = 1:na 
             S_best_vec = []; 
            for jj=1:k 
             S_best_vec = [S_best_vec S_best(jj,1:m)]; 
            end 
            Su_best_vec   = unique(S_best_vec); 
            leng          = length(Su_best_vec); 
            R             = randperm(leng); 
            Snew(j,1:m-p) = Su_best_vec(R(1:m-p)); 
       end 
         
% St =[St; S_best]; 
 
% save St3_2 St rho 
 end 
 
 vindex     = best(1:m)'; 
 err_best   = feval(objf,vindex,y); 
 vindex     = S_best(1,1:m)'; 
 err_S_best = feval(objf,vindex,y); 
 S2Nsorted  = sortbycold([(1:row)' S2N],2); 
 vindex     = S2Nsorted(1:m,1); 
 err_S2N    = feval(objf,vindex,y); 
     
return