yangplus.m

粒子群算法

clear
% H1=xlsread('tading.xls',1,'A1:H123');
H1=xlsread('456.xls',1,'A1:E177');
len=size(H1,1);
H2=H1(101:177,5);

delta=(max(H2)-min(H2));  %反归一
delta1=min(H2); 


 
 data=H1;
 data=(data-ones(size(data,1),1)*min(data))./(ones(size(data,1),1)*(max(data)-min(data)));
  H1=data-ones(size(data,1),1)*mean(data);
  
 
 A1=H1(1:100,1:4);
 B1=H1(1:100,5);
 C1=H1(101:177,1:4);
 D1=H1(101:177,5);
 
      
   
       max_ev =8;  %选择主元个数
      rbf_var = 1.5;  %髙斯函数的参数
      cov_size=size(A1,1);     %样本个数
      for i=1:cov_size,    %得到K
          for j=i:cov_size,
              K(i,j) = exp(-norm(A1(i,:)-A1(j,:))^2/rbf_var);
              K(j,i) = K(i,j);
          end
      end
      unit = ones(cov_size, cov_size)/cov_size;
      % centering in feature space!
      K_n = K - unit*K - K*unit + unit*K*unit;

      [evecs,evals] = eig(K_n);
     % A2=evecs;
      evals = real(diag(evals));
      for i=1:cov_size,
          evecs(:,i) = evecs(:,i)/(sqrt(evals(i)));   
      end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
      test_num=size(A1,1);

      unit_test = ones(test_num,cov_size)/cov_size;
      K_test = zeros(test_num,cov_size);
      for i=1:test_num,
          for j=1:cov_size,
              K_test(i,j) = exp(-norm(A1(i,:)-A1(j,:))^2/rbf_var);
          end
      end
      K_test_n = K_test - unit_test*K - K_test*unit + unit_test*K*unit;
      test_features = zeros(test_num, max_ev);
      test_features = K_test_n * evecs(:,1:max_ev);
      A2=test_features;


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
      test_num=size(C1,1);
      unit_test = ones(test_num,cov_size)/cov_size;
      K_test = zeros(test_num,cov_size);
      for i=1:test_num,
          for j=1:cov_size,
              K_test(i,j) = exp(-norm(C1(i,:)-A1(j,:))^2/rbf_var);
          end
      end
      K_test_n = K_test - unit_test*K - K_test*unit + unit_test*K*unit;
      test_features = zeros(test_num, max_ev);
      test_features = K_test_n * evecs(:,1:max_ev);
      C2=test_features;


      model = svmtrain(B1, A2, '-s 3 -t 2 -g 0.1 -c 50 -p 0.01');
      [y, accuracy] = svmpredict(D1, C2, model);
      
   
          Y=y;         
 Y=Y+mean(data(:,5));
%  D=data(:,9);
 Y= Y*delta+delta1;
%  D= D*delta+delta1;
 X=abs(H2-Y)./H2;

%  X=abs(D-Y)./D;

meanerror=mean(X)
maxerror=max(X)
stderror=std(X)

 plot(H2,'r--','LineWidth',2 );
%  axis(V);
 hold on
 plot(Y,'b','linewidth',2);
%  axis(V);