Matlab 版 (精华区)

发信人: zjliu (秋天的萝卜), 信区: Matlab
标  题: 偏最小二乘回归程序
发信站: BBS 哈工大紫丁香站 (Fri Mar 25 15:38:39 2005)

 以下是偏最小二乘回归的程序,详细算法见(王惠文著偏最小二乘回归应用一书,国防科
技大学出版社)
%偏最小二乘回归程序处理数据
                    clear  all
                    close all
                    clc
                   %原始数据处理的过程极其标准化的过程
                   [file1,path1]=uigetfile('*.*','请打开数据文件夹');
                   filename1=strcat(path1,file1);
                   prompt={'除Y外的列数','样本总数','提取成分的个数'};
                   def={'','',''};
                   lineNo=1;
                   dlgTitle='参数输入，偏最小二乘回归程序,吴建生';
                   answer=inputdlg(prompt,dlgTitle,lineNo,def);
                   answer=char(answer);
                   ny=str2num(answer(1,:));
                   nx=str2num(answer(2,:));
                   it=str2num(answer(3,:));
                   [file2,path2]=uiputfile('*.*','请选择要保存结果的文件夹');

                   filename2=strcat(path2,file2);
                    %读取所需数据
                   fid1=fopen(filename1,'r');
                   fid2=fopen(filename2,'w');
                   AA1=fscanf(fid1,'%f');
                   BB=reshape(AA1,ny+1,nx)';
                   [px,py]=size(BB);
fprintf(fid2,'#########************************************************######

##\n');
fprintf(fid2,'******************原始数据未经处理*****************************

*\n');
               for i=1:nx
                     for j=1:ny+1
                    fprintf(fid2,'%12d',BB(i,j));
                      end
                    fprintf(fid2,'\n');
               end
                    fclose(fid1);
                    xy=BB';
                    p=xy(1:ny,:);
                    t=xy(ny+1,:);
                    [pn,meanp,stdp,tn,meant,stdt]=prestd(p,t);
                    %建模样本
                    E0=pn';
                    F0=tn';
                  %检测样本
                    I=eye(ny,ny);
               for n=1:it
                   ww(:,n)=(E0'*F0)/norm(E0'*F0);
                   tt(:,n)=E0*ww(:,n);
                   pp(:,n)=(E0'*tt(:,n))/(tt(:,n)'*tt(:,n));
                   E0=E0-tt(:,n)*pp(:,n)';
               end
                   tta=[ones(nx,1),tt];
                   [b1,bint1,r1,rint1,stats1]=regress(F0,tta);
                   FY=tta*b1;
                   y1=poststd(FY,meant,stdt);
                   %最终的系数
                   AAA(:,1)=b1(2,1)*ww(:,1);
                   BBB=I-ww(:,1)*pp(:,1)';
                   for tc=2:it
                       AAA(:,tc)=b1(tc+1,1)*BBB*ww(:,tc)+AAA(:,tc-1);
                       BBB=BBB*[I-ww(:,tc)*pp(:,tc)'];
                   end
                   Y=t';
                   Y1=y1;
                    rejust=[Y/10,Y1/10,(Y-Y1)/10,abs(Y-Y1)./Y];
                    [px2,py2]=size(rejust);

fprintf(fid2,'*************************************************************\n

');
fprintf(fid2,'**（原始值）     （拟合值）      （误差）     （相对误差）
**\n');
          for i=1:px2
               for j=1:py2
                  fprintf(fid2,'%14.5f',rejust(i,j));
               end
               fprintf(fid2,'\n');
          end
               daer=sum(abs(rejust))/px2;

fprintf(fid2,'***************************************************************

*\n');
               fprintf(fid2,'**平均值**\n');
               for i=1
                     for j=1:py2
                         fprintf(fid2,'%14.5f',daer(i,j));
                     end
                     fprintf(fid2,'\n');
                 end
                 [px4,py4]=size(AAA);

fprintf(fid2,'******************************************************\n');
fprintf(fid2,'**************系数矩阵*******************************\n');
                 for i=1:px4
                     for j=1:py4
                         fprintf(fid2,'%14.5f',AAA(i,j));
                     end
                          fprintf(fid2,'\n');
                 end
                 ddrthe=corrcoef([p',t',tt]);
                 fcds1=ddrthe(1:ny+1,ny+2:ny+1+it);
                 fcds2=fcds1.*fcds1;
                 fcds3=sum(fcds2(1:ny,:))/ny;
                 fcds4=fcds2(ny+1,:);
                 dsa1=sum(fcds3);
                 dsa2=sum(fcds4);
                 [px11,py11]=size(fcds2);

fprintf(fid2,'************************************************\n');
  fprintf(fid2,'*****************累计解释能力***************\n');
                 for i=1:px11
                     for j=1:py11
                         fprintf(fid2,'%14.5f',fcds2(i,j));
                     end
                          fprintf(fid2,'\n');
                 end
                 aq1=[fcds3,dsa1;fcds4,dsa2];
                 [px12,py12]=size(aq1);

fprintf(fid2,'******************************************\n');
                 for i=1:px12
                     for j=1:py12
                         fprintf(fid2,'%14.5f',aq1(i,j));
                     end
                          fprintf(fid2,'\n');
                 end
                 u1=F0/b1(2,1);
                 u2=F0/b1(3,1);
                 u3=F0/b1(4,1);
                 figure(1)
                 xty=-3:0.001:3;
                 plot(tt(:,1),u1,'*',xty,xty,'-');
                 axis([-3,3,-4,3])
                 figure(2)
                 plot(tt(:,2),u2,'*',xty,xty,'-');
                 figure(3)
                 plot(tt(:,3),u3,'*',xty,xty,'-');
                 figure(4)
                 plot(1:px2,Y/10,'-*',1:px2,Y1/10,'-o');
                  xlabel('年份');
                  ylabel('降雨量');
                  title('拟合图形');
                  legend('实况值','拟合值');
                   fclose(fid2);
--
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