最全的matlab实现pls的代码（0资源分）

function [c,m,v]=norm1(C) %对数据进行标准化处理 [n,s]=size(C); for i=1:n for j=1:s c(i,j)=(C(i,j)-mean(C(:,j)))/sqrt(cov(C(:,j))); end end m=mean(C); for j=1:s v(1,j)=sqrt(cov(C(:,j))); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function w=maxdet(A) %求矩阵的最大特征值 [v,d]=eig(A); [n,p]=size(d); d1=d*ones(p,1); d2=max(d1); i=find(d1==d2); w=v(:,i); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [t,q,w,wh,f0,FF]=fun717(px,py,C) % px自变量的输入个数 % py因变量的输入个数。 % C输入的自变量和因变量组成的矩阵 % t提取的主成分 % q为回归系数。 % w最大特征值所对应的特征向量。 % wh处理后的特征向量 % f0回归的标准化的方程系数 % FF原始变量的回归方程的系数 c=norm1(C); %norm1为标准化函数 %y=c(:,px+1:px+py); %截取标准化的因变量 E0=c(:,1:px); %截取标准化的自变量 F0=c(:,px+1:px+py); %截取标准化的因变量 A=E0'*F0*F0'*E0; %关联矩阵 w(:,1)=maxdet(A); % maxdet为求取最大特征值的函数 t(:,1)=E0*w(:,1); %第一主成分 p(:,1:px)=(E0'*t(:,1)/(t(:,1)'*t(:,1)))'; % p参 E(:,1:px)=E0-t(:,1)*(E0'*t(:,1)/(t(:,1)'*t(:,1)))'; % 残差矩阵 %进入循环 for i=0:px-2 B(:,px*i+1:px*i+px)=E(:,px*i+1:px*i+px)'*F0*F0'*E(:,px*i+1:px*i+px); w(:,i+2)=maxdet(B(:,px*i+1:px*i+px)); t(:,i+2)=E(:,px*i+1:px*i+px)*w(:,i+2); p(:,px*i+px+1:px*i+2*px)=(E(:,px*i+1:px*i+px)'*t(:,i+2)/(t(:,i+2)'*t(:,i+2)))'; E(:,px*i+px+1:px*i+2*px)=E(:,px*i+1:px*i+px)-t(:,i+2)*(E(:,px*i+1:px*i+px)'*t(:,i+2)/(t(:,i+2)'*t(:,i+2)))'; end for s=1:px q(:,s)=p(1,px*(s-1)+1:px*s)'; end [n,d]=size(q); for h=1:px iw=eye(d); for j=1:h-1 iw=iw*(eye(d)-w(:,j)*q(:,j)'); end wh(:,h)=iw*w(:,h); end for j=1:py zr(j,:)=(regress(F0(:,j),t))'; end for j=1:px for i=1:py w1=wh(:,1:j); zr1=(zr(i,1:j))'; f0(i,:,j)=(w1*zr1)'; %生成标准化变量的方程的系数矩阵 end [c,m,v]=norm1(C); ccxx=ones(py,1)*m(1,1:px); ccy=(v(1,px+1:px+py))'*ones(1,px); ccx=ones(py,1)*(v(1,1:px)); ff=ccy.*f0(:,:,j)./ccx; fff=-(sum((ccy.*ccxx.*f0(:,:,j)./ccx)')-m(1,px+1:px+py))'; FF(:,:,j)=[fff,ff]; %生成原始变量方程的常数项和系数矩阵 end 实验结果： %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% [t,q,w,wh,f0,FF]=fun717(3,3,C) t = -0.6429 -0.5914 -0.1305 -0.7697 -0.1667 0.1339 -0.9074 0.5212 0.0477 0.6884 0.6800 0.3458 -0.4867 -1.1328 -0.1818 -0.2291 0.0717 0.0247 -1.4037 0.0767 -0.5718 0.7436 0.2106 -0.0322 1.7151 0.6549 -1.5572 1.1626 -0.1668 0.3329 0.3645 -0.7007 0.2011 0.7433 -0.6983 0.0023 1.1867 0.7570 0.3362 -4.3898 0.7600 0.2554 -0.8232 -0.9738 -0.0826 -0.7490 0.5211 -0.6672 -0.3929 0.2034 0.5642 1.1993 -0.7827 0.0924 1.0485 -0.3729 0.3190 1.9424 1.1294 0.5677 q = -0.6659 0.0198 -0.6575 -0.6760 0.3547 0.2871 0.3589 1.1942 -0.6967 w = -0.5899 -0.4688 -0.6575 -0.7713 0.5680 0.2871 0.2389 0.6765 -0.6967 wh = -0.5899 -0.3679 -0.9346 -0.7713 0.6999 0.8023 0.2389 0.6356 -0.2228 f0(:,:,1) = -0.2015 -0.2635 0.0816 -0.2454 -0.3209 0.0994 -0.0843 -0.1103 0.0342 f0(:,:,2) = -0.0778 -0.4989 -0.1322 -0.1385 -0.5244 -0.0854 %取两个成分的标准数据回归系数 -0.0604 -0.1559 -0.0073 f0(:,:,3) = 0.3683 -0.8818 -0.0258 0.2872 -0.8898 0.0161 -0.2590 0.0146 -0.0546 FF(:,:,1) = 29.2002 -0.0431 -0.4350 0.0598 430.2511 -0.6220 -6.2712 0.8625 150.4807 -0.1752 -1.7662 0.2429 FF(:,:,2) = 47.0197 -0.0167 -0.8237 -0.0969 612.5671 -0.3509 -10.2477 -0.7412 %取两个成分的原始数据回归系数 183.9849 -0.1253 -2.4969 -0.0518 FF(:,:,3) = 47.9684 0.0788 -1.4558 -0.0190 623.2817 0.7277 -17.3872 0.1393 179.8868 -0.5379 0.2338 -0.3886 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %主成分分析程序 px=3; py=1; c=norm1(A); E0=c(:,1:px); F0=c(:,px+py); f0=pinv(E0'*E0)*E0'*F0; disp(['y1=' num2str(f0(1)) 'x1+(' num2str(f0(2)) ')x2+(' num2str(f0(3)) ')x3']) [c,m,v]=norm1(A); %待改进 for j=1:py ccxx=ones(py,1)*m(1,1:px); ccy=(v(1,px+1:px+py))'*ones(1,px); ccx=ones(py,1)*(v(1,1:px)); ff=ccy.*f0(:,:,j)./ccx; fff=-(sum((ccy.*ccxx.*f0(:,:,j)./ccx)')-m(1,px+1:px+py))'; FF(:,:,j)=[fff,ff]; end FF %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [r,Rdyt,RdYt,RdYtt,Rdytt,VIP]=fun8y(px,py,c) X=c(:,1:px); Y=c(:,px+1:px+py); x=norm1(X); y=norm1(Y); [t,q,w]=fun717(px,py,[X,Y]); r1=corrcoef([y,t]); r=r1(py+1:px+py,1:py)'; Rdyt=r.^2; RdYt=mean(Rdyt) for m=1:px RdYtt(1,m)=sum(RdYt(1,1:m)'); end for j=1:py for m=1:py Rdytt(j,m)=sum(Rdyt(j,1:m)'); end end for j=1:px for m=1:px Rd(j,m)=RdYt(1,1:m)*((w(j,1:m).^2)'); end end for j=1:px VIP(j,:)=sqrt((px*ones(1,px)./RdYtt).*Rd(j,:)); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [r,Rdxt,RdXt,RdXtt,Rdxtt]=fun8x(px,py,c) X=c(:,1:px); Y=c(:,px+1:px+py); x=norm1(X); y=norm1(Y); [t,q,w]=fun717(px,py,[X,Y]); r1=corrcoef([x,t]); r=r1(px+1:px+px,1:px)'; Rdxt=r.^2; RdXt=mean(Rdxt); for m=1:px RdXtt(1,m)=sum(RdXt(1,1:m)'); end for j=1:px for m=1:px Rdxtt(j,m)=sum(Rdxt(j,1:m)'); end end % for j=1:px % for m=1:px % Rd(j,m)=RdXt(1,1:m)*((w(j,1:m).^2)'); % end % end % for j=1:px % VIP(j,:)=sqrt((px*ones(1,px)./RdYtt).*Rd(j,:)); % end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [t,u]=TU(px,py,C) %t提取的自变量的主成分 %u 提取的因变量的主成分 c=norm1(C); y=c(:,px+1:px+py); E0=c(:,1:px); F0=c(:,px+1:px+py); A=E0'*F0*F0'*E0; w(:,1)=maxdet(A); t(:,1)=E0*w(:,1); B=F0'*E0*E0'*F0; cc(:,1)=maxdet(B); u(:,1)=F0*cc(:,1); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function drew(px,py,c) X=c(:,1:px); Y=c(:,px+1:px+py); [line,l]=size(Y); [t,q,w,wh,f0,FF]=fun717(px,py,c); YY=X*FF(:,2:px+1,3)'+ones(line,1)*FF(:,1,3)'; subplot(1,1,1,1) bar(f0(:,:,3)) title(' 直方图') legend('SG','TZBFB','FHL','JK','HPZD','JPZD','TZ','ZG','GPK') grid on plot(YY(:,4),Y(:,4),'+'); lsline for i=1:py v=mod(i,4); d=(i-v)/4; subplot(2,2,v,d+1) plot(YY(:,i),Y(:,i),'*'); lsline end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [ Qhj,Qh,prey]=crossval7(px,py,c) %px是自变量的个数； %py是因量 PRESShj=zeros(px,py); X=c(:,1:px); Y=c(:,px+1:px+py); x=norm1(X); y=norm1(Y); [line,row]=size(x); for h=1:px for j=1:line newx=X; newy=Y; newx(j,:)=[]; newy(j,:)=[]; [t,p0,w,wh,f0,FF]=fun717(px,py,[newx,newy]); prey(j,:,h)=X(j,:)*FF(:,2:px+1,h)'+FF(:,1,h)'; end PRESShj(h,:)=sum((Y-prey(:,:,h)).^2); end PRESSh=(sum(PRESShj'))'; for h=1:px [t1,p0,w,wh,f0,FF]=fun717(px,py,c); prey2(:,:,h)=X(:,:)*FF(:,2:px+1,h)'+ones(line,1)*FF(:,1,h)'; SShj(h,:)=sum((Y-prey2(:,:,h)).^2); end SSh=(sum(SShj'))'; Qhj=ones(px-1,py)-PRESShj(2:px,:)./SShj(1:px-1,:); % 错位 Qh=ones(px-1,1)-PRESSh(2:px,1)./SSh(1:px-1,1);