PLSmodel.zip_PLSmodel_matlab 建模_matlab 建模_回归

% 这是本人编写的PLS建模程序，希望对兄台有帮助。 function [pls_P,pls_Q,pls_W,pls_B]=pls_model(X_M, Y_M, iter) % reference: Paul Geladi and Bruce R. Kowalski. Partial Least-Square Regression: A Tutorial. Analytica Chimica Acta,1986, 185:19 % Fundation % X=TP'+E=∑tp'+E % Y=UQ'+F=∑uq'+F % u=bt[pls_P,pls_Q,pls_W,pls_B]=pls_model(X_M, Y_M, iter) % b=u't/t't % Y=TBQ'+F % input % X_M N by K the independant block, must be processed with meaning and scaling % Y_M N by P the dependant block, must be processed with meaning and scaling % iter-- the number of PLS components extracted % output % pls_P the loadings for every pls component of X % pls_Q the loadings for every pls componentof Y % pls_W the weights % pls_B the regression coefficients for the inner relation. [N,K]=size(X_M); [N,P]=size(Y_M); if iter > min(N,K) iter = min(N,K); end % start algrithm: PLS for counter = 1:iter; % extract 3 PLS components pls_u = Y_M(:,1); % get any column from Y t_old_n = 1.0;% the ini of t old t_new_n = 0.0;% the ini of t new pls_t = 0; while ( abs( t_old_n - t_new_n )>0.0001) pls_t_old = pls_t; % in the X block pls_w = (pls_u' * X_M / ( pls_u' * pls_u ))'; pls_w = pls_w/norm(pls_w); pls_t = X_M * pls_w; % in the Y block if(P>1) pls_q = (pls_t' * Y_M /(pls_t'*pls_t))'; pls_q = pls_q/norm(pls_q); pls_u = Y_M*pls_q; else pls_q=1; end % compare t,check for convergence t_old_n = norm( pls_t_old ); t_new_n = norm( pls_t); end pls_p = (pls_t'*X_M/(pls_t'*pls_t))'; pls_p_n=norm(pls_p); pls_p = pls_p/pls_p_n; pls_t = pls_t*pls_p_n; pls_w = pls_w*pls_p_n; pls_b = pls_u'*pls_t/(pls_t'*pls_t); X_M = X_M - pls_t*pls_p'; Y_M = Y_M - pls_b*pls_t*pls_q'; % pls_U(:,counter)=pls_u; % pls_T(:,counter)=pls_t; pls_P(:,counter)=pls_p; pls_Q(:,counter)=pls_q; pls_W(:,counter)=pls_w; pls_B(:,counter)=pls_b; end % Now that correction is completed, start validation. % Y_P = 0; % for counter = 1:iter % t_p = X_P*pls_W(:,counter); % X_P=X_P-t_p*pls_P(:,counter)'; % Y_P =Y_P + pls_B(:,counter)*t_p*pls_Q(:,counter)'; % end % for i = 1:m; % Y_P(i,:)=Y_P(i,:).*std_Y+ave_Y_new; % end