S-G平滑.zip_s-g平滑_去噪_去噪平滑_平滑_平滑去噪

clc;clear;close all disp('原始数据，不同预处理方法进行处理的RMSECV,RMSEP,训练集及预测集的R') load UV cal = calset(:,1:600); caltar = calsettar; for columm = 1:4; dataname = ynames{columm}; disp(dataname); %----------------------------因子数的确定-------------------------------- switch columm case 1 maxrank = 8; case 2 maxrank = 6; case 3 maxrank = 7; case 4 maxrank = 6; end [m,n] = size(cal); %-----------------KS对数据划分，训练集2/3，预测集1/3--------------------- [model,test] = kenstone(cal,floor(2/3*m)); x_train = cal(model,:); x_pred = cal(test,:); y_train = caltar(model,columm); y_pred = caltar(test,columm); [m_train,n_train] = size(x_train); %-------------------------------PLS------------------------------------ disp('偏最小二乘-PLS') b = simpls(x_train,y_train,maxrank); c = x_pred*b(:,maxrank); [c_train,valpresses] = loocv(x_train,y_train,maxrank); disp('RMSECV,RMSEP,训练集及预测集的R'); rmsecv_PLS = sqrt(valpresses./m_train); rmsep_PLS = rms(c-y_pred); r_train = corrcoef(c_train,y_train); r_train = r_train(1,2); r_PLS = corrcoef(c,y_pred); r_PLS = r_PLS(1,2); disp([rmsecv_PLS,rmsep_PLS,r_train,r_PLS]); disp('回收率最大最小值'); recovery_PLS = 100*c./y_pred; max_recovery_PLS(columm) = max(recovery_PLS); min_recovery_PLS(columm) = min(recovery_PLS); disp([max_recovery_PLS(columm),min_recovery_PLS(columm)]); clear b c c_train valpresses r_train %--------------------------------------------------------------------- ttt = 1; for window = 3:2:40 disp(window); disp('S-G平滑'); x_train_SG = sgdiff(x_train,2,window,2); x_pred_SG = sgdiff(x_pred,2,window,2); [p, q, w, b] = ch_pls_new(x_train_SG,y_train,maxrank); c = ch_plspred(x_pred_SG, p, q, w, b,maxrank); disp('RMSEP及R'); rmsep_SG(ttt) = rms(c-y_pred); R_SG = corrcoef(c,y_pred); r_SG(ttt) = R_SG(1,2); disp([rmsep_SG,r_SG]); disp('回收率最大最小值'); recovery_SG = 100*c./y_pred; max_recovery_SG(ttt) = max(recovery_SG); min_recovery_SG(ttt) = min(recovery_SG); ttt = ttt + 1; disp([max_recovery_SG,min_recovery_SG]); end text_size = 16; figure;subplot(2,2,1);plot([3:2:40],rmsep_SG,'o-','LineWidth',2);set(gca,'fontsize',text_size);title(dataname,'fontsize',text_size+1); xlabel('Window','fontsize',text_size);ylabel('RMSEP','fontsize',text_size); subplot(2,2,2);plot([3:2:40],r_SG,'o-','LineWidth',2);set(gca,'fontsize',text_size);title(dataname,'fontsize',text_size+1); xlabel('Window','fontsize',text_size);ylabel('R','fontsize',text_size); subplot(2,2,3);plot([3:2:40],max_recovery_SG,'*-','LineWidth',2);set(gca,'fontsize',text_size);title(dataname,'fontsize',text_size+1); xlabel('Window','fontsize',text_size);ylabel('Max-recovery','fontsize',text_size); subplot(2,2,4);plot([3:2:40],min_recovery_SG,'*-','LineWidth',2);set(gca,'fontsize',text_size);title(dataname,'fontsize',text_size+1); xlabel('Window','fontsize',text_size);ylabel('Min-recovery','fontsize',text_size); end