PLS.rar_PLS MATLAB _PLS算法_pls

clc clear clf %=============data============= X=[0.136 0.00248 0.00158 0.138 0.00236 0.00167 0.14 0.00229 0.00171 0.142 0.00221 0.00168 0.143 0.0022 0.00171 0.147 0.00212 0.00176 0.148 0.00206 0.00175 0.152 0.00206 0.00176 0.155 0.00198 0.00178 0.158 0.00197 0.0018 0.159 0.00192 0.00183 0.161 0.00185 0.00186 0.162 0.00184 0.00192 0.179 0.00142 0.00203 ]; Y=[0 5 10 15 20 25 30 35 40 45 50 55 60 100 ]; %+++++++++Analysis according to method of Partial least squares [S,T]=regress(Y,X); symS P1 P2 P3 PY X1 X2 X3 P1=num2str(S(1)); %get the Estimated function P2=num2str(S(2)); P3=num2str(S(3)); PY=P1*X1+P2*X2+P3*X3 disp('Estimated function') disp(PY) SY=X*S; %get the Estimated procent disp('Estimated procent') disp(SY) [p3,n3]=size(SY); %maeke figure of points of model-predicted versus actua% robusta in arabica/robusta mixtures for i=1:p3 plot(Y,SY,'x') end grid on hold on q=polyfit(Y,SY,1); %make the line of model-predicted versus actua% robusta in arabica/robusta mixtures SSY=polyval(q,Y); plot(Y,SSY) axis([-5,110,-5,110]) xlabel('Actual robusta percentage') ylabel('Estimated Robusta percentage') title('model-predicted versus actua% robusta in arabica/robusta mixtures')