SRD.zip_mapca.zip_光谱_光谱奇异值_红外光谱_近红外

function srd=srdMasterDemo(k) %========================================================================== % k is in {1,2,3,4,5,6,7} => No cross-validation scenarios % k is in {11,21,22,23,24,25,26} => Classical cross validation % k is in {101,102,103,104} => Leave multiple out cross validation %========================================================================== % % Load the 100 x 40 matrix of RMSECV values. This will be the input % matrix that SRD values will be computed on. % load CAIDataAndCV; res=outplsCA1Cal60CV100; % % Select case % close all; switch k %---------------------------------------------------------------------- % CASES 1 & 2: % Since A has more than 10 rows, the normal approximation % of the SRD distribution *must* be used in MATLAB. %---------------------------------------------------------------------- case {1,2} A=res.rmsecv'; % Merits if k==1, T='min'; else T=min(A,[],2); end; % Target exact=false; % Normal Approximation cv='none'; % No CV srd=srdMaster(A,T,exact,cv); %---------------------------------------------------------------------- % CASES 3 & 4: % In this case, I have articially truncated A to have 10 rows. As a % result, one can cpmpare the exact SRD distribution (k==3) versus % the normal approximation to the SRD distribution (k==4) %---------------------------------------------------------------------- case {3,4} A=res.rmsecv'; A=A(1:10,:); T=min(A,[],2); if k==3, exact=true; else exact=false; end; cv='none'; srd=srdMaster(A,T,exact,cv); %---------------------------------------------------------------------- % CASES 5 & 6: % Stacking of different merits %---------------------------------------------------------------------- case {5,6} switch k case 5, A={res.rmsecv',1-res.R2val'}; T={'min','min'}; case 6, A={res.rmsecv', res.R2val'}; T={'min','max'}; end exact=false; cv='none'; srd=srdMaster(A,T,exact,cv); %---------------------------------------------------------------------- % CASE 7: % Use 1 - R2 values %---------------------------------------------------------------------- case 7 A=1-res.R2val'; T='min'; exact=false; cv='none'; srd=srdMaster(A,T,exact,cv); %---------------------------------------------------------------------- % CASE 11: % We examine 5-fold cross-validation (CV). This is the same % as case 1 except that we use CV instead. %---------------------------------------------------------------------- case 11 A=res.rmsecv'; T='min'; exact=false; cv='classic'; nFold=5; srd=srdMaster(A,T,exact,cv,nFold); %---------------------------------------------------------------------- % CASES 21-26: STACKING with 5-fold CV. %---------------------------------------------------------------------- case {21,22,23,24,25,26} switch k case 21 A={1-res.R2cal',1-res.R2val'}; T={'min','min'}; case 22 A={res.R2cal',res.R2val'}; T={'max','max'}; case 23 A={res.rmsecv',1-res.R2val'}; T={'min','min'}; case 24 A={res.rmsecv',res.R2val'}; T={'min','max'}; case 25 A={res.rmsecv',res.rmsec',1-res.R2cal',1-res.R2val'}; T='min'; case 26 A={res.rmsecv',res.rmsec',res.R2cal',res.R2val'}; T={'min','min','max','max'}; end exact=false; cv='classic'; nFold=5; srd=srdMaster(A,T,exact,cv,nFold); %---------------------------------------------------------------------- % CASES 31: STACKING whose blocks have rows of *unequal* size %---------------------------------------------------------------------- case 31 A={1-res.R2cal(:,1:80)',1-res.R2val(:,1:63)'}; T={'min','min'}; exact=false; cv='classic'; nFold=5; srd=srdMaster(A,T,exact,cv,nFold); %---------------------------------------------------------------------- % CASE 101: % Similar to case 11 except that we are using many LMO iterations %---------------------------------------------------------------------- case 101 A=res.rmsecv'; T='min'; exact=false; cv='lmo'; nIteration=20; percent=60; srd=srdMaster(A,T,exact,cv,nIteration,percent); %---------------------------------------------------------------------- % CASES 102: STACKING whose blocks have rows of equal size %---------------------------------------------------------------------- case 102 A={1-res.R2cal',1-res.R2val'}; T={'min','min'}; exact=false; cv='lmo'; nIteration=10; percent=75; allMix=false; srd=srdMaster(A,T,exact,cv,nIteration,percent,allMix); %---------------------------------------------------------------------- % CASES 103: same as case 102 except allMix=true %---------------------------------------------------------------------- case 103 A={1-res.R2cal',1-res.R2val'}; T={'min','min'}; exact=false; cv='lmo'; nIteration=10; percent=75; allMix=true; srd=srdMaster(A,T,exact,cv,nIteration,percent,allMix); %---------------------------------------------------------------------- % CASES 104: STACKING whose blocks have rows of *unequal* size %---------------------------------------------------------------------- case 104 A={1-res.R2cal(:,1:80)',1-res.R2val(:,1:63)'}; T={'min','min'}; exact=false; cv='lmo'; nIteration=10; percent=50; srd=srdMaster(A,T,exact,cv,nIteration,percent); otherwise error('Illegal case selection for k.') end % % Plot the SRDs % srdPlots(srd); %========================================================================== end