光谱特征提取算法BOSS（MATLAB代码）.zip

function BOSS=boss(Xcal,ycal,nLV_max,fold,method,num_bootstrap,flag,speed) %% Initial settings if nargin<8; speed=0;end; if nargin<7; flag=0;end; if nargin<6; num_bootstrap=1000;end; if nargin<5; method=('center');end; if nargin<4; fold=5;end; if nargin<3; nLV_max=10;end if num_bootstrap>=2000 ratio=0.05; else if num_bootstrap<2000 && num_bootstrap>=1000 ratio=0.1; else if num_bootstrap<1000 && num_bootstrap>=500 ratio=0.2; else if num_bootstrap<500 fprintf('The number of bootstrap sampling ought to be larger than 500') BBS=[]; return end end end end [~,n]=size(Xcal); num_retained=n; num_best=round(num_bootstrap*ratio); w=ones(n,1)/n; Variable=zeros(n,100); W=zeros(n,100); RMSECV=zeros(1,100); for j=1:100 variable_index=zeros(n,num_bootstrap); for i=1:num_bootstrap Vsel=randsample(n,num_retained,true,w); Vsel=unique(Vsel); variable_index(Vsel,i)=1; end B=zeros(n,num_bootstrap); for i=1:num_bootstrap vsel=find(variable_index(:,i)==1); CV=plscvfold(Xcal(:,vsel),ycal,nLV_max,fold,method); rmsecv(i)=CV.RMSECV; PLS=pls(Xcal(:,vsel),ycal,CV.optPC,method); coef=PLS.regcoef_pretreat; coef=coef/norm(coef); b=zeros(n,1); b(vsel)=coef; B(:,i)=b; end [rmsecv_sort,index]=sort(rmsecv); RMSECV(j)=rmsecv(index(1)); Variable(:,j)=variable_index(:,index(1)); %w=sum(B,2); w=abs(sum(B(:,index(1:num_best)),2)); % w=sum(variable_index(:,index(1:num_best)),2); w=w/norm(w); W(:,j)=w; if flag==1 figure(1) bar(w) xlabel('varaible') ylabel('weight') axis([0 n 0 max(w)*1.1]) drawnow end % figure % plot(w) % figure % imagesc(B(:,index)') % figure % plot(sum(B(:,index(1:500))')) if speed==0 num_retained=sum(sum(variable_index))/num_bootstrap; elseif speed==1 num_retained=sum(sum(variable_index))/num_bootstrap*1.1; elseif speed==2 num_retained=sum(sum(variable_index))/num_bootstrap*1.2; elseif speed==3 num_retained=sum(sum(variable_index))/num_bootstrap*1.3; elseif speed==4 num_retained=sum(sum(variable_index))/num_bootstrap*1.4; elseif speed==5 num_retained=sum(sum(variable_index))/num_bootstrap*1.5; end % num_retained=sum(sum(variable_index))/num_bootstrap; num_retained=floor(num_retained); Num_retained(i)=num_retained; if num_retained<=1 break end j end if j<100 W(:,j+1:100)=[]; RMSECV(j+1:end)=[]; end in=find(RMSECV==min(RMSECV)); in=in(end); CV=plscvfold(Xcal(:,Variable(:,in)==1),ycal,nLV_max,fold,method); BOSS.minRMSECV=CV.RMSECV; BOSS.RMSECV_all=RMSECV; BOSS.Q2_max=CV.Q2_max; BOSS.variable_index=Variable(:,in); BOSS.nVAR=length(Variable(:,in)==1); BOSS.optPC=CV.optPC; BOSS.weight=W(:,in); BOSS.W=W;