matlab_GA-SVM预测_GA_SVM_GA-SVM_matlab_SVM_

function [BestMSE,Bestc,Bestg,ga_option] = gaSVMcgForRegress(train_label,train_data,ga_option) %% 参数初始化 if nargin == 2 ga_option = struct('maxgen',200,'sizepop',20,'ggap',0.9,... 'cbound',[0,100],'gbound',[0,1000],'v',5); end % maxgen:最大的进化代数,默认为200,一般取值范围为[100,500] % sizepop:种群最大数量,默认为20,一般取值范围为[20,100] % cbound = [cmin,cmax],参数c的变化范围,默认为(0,100] % gbound = [gmin,gmax],参数g的变化范围,默认为[0,1000] % v:SVM Cross Validation参数,默认为5 %% MAXGEN = ga_option.maxgen; NIND = ga_option.sizepop; NVAR = 2; PRECI = 20; GGAP = ga_option.ggap; trace = zeros(MAXGEN,2); FieldID = ... [rep([PRECI],[1,NVAR]);[ga_option.cbound(1),ga_option.gbound(1);ga_option.cbound(2),ga_option.gbound(2)];... [1,1;0,0;0,1;1,1]]; Chrom = crtbp(NIND,NVAR*PRECI); gen = 1; v = ga_option.v; BestMSE = inf; Bestc = 0; Bestg = 0; %% cg = bs2rv(Chrom,FieldID); for nind = 1:NIND cmd = ['-v ',num2str(v),' -c ',num2str(cg(nind,1)),' -g ',num2str(cg(nind,2)),' -s 3 -p 0.01']; ObjV(nind,1) = svmtrain(train_label,train_data,cmd); end [BestMSE,I] = min(ObjV); Bestc = cg(I,1); Bestg = cg(I,2); %% while 1 FitnV = ranking(ObjV); SelCh = select('sus',Chrom,FitnV,GGAP); SelCh = recombin('xovsp',SelCh,0.7); SelCh = mut(SelCh); cg = bs2rv(SelCh,FieldID); for nind = 1:size(SelCh,1) cmd = ['-v ',num2str(v),' -c ',num2str(cg(nind,1)),' -g ',num2str(cg(nind,2)),' -s 3 -p 0.01']; ObjVSel(nind,1) = svmtrain(train_label,train_data,cmd); end [Chrom,ObjV] = reins(Chrom,SelCh,1,1,ObjV,ObjVSel); [NewBestCVaccuracy,I] = min(ObjV); cg_temp = bs2rv(Chrom,FieldID); temp_NewBestCVaccuracy = NewBestCVaccuracy; if NewBestCVaccuracy < BestMSE BestMSE = NewBestCVaccuracy; Bestc = cg_temp(I,1); Bestg = cg_temp(I,2); end if abs( NewBestCVaccuracy-BestMSE ) <= 10^(-2) && ... cg_temp(I,1) < Bestc BestMSE = NewBestCVaccuracy; Bestc = cg_temp(I,1); Bestg = cg_temp(I,2); end trace(gen,1) = min(ObjV); trace(gen,2) = sum(ObjV)/length(ObjV); if gen >= MAXGEN/2 && ... ( temp_NewBestCVaccuracy-BestMSE ) <= 10^(-2) break; end if gen == MAXGEN break; end gen = gen + 1; end %% figure; hold on; trace = round(trace*10000)/10000; plot(trace(1:gen,1),'r*-','LineWidth',1.5); plot(trace(1:gen,2),'o-','LineWidth',1.5); legend('最佳适应度','平均适应度'); xlabel('进化代数','FontSize',12); ylabel('适应度','FontSize',12); grid on; axis auto; line1 = '适应度曲线MSE[GAmethod]'; line2 = ['(终止代数=', ... num2str(gen),',种群数量pop=', ... num2str(NIND),')']; line3 = ['Best c=',num2str(Bestc),' g=',num2str(Bestg), ... ' MSE=',num2str(BestMSE)]; title({line1;line2;line3},'FontSize',12);