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DIY_OSC_SVM2.m
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% DIY_OSC_SVM dataset 2
%% setting 1
acc1 = zeros(1,2);
for SL = 2:3
clear -regexp [^SL,^acc1,^acc2]
clc
load('D:\A_\Enose_datasets\4个月原始数据\datasetB_prep.mat')
Source = batch1;
if SL == 2
Target = batch2;
Tbatch_label = batch2_label;
elseif SL == 3
Target = batch3;
Tbatch_label = batch3_label;
end
Source_label=(vec2ind(batch1_label'))';
Target_label=(vec2ind(Tbatch_label'))';
%归一化
Source=Source./repmat(sqrt(sum(Source.^2,1)),size(Source,1),1);
Target=Target./repmat(sqrt(sum(Target.^2,1)),size(Target,1),1);
%中心化
for i=1: size(Source,1)
Xmaster(i,:) = Source (i,:) - mean(Source);
end
for i=1: size(Target,1)
Xslave(i,:) = Target (i,:) - mean(Target);
end
% 样本子集选择
Num=30;
Hmaster=Xmaster*Xmaster';
Ht_master=diag(Hmaster);
[Amaster,Bmaster]=sort(Ht_master,'descend' );
Smaster_select=Xmaster(Bmaster(1:Num,:),:);
Hslave=Xslave*Xslave';
Ht_slave=diag(Hslave);
[Aslave,Bslave]=sort(Ht_slave,'descend' );
Sslave_select=Xslave(Bslave(1:Num,:),:);
% OSC
[x,nw,np,nt] = osccalc(Sslave_select,Smaster_select,1);
T = oscapp(Target,nw,np);
Re_Target = T;
error1=0;
counter=0;
for m=-5:0.2:5
c=10^m;
for n=-5:0.2:5
gama=10^n;
counter=counter+1;
try
% SVM:
cmd=[' -c ',num2str(c),' -g ',num2str(gama)]; %svmtrain参数
model = svmtrain(Source_label,Source,cmd);
[predict_label_test,] = svmpredict(Target_label,Re_Target, model);
d=diff([predict_label_test';Target_label']);
N = numel(find(d==0));
accur_test=N/size(Re_Target,1);
catch
error1 =error1 + 1;
accur_test = 0;
end
result(counter,1)=m;
result(counter,2)=n;
result(counter,3)=accur_test;
end
end
[max,index]=max(result(:,3));
acc1(SL-1) = max;
end
acc1_mean = mean(acc1)
%% setting 2
acc2 = zeros(1,2);
for SL = 2:3
clear -regexp [^SL,^acc1,^acc2,^acc1_mean]
clc
load('D:\A_\Enose_datasets\4个月原始数据\datasetB_prep.mat')
if SL-1 == 1
Source = batch1;
Sbatch_label = batch1_label;
elseif SL-1 == 2
Source = batch2;
Sbatch_label = batch2_label;
end
if SL == 2
Target = batch2;
Tbatch_label = batch2_label;
elseif SL == 3
Target = batch3;
Tbatch_label = batch3_label;
end
Source_label=(vec2ind(Sbatch_label'))';
Target_label=(vec2ind(Tbatch_label'))';
%归一化
Source=Source./repmat(sqrt(sum(Source.^2,1)),size(Source,1),1);
Target=Target./repmat(sqrt(sum(Target.^2,1)),size(Target,1),1);
%中心化
for i=1: size(Source,1)
Xmaster(i,:) = Source (i,:) - mean(Source);
end
for i=1: size(Target,1)
Xslave(i,:) = Target (i,:) - mean(Target);
end
% 样本子集选择
Num=30;
Hmaster=Xmaster*Xmaster';
Ht_master=diag(Hmaster);
[Amaster,Bmaster]=sort(Ht_master,'descend' );
Smaster_select=Xmaster(Bmaster(1:Num,:),:);
Hslave=Xslave*Xslave';
Ht_slave=diag(Hslave);
[Aslave,Bslave]=sort(Ht_slave,'descend' );
Sslave_select=Xslave(Bslave(1:Num,:),:);
% OSC
[x,nw,np,nt] = osccalc(Sslave_select,Smaster_select,1);
T = oscapp(Target,nw,np);
Re_Target = T;
error1=0;
counter=0;
for m=-5:0.2:5
c=10^m;
for n=-5:0.2:5
gama=10^n;
counter=counter+1;
try
% SVM:
cmd=[' -c ',num2str(c),' -g ',num2str(gama)]; %svmtrain参数
model = svmtrain(Source_label,Source,cmd);
[predict_label_test,] = svmpredict(Target_label,Re_Target, model);
d=diff([predict_label_test';Target_label']);
N = numel(find(d==0));
accur_test=N/size(Re_Target,1);
catch
error1 =error1 + 1;
accur_test = 0;
end
result(counter,1)=m;
result(counter,2)=n;
result(counter,3)=accur_test;
end
end
[max,index]=max(result(:,3));
acc2(SL-1) = max;
end
acc2_mean = mean(acc2)