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DIY_LPP_SVM2.m
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% DIY_LPP_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);
% 调用constructW函数计算近邻，为lpp提供近邻关系矩阵
fea = Source; %待处理矩阵输入，每一行是一个样本点
options = [];
options.NeighborMode = 'KNN'; %选择计算近邻的方法，此处用的是K近邻法
options.WeightMode = 'HeatKernel'; %根据样本点之间的距离关系计算W的函数类型,本程序中选择的是“热核”
options.k = 5; %近邻的个数
options.t = 0.1; %热核参数,不能太小，太小导致计算出来的W都趋近于0，导致LPP报错
W = constructW(fea,options);
% LPP算法进行降维
npc=17;
options.PCARatio = 1; %The percentage of principal
%component kept in【 the PCA
%step. The percentage is
%calculated based on the
%eigenvalue. Default is 1
%(100%, all the non-zero
%eigenvalues will be kept.
options.ReducedDim = npc; %目标空间的维数，与options.PCARatio的值有关，当options.PCARatio=0.8时<=3维，当options.PCARatio=1时<=10
[eigvector, eigvalue] = LPP(W, options, fea);
Re_Source= fea*eigvector;
input_test = Target*eigvector;%测试样本进行LPP投影
Re_Target = input_test;
% % from CDSL other methods
% batchS =Source;
% batchT =Target;
% [mappedA, mapping] = compute_mapping(batchS,'LPP',4);
% W=mapping.M;
% Re_Source=batchS*W;
% Re_Target=batchT*W;
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,Re_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);
% 调用constructW函数计算近邻，为lpp提供近邻关系矩阵
fea = Source; %待处理矩阵输入，每一行是一个样本点
options = [];
options.NeighborMode = 'KNN'; %选择计算近邻的方法，此处用的是K近邻法
options.WeightMode = 'HeatKernel'; %根据样本点之间的距离关系计算W的函数类型,本程序中选择的是“热核”
options.k = 5; %近邻的个数
options.t = 0.1; %热核参数,不能太小，太小导致计算出来的W都趋近于0，导致LPP报错
W = constructW(fea,options);
% LPP算法进行降维
npc=17;
options.PCARatio = 1; %The percentage of principal
%component kept in【 the PCA
%step. The percentage is
%calculated based on the
%eigenvalue. Default is 1
%(100%, all the non-zero
%eigenvalues will be kept.
options.ReducedDim = npc; %目标空间的维数，与options.PCARatio的值有关，当options.PCARatio=0.8时<=3维，当options.PCARatio=1时<=10
[eigvector, eigvalue] = LPP(W, options, fea);
Re_Source= fea*eigvector;
input_test = Target*eigvector;%测试样本进行LPP投影
Re_Target = input_test;
% % from CDSL other methods
% batchS =Source;
% batchT =Target;
% [mappedA, mapping] = compute_mapping(batchS,'LPP',4);
% W=mapping.M;
% Re_Source=batchS*W;
% Re_Target=batchT*W;
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,Re_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)