clc;
clear;
%% 导入数据
data=importdata('mfeat-kar');
for i=0:9
for j=1:200
data(i*200+j:((i+1)*200),65)=i;
end
end
%% 随机划分训练集和测试集
N=size(data);
ind = randperm(N(1)); %产生一个随机的排序
sample_train=data(ind(1:N(1)*0.5),1:65); %随机挑选出训练样本, 50%
sample_test=data(ind(N(1)*0.5+1:end),1:65); %随机挑选出测试样本, 50%
%% 将训练集划分为有标记的数据集L和无标记的数据集U L/U=90%
sample_train_L=sample_train(1:100,:); %得到有标记训练样本占训练集10%
sample_train_U=sample_train(101:end,1:64); %得到无标记训练样本占训练集90%
L1=zeros(1000,33);
L2=zeros(1000,33);
%将L分为两个视图L1 和 L2
L1(1:100,1:32)=sample_train_L(1:100,1:32);
L1(1:100,33)=sample_train_L(1:100,65);
L2(1:100,1:32)=sample_train_L(1:100,33:64);
L2(1:100,33)=sample_train_L(1:100,65);
for i=1:9
U11=sample_train_U(((i-1)*100+1):(i*100),1:32); % 得到一个初始的无标记子集U'
meas1=L1(1:(i*100),1:32);
species1=L1(1:(i*100),33);
by1=fitcnb(meas1,species1);
R1=by1.predict(U11);
result1(1:100,1:32)=L1(1:100,1:32);
result1(1:100,33)=R1(1:100,1);
U12=sample_train_U(((i-1)*100+1):(i*100),33:64);
meas2=L2(1:(i*100),1:32);
species2=L2(1:(i*100),33);
by2=fitcnb(meas2,species2);
R2=by2.predict(U12);
result2(1:100,1:32)=L2(1:100,1:32);
result2(1:100,33)=R2(1:100,1);
L1((i*100+1):((i+1)*100),:)=result2(1:100,:);
L2((i*100+1):((i+1)*100),:)=result1(1:100,:);
end
%% h1对测试集进行分类
sample_test_t1=sample_test(:,1:32); %测试集的样本特征
sample_test_t2=sample_test(:,33:64);
sample_test_c=sample_test(:,65); %测试集的类别
last_r1=by1.predict(sample_test_t1);
cMat1=confusionmat(sample_test_c,last_r1);
r=diag(cMat1);
accuracy1=(sum(r))/1000;
%% h2对测试集进行分类
last_r2=by2.predict(sample_test_t2);
cMat2=confusionmat(sample_test_c,last_r2);
r2=diag(cMat2);
accuracy2=(sum(r2))/2000;
disp('accuracy= ');
if(accuracy1>=accuracy2)
disp(accuracy1);
else
disp(accuracy2);
end
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