testing3.m

此算法利用一种有监督的人工免疫系统实现一个图像分类的分类器。

function main()
clc; clear all;
Method='class';
load center1
%读入第一个样区的聚类中心
CENTER1=center;  
load center2                               %读入第二个样区的聚类中心
CENTER2=center;  
load UAIC10-Apr8078                               %读入第三个样区的聚类中心
CENTER3=center;  
load SAriver3 %读入测试数据
SA=sarsamples;
% sa1=SA(1:5,:);
% sa2=SA(51:55,:);
% sa3=SA(101:105,:);
% sa=[sa1;sa2;sa3];
% sa=ceil(rand(1,1)*150+1-0.5);
% sa2=SA(135,:);
% sa2=SA(sa,:);
% for i=1:k
%     sa2(i,:)=SA(sa(i),:);                                    %sa1表示某个样区的样本
% end  
distance1=[];
distance2=[];
distance3=[];
d_min=[];
aa=[];bb=[];
% for i=1:15
%     distance(i,1)=abs(fitcom(CENTER1,sa(i,:)));
%     distance(i,2)=abs(fitcom(CENTER2,sa(i,:)));
%     distance(i,3)=abs(fitcom(CENTER3,sa(i,:)));
% end%计算每个测试样本到每个聚类中心的距离
for i=1:10
    sa1(1+(i-1)*10:10*i,:)=SA(45613+(i-1)*256:45622+(i-1)*256,:);         %sa1表示某个样区的样本，任取一百个训练样本
end 
for i=1:10
    sa2(1+(i-1)*10:10*i,:)=SA(38234+(i-1)*256:38243+(i-1)*256,:);         %sa1表示某个样区的样本，任取一百个训练样本
end 
for i=1:10
    sa3(1+(i-1)*10:10*i,:)=SA(6016+(i-1)*256:6025+(i-1)*256,:);         %sa1表示某个样区的样本，任取一百个训练样本
end 
for i=1:100
    distance1(i,1)=abs(fitcom(CENTER1,sa1(i,:)));
    distance1(i,2)=abs(fitcom(CENTER2,sa1(i,:)));
    distance1(i,3)=abs(fitcom(CENTER3,sa1(i,:)));
end%计算每个测试样本到每个聚类中心的距离
for i=1:100
    distance2(i,1)=abs(fitcom(CENTER1,sa2(i,:)));
    distance2(i,2)=abs(fitcom(CENTER2,sa2(i,:)));
    distance2(i,3)=abs(fitcom(CENTER3,sa2(i,:)));
end%计算每个测试样本到每个聚类中心的距离
for i=1:100
    distance3(i,1)=abs(fitcom(CENTER1,sa3(i,:)));
    distance3(i,2)=abs(fitcom(CENTER2,sa3(i,:)));
    distance3(i,3)=abs(fitcom(CENTER3,sa3(i,:)));
end%计算每个测试样本到每个聚类中心的距离
[aa1,bb1]=sort(distance1,2); 
[aa2,bb2]=sort(distance2,2); 
[aa3,bb3]=sort(distance3,2);   
for j=1:100
    disp(sprintf('sample1: %d   class1: %d   ',j,bb1(j,1)));
end
for j=1:100
    disp(sprintf('sample2: %d   class2: %d   ',j,bb2(j,1)));
end
for j=1:100
    disp(sprintf('sample3: %d   class3: %d   ',j,bb3(j,1)));
end
classify1=bb1(:,1);
classify2=bb2(:,1);
classify3=bb3(:,1);
eval(['save ' Method ' classify1 classify2 classify3  ']) ;