MATLAB_人工免疫系统中的反向选择算法_NSA_异常检测方面

%% 该算法是NSA的编程实例，其中：sf是自己模式串；det是检测器集合； %% n是解测器检测器集合的个数； %% ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ %% 下面是检测器产生阶段 %% rs=0.04; rab=0.05; self=[0.2265 0.1423; 0.2871 0.2186; 0.273 0.1977; 0.265 0.2008; 0.1426 0.0567; 0.17 0.0797; 0.1772 0.087; 0.2773 0.2113; 0.2874 0.2288; 0.3045 0.2572]; d=rand(250,2); %for s=1:250 %plot(det(s,1),det(s,2),'b+');hold on; %end for n=1:250 for i=1:250 for j=1:10 affity(j)=sqrt((d(i,1)-self(j,1)).^2+(d(i,2)-self(j,2)).^2); if affity(j)<=(rab+rs); d(i,:)=rand(1,2); else d(i,:)=d(i,:); end end end end d; for i=1:10 plot(self(i,1),self(i,2),'bo');hold on; end for i=1:250 plot(d(i,1),d(i,2),'b+');hold on; end %************************** the end ******************** %% 下面是测试阶段 %% test=[0.3245 0.22; 0.2866 0.2279; 0.2308 0.1464; 0.2544 0.18; 0.151 0.068; 0.3179 0.2077; 0.3042 0.1989; 0.258 0.3477; 0.3327 0.2501; 0.214 0.139; 0.4524 0.31; 0.4401 0.30; 0.4782 0.36; 0.4633 0.357; 0.5687 0.898; 0.7245 0.987; 0.6333 0.96; 0.6929 0.983; 0.31 0.454; 0.344 0.568; 0.376 0.67; 0.284 0.563; 0.5501 0.1512; 0.2250 0.7821; 0.6543 0.2945]; for j=1:25 plot(test(j,1),test(j,2),'r*'); hold on; % 标识当前的测试样本 for i=1:250 dir=norm(test(j,:)-d(i,:)); d_test(i,1)=dir; d_label(i,1)=i; % 标识当前是那个抗体和那个抗原在计算亲和力 end %d(d_label(i,1) for i=1:250 if d_test(i,1)<=rab plot(d(d_label(i,1),1),d(d_label(i,1),2),'g^'); hold on; end end min_dir=min(d_test); %for i=1:245 % f=or(d_test(i,1)<=rab,d_test(i+1,1)<=rab); % f=and(f,1); %end %f; %if f==1 if min_dir<=rab disp('This data is detected') else for p=1:10 dir2=norm(test(j,:)-self(p,:)); test_self(p,1)=dir2; self_label(p,1)=p; end for p=1:10 if test_self(p,1)<=rs plot(self(self_label(p,1),1),self(self_label(p,1),2),'g^'); hold on; end end %for p=1:9 %ff=or(test_self(p,1)<=rs,test_self(p+1,1)<=rs); %ff=and(ff,1); %end min_dir2=min(test_self); %if ff==1 if min_dir2<=rs disp('This data is self_data') else disp('This data is not detected and not a selfdata') end end end %% ^^^^^^^^^^^^^^^^^ The end ^^^^^^^^^^^^^^^^^^^^ %%