基于matlab实现的KNN算法机器学习项目源码+数据集+项目说明+代码注释.zip

k=[1,3,5,10,15,20,30,40,50,60,70,80,90,100]; i=1; p=[]; label=[]; correct=[]; false0=[]; false1=[]; testnumber=[]; testnumber=[2000,3000,5000,8000,10000]; for x=1:5 %maliciousdata=0; testnum=testnumber(x); %用来测试的数据量 % while(i<testnum) % if test.labels(i,:)==1 % maliciousdata=maliciousdata+1; % end % i=i+1; % end for i=1:14 correct(i)=0; false0(i)=0; false1(i)=0; end for j=1:14 i=1; while (i<=testnum) if dist_benign(ceil(k(j)/2),i) < dist_malicious(ceil(k(j)/2),i) label(i,:)=0; else label(i,:)=1; end %判断结果与原值的差的绝对值 p(i,:)=abs(label(i,:)-test.labels(i,:)); if p(i,:)==0 %判断结果与原值相同 correct(j)=correct(j)+1; else if test.labels(i,:)==0 && label(i,:)==1 %原值为良性，判断成恶性 false0(j)=false0(j)+1; else %原值为恶性，判断成良性 false1(j)=false1(j)+1; end end i=i+1; end plot(k(j),correct(j)/testnum,'*r'); hold on; if j>1 if x==1 plot([k(j-1),k(j)],[correct(j-1)/testnum,correct(j)/testnum],'Color','g'); hold on; end if x==2 plot([k(j-1),k(j)],[correct(j-1)/testnum,correct(j)/testnum],'Color','c'); hold on; end if x==3 plot([k(j-1),k(j)],[correct(j-1)/testnum,correct(j)/testnum],'Color','b'); hold on; end if x==4 plot([k(j-1),k(j)],[correct(j-1)/testnum,correct(j)/testnum],'Color','m'); hold on; end if x==5 plot([k(j-1),k(j)],[correct(j-1)/testnum,correct(j)/testnum],'Color','r'); hold on; end end % xlswrite(['k_',num2str(k(j)),'.xlsx'],test.labels,'sheet1','1A'); % xlswrite(['k_',num2str(k(j)),'.xlsx'],label,'sheet1','1B'); % xlswrite(['k_',num2str(k(j)),'.xlsx'],p,'sheet1','1C'); % % xlswrite(['k_',num2str(testnum-1),'.xlsx'],k(j),'sheet2',[num2str(j),'A']); % xlswrite(['k_',num2str(testnum-1),'.xlsx'],correct(j)/testnum,'sheet2',[num2str(j),'B']); % xlswrite(['k_',num2str(testnum-1),'.xlsx'],false0(j)/testnum,'sheet2',[num2str(j),'C']); % xlswrite(['k_',num2str(testnum-1),'.xlsx'],false1(j)/testnum,'sheet2',[num2str(j),'D']); end end legend('1000','2000','3000','4000','5000'); ylabel('正确率'); xlabel('k值'); title('正确率随K值的变化情况'); axis([0,100,0.7,1.0]);