模式识别中K均值与最近邻法matlab实现.doc

模式识别算法实现 K近邻法&最近邻法一 程序1. 用Iris数据集验证的程序（1）用Iris数据集验证的M文件：load iris.mat;A=randperm(50);B=randperm(50);C=randperm(50);a1=A(1,1:25);a2=A(1,26:50);a=Iris(a1,1:4);%随机取第一类数据的25组aa=Iris(a2,1:4);b1=50*ones(1,25)+B(1,1:25);b2=50*ones(1,25)+B(1,26:50);b=Iris(b1,1:4);%随机取第二类数据的25组bb=Iris(b2,1:4);c1=100*ones(1,25)+C(1,1:25);c2=100*ones(1,25)+C(1,26:50);c=Iris(c1,1:4);%随机取第三类数据的25组cc=Iris(c2,1:4);train_sample=cat(1,a,b,c);%拼接成包含75组数据的样本集test_sample=cat(1,aa,bb,cc);k=7;%7近邻法cha=zeros(1,75);sum1=0;sum2=0;sum3=0;sum=0;i,j=size(train_sample);%i=75,j=4u,v=size(test_sample);%u=75,v=4for x=1:u for y=1:i result=sqrt(test_sample(x,1)-train_sample(y,1)2+(test_sample(x,2)-train_sample(y,2)2+(test_sample(x,3)-train_sample(y,3)2+(test_sample(x,4)-train_sample(y,4)2); cha(1,y)=result; end; z,Ind=sort(cha);%Ind用来存储排序前在cha中的下标 m1=0; m2=0; m3=0; for n=1:k if Ind(1,n)25&Ind(1,n)=m2&m1=m3 m=1; elseif m2=m1&m2=m3 m=2; elseif m3=m1&m3=m2 m=3; end if (x25&x50&x=75&m=3) sum3=sum3+1; end if (x25&x50&x=75&m=3) sum=sum+1; endenddisp(sprintf(第一类分类正确率为%4.2f,sum1/25);disp(sprintf(第二类分类正确率为%4.2f,sum2/25);disp(sprintf(第三类分类正确率为%4.2f,sum3/25);disp(sprintf(总分类正确率为%4.2f,sum/75);（2）验证结果：a) K近邻法：K=7 实验结果表1.类别次数第一类第二类第三类总识别率11.000.961.000.9921.000.920.960.9631.000.840.960.9341.000.920.920.9551.000.880.960.9561.000.961.000.9971.000.960.960.9781.000.960.920.9691.000.961.000.99101.000.920.880.93总识别率1.000.920.960.96b) 最近邻法 （即令K=1） 实验结果表2.类别次数第一类第二类第三类总识别率11.000.880.960.9521.000.960.800.9231.000.960.920.9641.000.920.880.9351.000.960.980.9561.000.960.960.9771.000.960.800.9281.001.001.001.0091.000.880.960.95101.000.960.920.96总识别率1.000.940.920.95(3) 数据分析第一个表格是用K(k=7)近邻法得到的识别率，第二个表格是用最近邻法得到的识别率，可以看出对于Iris数据，两种方法的识别率都达到了95%左右。2. 用Wine数据集验证的程序（1）用Wine数据集验证的M文件：load wine.mat;A=randperm(59);B=randperm(71);C=randperm(48);a1=A(1,1:30);a2=A(1,31:59);a=wine(a1,1:14);aa=wine(a2,1:14);b1=59*ones(1,36)+B(1,1:36);b2=59*ones(1,35)+B(1,37:71);b=wine(b1,1:14);bb=wine(b2,1:14);c1=130*ones(1,24)+C(1,1:24);c2=130*ones(1,24)+C(1,25:48);c=wine(c1,1:14);cc=wine(c2,1:14);train_sample=cat(1,a,b,c);%拼接成样本集test_sample=cat(1,aa,bb,cc);k=7;%7近邻法cha=zeros(1,90);sum1=0;sum2=0;sum3=0;sum4=0;sum=0;i,j=size(train_sample);u,v=size(test_sample);for x=1:u for y=1:i result=sqrt(test_sample(x,2)-train_sample(y,2)2+(test_sample(x,3)-train_sample(y,3)2+(test_sample(x,4)-train_sample(y,4)2+(test_sample(x,5)-train_sample(y,5)2+(test_sample(x,6)-train_sample(y,6)2+(test_sample(x,7)-train_sample(y,7)2+(test_sample(x,8)-train_sample(y,8)2+(test_sample(x,9)-train_sample(y,9)2+(test_sample(x,10)-train_sample(y,10)2+(test_sample(x,11)-train_sample(y,11)2+(test_sample(x,12)-train_sample(y,12)2+(test_sample(x,13)-train_sample(y,13)2+(test_sample(x,14)-train_sample(y,14)2); cha(1,y)=result; end; z,Ind=sort(cha);%Ind用来存储排序前在cha中的下标 m1=0; m2=0; m3=0; for n=1:k if Ind(1,n)30&Ind(1,n)=m2&m1=m3 m=1; elseif m2=m1&m2=m3 m=2; elseif m3=m1&m3=m2 m=3; end if (x29&x64&x=88&m=3) sum3=sum3+1; end if (x29&x64&x=88&m=3) sum=sum+1; endenddisp(sprintf(第一类分类正确率为%4.2f,sum1/30);disp(sprintf(第二类分类正确率为%4.2f,sum2/36);disp(sprintf(第三类分类正确率为%4.2f,sum3/24);disp(sprintf(总分类正确率为%4.2f,sum/90);（2） 验证结果：a).K近邻法：K=7 实验结果表1.类别次数第一类第二类第三类总识别率10.900.690.380.6820.970.690.420.7130.900.670.500.7040.770.560.670.6650.870.610.460.6660.700.440.670.5970.970.720.540.7680.800.670.540.6890.930.580.580.70100.870.500.790.70总识别率0.870.610.560.68b).最近邻法 （即令K=1） 实验结果表2.类别次数第一类第二类第三类总识别率10.770.750.670.7320.800.670.630.7030.830.670.630.7140.770.640.630.6850.880.640.580.6860.830.690.500.6970.800.640.580.6880.800.780.540.7290.870.640.670.72100.700.670.420.61总识别率0.810.680.590.69(3)数据分析这两个是对Wine数据的识别率，第一个表格是用K(k=7)近邻法得到的识别率，