matlab 贝叶斯决策对二维二分类问题的实现

load data1;right=0;x=zeros(2,1);%假设它们都符合二元高斯分布u1=zeros(2,1);%值为1的类的均值v1=zeros(2,2);%值为1的类的方差u2=zeros(2,1);%值为2的类的均值v2=zeros(2,2);%值为2的类的方差sum1=0;sum2=0;pior1=0;posterior1=0;likehood1=0;pior2=0;posterior2=0;likehood2=0;for i=1:1:2500%求先验    if(data(3,i)==1)   sum1=sum1+1;else       sum2=sum2+1;endendpior1=sum1/(sum1+sum2);pior2=sum2/(sum1+sum2);for m=2501:1:3000  %测试循环for i=1:1:2500%求均值    if(data(3,i)==1)   u1(1,1)=u1(1,1)+data(1,i);   u1(2,1)=u1(2,1)+data(2,i);else   u2(1,1)=u2(1,1)+data(1,i);   u2(2,1)=u2(2,1)+data(2,i);endendu1=u1/2500;u2=u2/2500;for i=1:1:2500%求方差    if(data(3,i)==1)   x(1,1)=data(1,i);   x(2,1)=data(2,i);   v1=v1+(x-u1)*(x-u1)';else   x(1,1)=data(1,i);   x(2,1)=data(2,i);   v2=v2+(x-u2)*(x-u2)';endendv1=v1/2500;v2=v2/2500;%求似然x(1,1)=data(1,m);x(2,1)=data(2,m);likehood1=1/(det(v1)^0.5)*exp(-0.5*(x-u1)'*(v1^-1)*(x-u1));%相同的项不乘了likehood2=1/(det(v2)^0.5)*exp(-0.5*(x-u2)'*(v2^-1)*(x-u2));求后验posterior1=pior1*likehood1;posterior2=pior2*likehood2;if(((posterior1>posterior2)&&(data(3,m)==1))||((posterior1<posterior2)&&(data(3,m)==-1)))   right=right+1;end   end 

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