bayes程序MATLAB

%为了提高实验样本测试的精度，故采用多次模拟求平均值的方法N=input('实验模拟次数N(N最好为奇数)＝');Result(1:3,1:3)=0;%判别矩阵的初始化fork=1:N%控制程序模拟次数N%生成二维正态分布的样本2XN维的矩阵X1=mvnrnd([12],[40;06],300)';%2XNX2=mvnrnd([53],[50;01],200)';X3=mvnrnd([47],[20;09],500)';%样本程序%---------------------------------------------------%%测试样本X10=mvnrnd([12],[40;06],100)';%2XNX20=mvnrnd([53],[50;01],100)';X30=mvnrnd([47],[20;09],100)';%先验概率P(1)=length(X1)/(length(X1)+length(X2)+length(X3));P(2)=length(X2)/(length(X1)+length(X2)+length(X3));P(3)=length(X3)/(length(X1)+length(X2)+length(X3));%计算相关量cov(X)：协方差矩阵Ave:均值%--------------------------------------------------------%W1=-1/2*inv(cov(X1'));W2=-1/2*inv(cov(X2'));W3=-1/2*inv(cov(X3'));%Ave1=(sum(X1')/length(X1))';Ave2=(sum(X2')/length(X2))';Ave3=(sum(X3')/length(X3))';%计算平均值(2维列向量)w1=inv(cov(X1'))*Ave1;w2=inv(cov(X2'))*Ave2;w3=inv(cov(X3'))*Ave3;%2w10=-1/2*Ave1'*inv(cov(X1'))*Ave1-1/2*log(det(cov(X1')))+log(P(1));w20=-1/2*Ave2'*inv(cov(X2'))*Ave2-1/2*log(det(cov(X2')))+log(P(2));w30=-1/2*Ave3'*inv(cov(X3'))*Ave3-1/2*log(det(cov(X3')))+log(P(3));%-----------------------------------------------------------%fori=1:3forj=1:100ifi==1g1=X10(:,j)'*W1*X10(:,j)+w1'*X10(:,j)+w10;g2=X10(:,j)'*W2*X10(:,j)+w2'*X10(:,j)+w20;g3=X10(:,j)'*W3*X10(:,j)+w3'*X10(:,j)+w30;ifg1=g2&g1=g3Result(1,1)=Result(1,1)+1;elseifg2=g1&g2=g3Result(1,2)=Result(1,2)+1;%记录误判情况elseResult(1,3)=Result(1,3)+1;%记录误判情况endelseifi==2g1=X20(:,j)'*W1*X20(:,j)+w1'*X20(:,j)+w10;g2=X20(:,j)'*W2*X20(:,j)+w2'*X20(:,j)+w20;g3=X20(:,j)'*W3*X20(:,j)+w3'*X20(:,j)+w30;ifg2=g1&g2=g3Result(2,2)=Result(2,2)+1;elseifg1=g2&g1=g3Result(2,1)=Result(2,1)+1;elseResult(2,3)=Result(2,3)+1;endelseg1=X30(:,j)'*W1*X30(:,j)+w1'*X30(:,j)+w10;g2=X30(:,j)'*W2*X30(:,j)+w2'*X30(:,j)+w20;g3=X30(:,j)'*W3*X30(:,j)+w3'*X30(:,j)+w30;ifg3=g1&g3=g2Result(3,3)=Result(3,3)+1;elseifg2=g1&g2=g3Result(3,2)=Result(3,2)+1;elseResult(3,1)=Result(3,1)+1;endendendendend%画出各样本的分布情况subplot(2,1,1)plot(X1(1,:),X1(2,:),'r.','LineWidth',2),holdonplot(X2(1,:),X2(2,:),'go','LineWidth',2),holdonplot(X3(1,:),X3(2,:),'b+','LineWidth',2),holdontitle('训练样本分布情况')legend('训练样本1','训练样本2','训练样本3')subplot(2,1,2)plot(X10(1,:),X10(2,:),'r.','LineWidth',2),holdonplot(X20(1,:),X20(2,:),'go','LineWidth',2),holdonplot(X30(1,:),X30(2,:),'b+','LineWidth',2),holdontitle('测试样本分布情况')legend('测试样本1','测试样本2','测试样本3')%由于多次循环后存在小数，根据实际情况判别矩阵须取整%如果N为偶数，可能出现小数为0.5的情况，此时将无法更加准确判断矩阵Result=Result/N%判别矩阵，反映Bayes的判别效果fori=1:length(Result)ifround(sum(Result(i,:)-fix(Result(i,:))))==1[m,n]=find(max(Result(i,:)-fix(Result(i,:)))==(Result(i,:)-fix(Result(i,:))));n=min(n);%存在小数点相同的情况随即选取一个forj=1:length(Result)ifj==nResult(i,j)=fix(Result(i,j))+1;elseResult(i,j)=fix(Result(i,j));endendelseifround(sum(Result(i,:)-fix(Result(i,:))))==2[m,n1]=find(max(Result(i,:)-fix(Result(i,:)))==(Result(i,:)-fix(Result(i,:))));[m,n2]=find(min(Result(i,:)-fix(Result(i,:)))==(Result(i,:)-fix(Result(i,:))));n1=min(n1);n2=min(n2);%如果有存在小数点相同的情况,随即选取一个forj=1:length(Result)ifj==n1Result(i,j)=fix(Result(i,j))+1;elseifj==n2Result(i,j)=fix(Result(i,j));elseResult(i,j)=fix(Result(i,j))+1;endendelsecontinue,endendResult,