贝叶斯分类器MATLAB经典程序.doc

function Bayes2%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for k=1:N %控制程序模拟次数N %生成二维正态分布的样本2 X N 维的矩阵 X1=mvnrnd(1 2,4 0;0 6,300); %2 X N X2=mvnrnd(5 3,5 0;0 1,200); X3=mvnrnd(4 7,2 0;0 9,500); %样本程序 %-% %测试样本 X10=mvnrnd(1 2,4 0;0 6,100); %2 X N X20=mvnrnd(5 3,5 0;0 1,100); X30=mvnrnd(4 7,2 0;0 9,100); %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;%2 w10=-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); %-% for i=1:3 for j=1:100 if i=1 g1=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; if g1=g2&g1=g3 Result(1,1)=Result(1,1)+1; elseif g2=g1&g2=g3 Result(1,2)=Result(1,2)+1;%记录误判情况 else Result(1,3)=Result(1,3)+1;%记录误判情况 end elseif i=2 g1=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; if g2=g1&g2=g3 Result(2,2)=Result(2,2)+1; elseif g1=g2&g1=g3 Result(2,1)=Result(2,1)+1; else Result(2,3)=Result(2,3)+1; end else g1=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; if g3=g1&g3=g2 Result(3,3)=Result(3,3)+1; elseif g2=g1&g2=g3 Result(3,2)=Result(3,2)+1; else Result(3,1)=Result(3,1)+1; end end end endend%画出各样本的分布情况subplot(2,1,1)plot(X1(1,:),X1(2,:),r.,LineWidth,2),hold onplot(X2(1,:),X2(2,:),go,LineWidth,2),hold onplot(X3(1,:),X3(2,:),b+,LineWidth,2),hold ontitle(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),hold onplot(X20(1,:),X20(2,:),go,LineWidth,2),hold onplot(X30(1,:),X30(2,:),b+,LineWidth,2),hold ontitle(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for i=1:length(Result) if round(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 for j=1:length(Result) if j=n Result(i,j)=fix(Result(i,j)+1; else Result(i,j)=fix(Result(i,j); end end elseif round(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 for j=1:length(Result)