1、-作者xxxx-日期xxxx貝葉斯分類器MATLAB經典程序【精品文檔】function Bayes2%為了提高實驗樣本測試的精度，故采用多次模擬求平均值的方法N=input('實驗模擬次數 N(N最好為奇數) ');Result(1:3,1:3)=0;      %判別矩陣的初始化for k=1:N             %控制程序模擬次數N    %生成二維正態分布的樣本2

2、X N 維的矩陣     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)'   %樣本程序    %-%    %測試樣本    X10=mvnrnd(1 2,4 0;0 6,100)'&#

3、160;  %2 X N    X20=mvnrnd(5 3,5 0;0 1,100)'    X30=mvnrnd(4 7,2 0;0 9,100)'      %先驗概率    P(1)=length(X1)/(length(X1)+length(X2)+length(X3);    P(2)=length(X2)/(length(X1)+length(X2)+length(X3); &

4、#160;  P(3)=length(X3)/(length(X1)+length(X2)+length(X3);    %計算相關量  cov(X)：協方差矩陣 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

5、9;)/length(X2)'    Ave3=(sum(X3')/length(X3)'%計算平均值(2維列向量)    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

6、/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              

7、60;                              for j=1:100                  

8、60;                        if i=1                g1=X10(:,j)'*W1*X10(:,j)+w1'*X10(:,j)+w10;  

9、;                g2=X10(:,j)'*W2*X10(:,j)+w2'*X10(:,j)+w20;                g3=X10(:,j)'*W3*X10(:,j)+w3'*X10(:,j)+w30;   

10、;             if g1>=g2&g1>=g3                         Result(1,1)=Result(1,1)+1;     

11、;           elseif g2>=g1&g2>=g3                    Result(1,2)=Result(1,2)+1;%記錄誤判情況          

12、      else                    Result(1,3)=Result(1,3)+1;%記錄誤判情況                end    

13、;        elseif i=2                g1=X20(:,j)'*W1*X20(:,j)+w1'*X20(:,j)+w10;                g2=X20(:,j)

14、9;*W2*X20(:,j)+w2'*X20(:,j)+w20;                g3=X20(:,j)'*W3*X20(:,j)+w3'*X20(:,j)+w30;                if g2>=g1&g2>=g3 

15、                   Result(2,2)=Result(2,2)+1;                elseif g1>=g2&g1>=g3       &#

16、160;            Result(2,1)=Result(2,1)+1;                else                  

17、  Result(2,3)=Result(2,3)+1;                end            else                g1=X30(:,j)

18、'*W1*X30(:,j)+w1'*X30(:,j)+w10;                g2=X30(:,j)'*W2*X30(:,j)+w2'*X30(:,j)+w20;                g3=X30(:,j)'*W3*X30(:,j)+

19、w3'*X30(:,j)+w30;                if g3>=g1&g3>=g2                    Result(3,3)=Result(3,3)+1;   

20、60;            elseif g2>=g1&g2>=g3                    Result(3,2)=Result(3,2)+1;          

21、;      else                    Result(3,1)=Result(3,1)+1;                end     

22、;       end        end    endend%畫出各樣本的分布情況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+',&

23、#39;LineWidth',2),hold ontitle('訓練樣本分布情況')legend('訓練樣本1','訓練樣本2','訓練樣本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+','Line

24、Width',2),hold ontitle('測試樣本分布情況')legend('測試樣本1','測試樣本2','測試樣本3')%由于多次循環后存在小數，根據實際情況判別矩陣須取整%如果N為偶數，可能出現小數為0.5的情況，此時將無法更加準確判斷矩陣Result=Result/N     %判別矩陣，反映Bayes的判別效果for i=1:length(Result)    if round(sum(Result(i,:)-fix(Result(i,:

25、)=1        m,n=find(max(Result(i,:)-fix(Result(i,:)=(Result(i,:)-fix(Result(i,:);        n=min(n);%存在小數點相同的情況隨即選取一個        for j=1:length(Result)        &#

26、160;   if j=n                Result(i,j)=fix(Result(i,j)+1;            else              &

27、#160; 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,:);      &