【-数学建模竞赛】蠓虫分类.doc

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1、蠓虫分类解：（1）Bayes判别：求条件概率假设假设两类数据均服从二维正态分布，，则条件概率为：决策规则为;或等价地:判决函数：类似地，Bayes最小风险判别可通过给出风险后得到。x=[1.201.301.181.141.261.281.361.481.401.381.241.381.541.381.561.241.281.401.221.36;1.861.961.781.782.002.001.741.821.701.901.721.641.821.822.081.801.782.041.881.78];n1=6;n2=9;n3=5;plot(x(1,1:n1),x(2,1:n1)

2、,'o',x(1,n1+1:n1+n2),x(2,n1+1:n1+n2),'*',x(1,n1+n2+1:end),x(2,n1+n2+1:end),'r+');mm1=sum(y(1:n1))/n1;mm2=sum(y(n1+1:n1+n2))/n2;sgm1=cov(x(:,1:n1)');%=s1/(n1-1);sgm2=cov(x(:,n1+1:n1+n2)');X=x(:,n1+n2+1:end);X=X';pxw1=mvnpdf(X,m1',sgm1);pxw2=mvnpdf(X,m2',sgm2);pwx1=pxw1./(pxw1+pxw2);pwx2=pxw2./(p

3、xw1+pxw2);display('UsingBayesprincipalis:')Apf=find(pwx1>pwx2)+n1+n2,（2）Fisher判别：求投影方向w*准则函数：其中最优解：m1=mean(x(:,1:n1),2);m2=mean(x(:,n1+1:n1+n2),2);s1=(x(:,1:n1)-repmat(m1,1,n1))*(x(:,1:n1)-repmat(m1,1,n1))';s2=(x(:,n1+1:n1+n2)-repmat(m2,1,n2))*(x(:,n1+1:n1+n2)-repmat(m2,1,n2))';S=s1+s2;w=inv(S)

4、*(m1-m2);y=w'*x;mm1=sum(y(1:n1))/n1;mm2=sum(y(n1+1:n1+n2))/n2;y0=(mm1+mm2)/2;%y0=(mm1*n1+mm2*n2)/(n1+n2);dpyb=y(n1+n2+1:end);display('Usingfisherprincipalis:')Apf=find(dpyb>y0)+n1+n2,figure(2);t=1.1:0.01:1.6;kkk=-w(1)/w(2);ft=kkk*t+y0/w(2);plot(x(1,1:n1),x(2,1:n1),'o',x(1,n1+1:n1+n2),x(2,n1+1:n

5、1+n2),'*',x(1,n1+n2+1:end),x(2,n1+n2+1:end),'r+',t,ft);axis([1.1,1.6,1.6,2.1]);感知器准则及梯度下降算法：样本的增广化：样本的规范增广化：（仍记为）目标：求使梯度下降法：批处理感知器算法固定增量单样本感知器%peceptronx1=[ones(1,length(x));x];x1(:,n1+1:n1+n2)=-x1(:,n1+1:n1+n2);epsl=0.1;a=1-2*rand(3,1);k=0;whilek

6、ndendy1=a'*x1(:,1:n1+n2);ind=find(y1<=0);display('thesamplesforfirstclassusingpeceptronprincipalis:')Apf=find(a'*x1(:,n1+n2+1:end)>0)pt=-a(2)*t/a(3)-a(1)/a(3);figure(3),plot(x(1,1:n1),x(2,1:n1),'o',x(1,n1+1:n1+n2),x(2,n1+1:n1+n2),'*',x(1,n1+n2+1:end),x(2,n1+n2+1:end),'r+',t,[pt,ft]);最小平方误差准则：设其

7、中，目标：min目标函数的梯度：令%MSEb=ones(n1+n2,1);Y=x1(:,1:n1+n2);Y=Y';Yplus=(Y'*Y)^(-1)*Y';ahat=Yplus*b;mt=-ahat(2)*t/ahat(3)-ahat(1)/ahat(3);figure(4)plot(t,[pt;ft;mt],x(1,1:n1),x(2,1:n1),'o',x(1,n1+1:n1+n2),x(2,n1+1:n1+n2),'*',x(1,n1+n2+1:e