svm（支持向量机）程序

clear; clc; %p0=[-10:0.1:10]; %输入数据 %p0=20*rand(1,100)-10; [x1,x2]=meshgrid(-5:0.5:5,-1:0.3:5); %x1=[-5:0.05:5]; %x2=[-1:0.03:5]; o=0.1; %噪声正态分布的标准差 lC=size(x1,2); z=o*randn(1,lC); %输出时伴随的噪声 for i=1:lC; for j=1:lC; t0(lC*i-j+1)=sin(sqrt(x1(i,j)*x1(i,j)+x2(i,j)*x2(i,j)))... /sqrt(x1(i,j)*x1(i,j)+x2(i,j)*x2(i,j))+z(i); X1(lC*i-j+1)=x1(i,j); X2(lC*i-j+1)=x2(i,j); end end %LC=75; %学习样本数 %P=p0(1:LC); %T=t0(1:LC); X=[X1;X2]'; %训练用输入数据 Y=t0'; %训练用输出数据 ker='poly'; C=1.4972; loss='einsensitive'; e=0.05; global p1; p1=2; [nsv,beta,bias]=svr(X,Y,ker,C,loss,e) %x=[-10:0.1:10]; %y=sin(sqrt(x1.*x1+x2.*x2))./sqrt(x1.*x1+x2.*x2); [t1,t2]=meshgrid(-5:0.2:5); m=size(t1,2); for i=1:m; for j=1:m; T1(m*i-j+1)=t1(i,j); T2(m*i-j+1)=t2(i,j); end end tstX=[T1;T2]'; %targetY=y'; tstY=svroutput(X,tstX,ker,beta,bias); u=tstY'; for i=1:m; for j=1:m; y(i,j)=u(m*i-j+1); end end %err=svrerror(X,tstX,targetY,ker,beta,bias,loss,e) %计算预测误差 figure; %[u1,u2]=meshgrid(-5:0.2:5); %y=sin(sqrt(u1.*u1+u2.*u2))./sqrt(u1.*u1+u2.*u2); mesh(t1,t2,y) %hold on; %svrplot(X,Y,ker,beta,bias,e) %hold off;