主程序如下：

clear

p_train=[0.7 0.7 0.7 1 1 1 1 0.7 0.7 1;

0.7 0.5 0.7 0.5 0.5 0.7 0.5 0.5 0.7 0.7;

0.5 0.7 0.7 0.7 0.7 0.7 0.5 0.7 0.5 0.7;

0.5 0.3 0.5 0.3 0.5 0.3 0.5 0.7 0.5 0.5;

0.3 0.5 0.7 0.5 0.7 0.3 0.3 0.5 0.3 0.5;

1 1 1 0.7 1 1 0.7 0.7 1 1;

0.7 0.7 0.7 0.7 0.7 0.7 1 0.5 0.7 1;

0.5 0.5 0.7 0.7 0.5 1   0.7 0.5 0.7 0.7;

1 0.7 0.7 0.7 1 0.7 0.7 1 0.7 1;

0.5 0.5 0.7 0.7 0.5 0.5 0.5 0.5 0.5 0.7];

t_train=[0.3 0.7 0.5 0.7 0.1 0.3 0.5 0.5 0.7 0.5;

0.7 0.5 1 0.7 1 1 0.7 0.7 1 1;

1 0.7 0.7 0.7 0.7 1 0.7 0.5 0.7 1;

0.7 0.5 0.7 0.7 0.5 0.7 0.5 0.7 0.7 0.7

];

p_test=[0.7 0.5;

0.5 0.7;

0.7 0.7;

0.7 0.5;

0.3 0.5;

0.7 1;

0.7 0.7;

0.7 0.5;

1 0.5;

0.7 0.5

];

t_test=[0.3 0.3;

0.7 1;

1 0.5;

0.5 0.5];

%p_train=(rand(4,50));

%t_train=rand(1,50);

%p_test=(rand(4,5));

%t_test=rand(1,5);

% [pn,minp,maxp,tn,mint,maxt] = premnmx(p,t)

[pn_train,minp,maxp,tn_train,mint,maxt] = premnmx(p_train,t_train);

%  [pn,minp,maxp] = premnmx(p)

%测试集

[pn_test] = tramnmx(p_test,minp,maxp);

[tn_test] = tramnmx(t_test,mint,maxt);

%创建训练模型

%寻找最佳c参数、g参数

[c,g]=meshgrid(-10:0.5:10,-10:0.5:10);

[m,n]=size(c);

cg=zeros(m,n);

eps=10^(-4);

v=5;

bestc=0;

bestg=0;

error=Inf;

for i=1:m

for j=1:n

cmd=['-v',num2str(v),'-c',num2str(2^c(i,j)),'-g',num2str(2^g(i,j)),'-s 3 -p 0.1'];

cg(i,j)=svmtrain(tn_train,pn_train,cmd);

if cg(i,j)

error=cg(i,j);

bestc=2^c(i,j);

bestg=2^g(i,j);

end

if abs(cg(i,j)-error)2^c(i,j)

error=cg(i,j);

bestc=2^c(i,j);

bestg=2^g(i,j);

end

end

end

cmd=['-c',num2str(bestc),'-g',num2str(bestg),'-s 3 -p 0.01'];

model=svmtrain(tn_train,pn_train,cmd);

[Predict_1,error_1]=svmpredict(tn_train,pn_train,model);

[Predict_2,error_2]=svmpredict(tn_test,pn_test,model);

predict_1=premnmx('reverse',predict_1,outputps);

postmnmx

predict_2=premnmx('reverse',predict_2,outputps);

result_1=[t_train predict_1];

result_2=[t_test predict_2];

%绘图

figure(1)

plot(1:length(t_train),t_train,'r-*',1:length(t_train),predict_1,'b:o')

grid on

legend('真实值','预测值')

xlabel('样本编号')

ylabel('风险强度')

string_1={'训练集预测结果对比';['mse='num2str(error_1(2))'R^2='num2str(error_1(3))]};

title(string_1)

figure(2)

plot(1:length(t_test),t_test,'r-*',1:length(t_test),predict_2,'b:o')

grid on

legend('真实值','预测值')

xlabel('样本编号')

ylabel('风险强度')

string_2={'测试集预测结果对比'；['mse='num2str(error_2(2))'R^2'=num2str(error_2(3))};

title(string_2)

运行结果

Length of label vector does not match # of instances.

??? Subscripted assignment dimension mismatch.

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