www.pudn.com > AnnInMat.rar > ToolBoxSam.m

clc 
close all 
clear all 
 
InDim=2;%样本输入维数 
OutDim=3;% 样本输出维数 
figure 
title('训练样本');echo off 
axis([-2,2,-2,2]);axis on;grid 
xlabel('SamIn x'); 
ylabel('SamIn y'); 
line([-1 1],[1 1]) 
line([1 -1],[1 0]) 
line([-1 -1],[0 1]) 
line([-1 1],[-0.5 -0.5]) 
line([-1 1],[-1.5 -1.5]) 
line([1 1],[-0.5 -1.5]) 
line([-1 -1],[-0.5 -1.5]) 
hold on 
SamNum=200;%训练样本数 
%     rand('state',sum(100*clock)) 
SamIn=(rand(2,SamNum)-0.5)*4;% 随机产生200个[-2,2]区间样本输入 
SamOut=[]; 
for i=1:SamNum 
    Sam=SamIn(:,i); 
    x=Sam(1,1); 
    y=Sam(2,1); 
    if((x>-1)&(x<1))==1 
        if ((y>x/2+1/2)&(y<1))==1 
            plot(x,y,'r+') 
            class=[0 1 0]'; 
        elseif((y<-0.5)&(y>-1.5))==1 
            plot(x,y,'rs') 
            class=[0 0 1]'; 
        else 
            plot(x,y,'ro') 
            class=[1 0 0]'; 
        end 
    else 
        plot(x,y,'ro') 
        class=[1 0 0]'; 
    end 
    SamOut=[SamOut class];                  %得到样本对应的类别属性 
end 
HiddenUnitNum=10;%隐节点数 
MaxEpochs=10000;%最大训练次数 
lr=0.1;%学习率 
E0=0.01;%目标误差 
 
net=newff([-2 2;-2 2],[HiddenUnitNum OutDim],{'logsig','logsig'},'trainbr');%此处采用贝叶斯正则化法。 
net=init(net); 
net.trainParam.epochs=MaxEpochs; 
net.trainParam.goal=E0; 
net.trainParam.lr=lr; 
[net,tr,NetOut,NetError]=train(net,SamIn,SamOut); 
 
% [net,tr]=newrb(SamIn,SamOut,0.01,1.0,20,1); 
 
% net=newlvq([-2 2;-2 2],8,[13/16 1/16 1/8],0.01); 
% net=init(net); 
% net.trainParam.show=1; 
% net.trainParam.epochs=20; 
% net=train(net,SamIn,SamOut); 
 
% net=newelm([-2 2;-2 2],[8 3]); 
% net=init(net); 
% net.trainParam.show=100; 
% net.trainParam.epochs=1000; 
% net=train(net,SamIn,SamOut); 
 
% net=newpnn(SamIn,SamOut,0.1); 
 
TestSamNum=500;% 测试样本数 
TestSamIn=(rand(2,TestSamNum)-0.5)*4; 
% TestSamNum=SamNum;% 测试样本数 
% TestSamIn=SamIn; 
TestNNOut = sim(net,TestSamIn); 
[val nnclass]=max(TestNNOut); 
 
figure 
title('测试结果');echo off 
axis([-2,2,-2,2]);axis on 
grid 
xlabel('TestSamIn x'); 
ylabel('TestSamIn y'); 
line([-1 1],[1 1]); 
line([1 -1],[1 0]); 
line([-1 -1],[0 1]); 
line([-1 1],[-0.5 -0.5]); 
line([-1 1],[-1.5 -1.5]); 
line([1 1],[-0.5 -1.5]); 
line([-1 -1],[-0.5 -1.5]); 
hold on 
 
TestSamOut = []; 
for i = 1:TestSamNum 
    x = TestSamIn(1,i); 
    y = TestSamIn(2,i); 
    if nnclass(i)==1 
        plot(x,y,'ro'); 
    elseif nnclass(i)==2 
        plot(x,y,'r+'); 
    else 
        plot(x,y,'rs'); 
    end 
    if((x>-1)&(x<1))==1 
        if ((y>x/2+1/2)&(y<1))==1 
            class = 2; 
        elseif((y<-0.5)&(y>-1.5))==1 
            class = 3; 
        else 
            class = 1; 
        end 
    else 
        class = 1; 
    end 
    TestSamOut = [TestSamOut class]; 
end 
 
Result = ~abs(nnclass-TestSamOut);     % 正确分类显示为1 
Percent = sum(Result)/length(Result)   % 正确分类率