www.pudn.com > AnnInMat.rar > MyPNN.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(InDim,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 
sigma = 0.1;%高斯扩展系数 
Dim = InDim + 1; 
SamInEx = [SamIn' ones(SamNum,1)]'; 
%建立网络权值 
W = SamInEx ./ repmat(sqrt(sum(SamInEx.^2)),Dim,1); 
 
%样本测试 
TestSamNum = 500;% 测试样本数 
TestSamIn = (rand(InDim,TestSamNum)-0.5)*4; 
TestData = [TestSamIn; ones(1, TestSamNum)]; 
TestData = TestData ./ repmat(sqrt(sum(TestData.^2)),Dim,1); 
 
Net = W' * TestData; 
 
TestNNOut = zeros(OutDim,TestSamNum); 
for i = 1:OutDim 
    Temp = SamOut(i,:)' * ones(1,TestSamNum); 
    TestNNOut(i,:) = sum(exp((Net-1)/sigma^2) .* Temp); 
end 
 
[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