mypnn.m

matlab写的神经网络的几个演示程序

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