iris2.m

matlab写的自己设计的自适应网络做iris聚类分析并分类的实验

clc
clear

p=[
5.1,3.5,1.4,0.2;
4.9,3.0,1.4,0.2;
4.7,3.2,1.3,0.2;
4.6,3.1,1.5,0.2;
5.0,3.6,1.4,0.2;
5.4,3.9,1.7,0.4;
4.6,3.4,1.4,0.3;
5.0,3.4,1.5,0.2;
4.4,2.9,1.4,0.2;
4.9,3.1,1.5,0.1;
5.4,3.7,1.5,0.2;
4.8,3.4,1.6,0.2;
4.8,3.0,1.4,0.1;
4.3,3.0,1.1,0.1;
5.8,4.0,1.2,0.2;
5.7,4.4,1.5,0.4;
5.4,3.9,1.3,0.4;
5.1,3.5,1.4,0.3;
5.7,3.8,1.7,0.3;
5.1,3.8,1.5,0.3;
5.4,3.4,1.7,0.2;
5.1,3.7,1.5,0.4;
4.6,3.6,1.0,0.2;
5.1,3.3,1.7,0.5;
4.8,3.4,1.9,0.2;
5.0,3.0,1.6,0.2;
5.0,3.4,1.6,0.4;
5.2,3.5,1.5,0.2;
5.2,3.4,1.4,0.2;
4.7,3.2,1.6,0.2;
4.8,3.1,1.6,0.2;
5.4,3.4,1.5,0.4;
5.2,4.1,1.5,0.1;
5.5,4.2,1.4,0.2;
4.9,3.1,1.5,0.2;
5.0,3.2,1.2,0.2;
5.5,3.5,1.3,0.2;
4.9,3.6,1.4,0.1;
4.4,3.0,1.3,0.2;
5.1,3.4,1.5,0.2;
5.0,3.5,1.3,0.3;
4.5,2.3,1.3,0.3;
4.4,3.2,1.3,0.2;
5.0,3.5,1.6,0.6;
5.1,3.8,1.9,0.4;
4.8,3.0,1.4,0.3;
5.1,3.8,1.6,0.2;
4.6,3.2,1.4,0.2;
5.3,3.7,1.5,0.2;
5.0,3.3,1.4,0.2;
7.0,3.2,4.7,1.4;
6.4,3.2,4.5,1.5;
6.9,3.1,4.9,1.5;
5.5,2.3,4.0,1.3;
6.5,2.8,4.6,1.5;
5.7,2.8,4.5,1.3;
6.3,3.3,4.7,1.6;
4.9,2.4,3.3,1.0;
6.6,2.9,4.6,1.3;
5.2,2.7,3.9,1.4;
5.0,2.0,3.5,1.0;
5.9,3.0,4.2,1.5;
6.0,2.2,4.0,1.0;
6.1,2.9,4.7,1.4;
5.6,2.9,3.6,1.3;
6.7,3.1,4.4,1.4;
5.6,3.0,4.5,1.5;
5.8,2.7,4.1,1.0;
6.2,2.2,4.5,1.5;
5.6,2.5,3.9,1.1;
5.9,3.2,4.8,1.8;
6.1,2.8,4.0,1.3;
6.3,2.5,4.9,1.5;
6.1,2.8,4.7,1.2;
6.4,2.9,4.3,1.3;
6.6,3.0,4.4,1.4;
6.8,2.8,4.8,1.4;
6.7,3.0,5.0,1.7;
6.0,2.9,4.5,1.5;
5.7,2.6,3.5,1.0;
5.5,2.4,3.8,1.1;
5.5,2.4,3.7,1.0;
5.8,2.7,3.9,1.2;
6.0,2.7,5.1,1.6;
5.4,3.0,4.5,1.5;
6.0,3.4,4.5,1.6;
6.7,3.1,4.7,1.5;
6.3,2.3,4.4,1.3;
5.6,3.0,4.1,1.3;
5.5,2.5,4.0,1.3;
5.5,2.6,4.4,1.2;
6.1,3.0,4.6,1.4;
5.8,2.6,4.0,1.2;
5.0,2.3,3.3,1.0;
5.6,2.7,4.2,1.3;
5.7,3.0,4.2,1.2;
5.7,2.9,4.2,1.3;
6.2,2.9,4.3,1.3;
5.1,2.5,3.0,1.1;
5.7,2.8,4.1,1.3;
6.3,3.3,6.0,2.5;
5.8,2.7,5.1,1.9;
7.1,3.0,5.9,2.1;
6.3,2.9,5.6,1.8;
6.5,3.0,5.8,2.2;
7.6,3.0,6.6,2.1;
4.9,2.5,4.5,1.7;
7.3,2.9,6.3,1.8;
6.7,2.5,5.8,1.8;
7.2,3.6,6.1,2.5;
6.5,3.2,5.1,2.0;
6.4,2.7,5.3,1.9;
6.8,3.0,5.5,2.1;
5.7,2.5,5.0,2.0;
5.8,2.8,5.1,2.4;
6.4,3.2,5.3,2.3;
6.5,3.0,5.5,1.8;
7.7,3.8,6.7,2.2;
7.7,2.6,6.9,2.3;
6.0,2.2,5.0,1.5;
6.9,3.2,5.7,2.3;
5.6,2.8,4.9,2.0;
7.7,2.8,6.7,2.0;
6.3,2.7,4.9,1.8;
6.7,3.3,5.7,2.1;
7.2,3.2,6.0,1.8;
6.2,2.8,4.8,1.8;
6.1,3.0,4.9,1.8;
6.4,2.8,5.6,2.1;
7.2,3.0,5.8,1.6;
7.4,2.8,6.1,1.9;
7.9,3.8,6.4,2.0;
6.4,2.8,5.6,2.2;
6.3,2.8,5.1,1.5;
6.1,2.6,5.6,1.4;
7.7,3.0,6.1,2.3;
6.3,3.4,5.6,2.4;
6.4,3.1,5.5,1.8;
6.0,3.0,4.8,1.8;
6.9,3.1,5.4,2.1;
6.7,3.1,5.6,2.4;
6.9,3.1,5.1,2.3;
5.8,2.7,5.1,1.9;
6.8,3.2,5.9,2.3;
6.7,3.3,5.7,2.5;
6.7,3.0,5.2,2.3;
6.3,2.5,5.0,1.9;
6.5,3.0,5.2,2.0;
6.2,3.4,5.4,2.3;
5.9,3.0,5.1,1.8;
]';

%生成临域选择矩阵


wc=[0 1 0 1 0 0 0 0 0;
    1 0 1 0 1 0 0 0 0;
    0 1 0 0 0 1 0 0 0;
    1 0 0 0 1 0 1 0 0;
    0 1 0 1 0 1 0 1 0;
    0 0 1 0 1 0 0 0 1;
    0 0 0 1 0 0 0 1 0;
    0 0 0 0 1 0 1 0 1;
    0 0 0 0 0 1 0 1 0;];

%生成w0
w=rand(9,4)
for i=1:1:9
    w([i],:)=1/((w([i],:)*w([i],:)')^(1/2))*w([i],:);
end

%增强输入向量组对比度并规格化
for i=1:1:150
    for i2=1:1:4
        p([i2],[i])=p([i2],[i])^2;
    end
    p(:,[i])=1/((p(:,[i])'*p(:,[i]))^(1/2))*p(:,[i]);
end


%权值训练
i=1;
while ~isequal(i,20000)
    i=i+1;
    k=floor(rand*150)+1;    %随机从P中取一向量
        pk=p(:,k);
        
        %A=2*(pk*pk');
        %rr=eigs(A);
        %r=(2/rr(1))-0.01;
        r=0.02;     %下降速度   0.005-0.002

        n1=compet(w*pk);
        q=n1'*wc;
        qq=[q(1) 0 0 0 0 0 0 0 0;
            0 q(2) 0 0 0 0 0 0 0;
            0 0 q(3) 0 0 0 0 0 0;
            0 0 0 q(4) 0 0 0 0 0;
            0 0 0 0 q(5) 0 0 0 0;
            0 0 0 0 0 q(6) 0 0 0;
            0 0 0 0 0 0 q(7) 0 0;
            0 0 0 0 0 0 0 q(8) 0;
            0 0 0 0 0 0 0 0 q(9);];
        pks=[pk';pk';pk';pk';pk';pk';pk';pk';pk'];
        
        w=w+r*qq*(pks-w);
        %for ix=1:1:9
            %w([ix],:)=1/((w([ix],:)*w([ix],:)')^(1/2))*w([ix],:);
        %end

    disp('本次权值');disp(i);disp(w);
end

%测试中间输出：增强输出向量对比度并规格化输出向量
for k=1:1:150
    pk=p(:,k);
    k
    a=w*pk;
    for h=1:1:9
        a([h],:)=a([h],:)^3;
        a=1/((a'*a)^(1/2))*a;
    end
    a
end

%标识w的相近向量组，有几组即分为几类，
%取每个子组的向量平均值作为改组向量的标识向量



disp('ok!')