contr.m

此程序是用于模式分类的在matlabhu环境下的原代码

clear
clc
nntwarn off
load v1v11.txt
load v1v3.txt
load v1v7.txt
load v6v12.txt
load v6v8.txt
load v10v8.txt
load v10v12.txt
load v10v4.txt
load v2v12.txt
load v9v7.txt
load v2v4.txt
load v2v8.txt
load v5v11.txt
load v5v3.txt
load v5v7.txt
load v6v4.txt
load v9v11.txt
load v9v3.txt
load wu.txt
load v1v5.txt
load v1v9.txt
load v5v9.txt
load v3v11.txt
load v3v7.txt
load v11v7.txt
load v2v6.txt
load v2v10.txt
load v6v10.txt
load v8v12.txt
load v8v14.txt
load v12v14.txt

p111=v1v11(:,2);
p13=v1v3(:,2);
p15=v1v5(:,2);
p17=v1v7(:,2);
p19=v1v9(:,2);
p59=v5v9(:,2);
p311=v3v11(:,2);
p37=v3v7(:,2);
p117=v11v7(:,2);
p26=v2v6(:,2);
p612=v6v12(:,2);
p68=v6v8(:,2);
p210=v2v10(:,2);
p610=v6v10(:,2);
p812=v8v12(:,2);
p814=v8v14(:,2);
p108=v10v8(:,2);
p1012=v10v12(:,2);
p1214=v12v14(:,2);
p104=v10v4(:,2);
p212=v2v12(:,2);
p97=v9v7(:,2);
p24=v2v4(:,2);
p28=v2v8(:,2);
p511=v5v11(:,2);
p53=v5v3(:,2);
p57=v5v7(:,2);
p64=v6v4(:,2);
p911=v9v11(:,2);
p93=v9v3(:,2);
pwu=wu(:,2);

for i=1:200
    qwu(i,1)=wu(i,1);
    q10(i,1)=p111(i,1);
    q1(i,1)=p13(i,1);
    q11(i,1)=p17(i,1);
    q4(i,1)=p15(i,1);
    q5(i,1)=p19(i,1);
    q6(i,1)=p59(i,1);
    q7(i,1)=p311(i,1);
    q8(i,1)=p37(i,1);
    q9(i,1)=p117(i,1);
    q17(i,1)=p612(i,1);
    q27(i,1)=p68(i,1);
    q29(i,1)=p108(i,1);
    q7(i,1)=p1012(i,1);
    q18(i,1)=p104(i,1);
    q19(i,1)=p26(i,1);
    q20(i,1)=p210(i,1);
    q21(i,1)=p610(i,1);
    q22(i,1)=p812(i,1);
    q23(i,1)=p814(i,1);
    q24(i,1)=p1214(i,1);
    q30(i,1)=p1012(i,1);
    q25(i,1)=p212(i,1);
    q3(i,1)=p97(i,1);
    q26(i,1)=p24(i,1);
    q16(i,1)=p28(i,1);
    q2(i,1)=p511(i,1);
    q12(i,1)=p53(i,1);
    q13(i,1)=p57(i,1);
    q28(i,1)=p64(i,1);
    q15(i,1)=p911(i,1);
    q14(i,1)=p93(i,1);
end  

Q=[qwu q1 q2 q3 q4 q5 q6 q7 q8 q9 q10 q11 q12 q13 q14 q15 q16 q17 q18 q19 q20 q21 q22 q23 q24 q25 q26 q17 q28 q29 q30];
Q=premnmx(Q);
t=[0	0	0	0	0	0   0	0	0	0	0	0	0	0	0	0	1	1	1	1   1   1   1   1   1   1   1   1   1   1   1;
   0	0	0	0	1	1   1	1	1	1	1	1	1	1	1	1	0	0	0	0   0   0   0   0   0   1   1   1   1   1   1;
   0	1	1	1	0	0   0	0	0	0	1	1	1	1	1	1	0	0	0	1   1   1   1   1   1   0   0   0   0   0   0;
   0	0	0	0	0	0	0   1	1	1	0	0	0	1	1	1	0	0	0	0   0   0   1   1   1   0   0   0   1   1   1;
   0	0	1	1	0	1	1	0	1	0	0   1	1	0	0	1	0	1	1	0   1   1   0   0   1   0   1   1   0   0   1;
   1	1	0	1	1	0	1	1	0	1	1   0	1	0	1	0	1	0	1	1   0   1   0   1   0   1   0   1   0   1   0];

%  Training parameters are:
%    TP(1) - Epochs between updating display, default = 25.
%    TP(2) - Maximum number of epochs to train, default = 1000.
%    TP(3) - Sum-squared error goal, default = 0.02.
%    TP(4) - Learning rate, 0.01.
%    TP(5) - Learning rate increase, default = 1.05.
%    TP(6) - Learning rate decrease, default = 0.7.
%    TP(7) - Momentum constant, default = 0.9.
%    TP(8) - Maximum error ratio, default = 1.04.
%  Missing parameters and NaN's are replaced with defaults.
p= princomp(Q);
%train
qq=[p p p p];% Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q];
qqq=[qq qq qq qq qq];
qqqq=[qqq qqq qqq qqq qqq];
q1=qqqq+0.20*rand(size(qqqq));
q2=qqqq+0.20*rand(size(qqqq));

tt=[t t t t];% t t t t t t t t t t t t t t t t t t];
ttt=[tt tt tt tt tt];
t1=[ttt ttt ttt ttt ttt];


tp=[20 10000 1 0.5 1.05 0.4 0.9 1.02];
[b1,w1,w2,b2,w3,b3,i,tr_hs,hs]=hsx3(50,20,'logsig','logsig','logsig',q1,t1,tp);
%%模拟
f1='logsig';
f2=f1;
f3=f1;

a1 = feval(f1,w1*q2,b1);
a2 = feval(f2,w2*a1,b2);
a3 = feval(f3,w3*a2,b3)
t=t1;
e = t1-a3;
SSE = sumsqr(e);

n=0;
m=0;
[r,s]=size(t);
for j=1:s
    for k=1:r
        if abs(a3(k,j)-t(k,j))<0.1
            n=n+1;
        else
            n=n;
        end
    end
    if n==r
            m=m+1;
        else
            m=m;      
    end
    n=0;
end
s
m
  u=m/s