%BP neural network
clear
inputNums=3;
outputNums=3;
hideNums=10;
maxcount=2000;
samplenum=3;
precision=0.001;

alpha=0.5;%設定值
a=0.5;%設定值
count=1;
error=zeros(1,count);
errorp=zeros(1,samplenum);

v=rand(inputNums,hideNums);
deltv=zeros(inputNums,hideNums);
dv=zeros(inputNums,hideNums);
st1=zeros(1,hideNums);

w=rand(hideNums,outputNums);
deltw=zeros(hideNums,outputNums);
dw=zeros(hideNums,outputNums);
st2=zeros(1,outputNums);

samplelist=[1,0,0;0,1,0;0,0,1];
expectlist=[1,0,0;0,1,0;0,0,1];

while (count<=maxcount)
    c=1;
    while (c<=samplenum)
      for k=1:outputNums
        d(k)=expectlist(c,k);%獲得期望輸出的向量
end
    
      for i=1:inputNums
        x(i)=samplelist(c,i);%獲得輸入的向量（數據）
end

%Forward();
   for j=1:hideNums
     net=0.0;
     for i=1:inputNums
       net=net+x(i)*v(i,j);
end
net=net-st1(j);
     y(j)=1/(1+exp(-net));
end
   for k=1:outputNums
     net=0.0;
     for j=1:hideNums
       net=net+y(j)*w(j,k);
end
net=net-st2(k);
     o(k)=1/(1+exp(-net)); 
end

%BpError(c);
errortmp=0.0;
   for k=1:outputNums
     errortmp=errortmp+(d(k)-o(k))^2;
end
   errorp(c)=0.5*errortmp; 
%end
      
      %Backward();
for k=1:outputNums
     yitao(k)=(d(k)-o(k))*o(k)*(1-o(k));
end
   for j=1:hideNums
     tem=0.0;
     for k=1:outputNums
       tem=tem+yitao(k)*w(j,k);
end
     yitay(j)=tem*y(j)*(1-y(j));
end

   %調整各層權值
   for j=1:hideNums
     for k=1:outputNums
       deltw(j,k)=alpha*yitao(k)*y(j);
       w(j,k)=w(j,k)+deltw(j,k)+a*dw(j,k);
       dw(j,k)=deltw(j,k);
end
end
for k=1:outputNums
st2(k)=st2(k)+alpha*yitao(k);
end
   for i=1:inputNums
     for j=1:hideNums
       deltv(i,j)=alpha*yitay(j)*x(i);
       v(i,j)=v(i,j)+deltv(i,j)+a*dv(i,j);
       dv(i,j)=deltv(i,j);
end
end
for j=1:hideNums
st1(j)=st1(j)+alpha*yitay(j);
end
%end

c=c+1;
end
    
    double tmp;
    tmp=0.0;
    for i=1:samplenum
      tmp=tmp+errorp(i)*errorp(i);
end
    tmp=tmp/c;
    error(count)=sqrt(tmp);

    if (error(count)<precision)
      break;
end
    count=count+1;%訓練次數加1
end

p=1:count;
plot(p,error(p),'-');