close all;
clear all;

num1=[0 0 0 0 0 0 0 0 0 0 0 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]'; 
num2=[1 0 0 0 1 1 1 1 1 1 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 1 1 1 1 1 0 0 0 1]';
num3=[1 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1]';
num4=[1 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 1 1 1 1 1 1]';
num5=[1 1 1 1 1 0 0 0 1 1 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 1 0 0 0 1 1 1 1 1]';
num6=[1 1 1 1 1 1 1 1 1 1 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 1 0 0 0 1 1 1 1 1]';
num7=[1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1]';
num8=[1 1 1 1 1 1 1 1 1 1 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1]';
num9=[1 1 1 1 1 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 1 1 1 1 1 1 1]';
num10=[1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1]';
X=[num1 num2 num3 num4 num5 num6 num7 num8 num9 num10];

d=eye(10,10);   %產生一個10*10的單位矩陣

%pr為R*2矩陣,pr(:,1)為輸入元素的min值,pr(:,2)為輸入元素的max值
pr=zeros(36,2);     %pr(:,1)之元素皆為0
pr(:,2)=ones(36,1);  %pr(:,2)之元素皆為1

s=10;   %神經元的數目

%感知機設定方式
%net = newp creates a new network with a dialog box.
%net = newp(PR,S,TF,LF) takes these inputs,
%PR -- R x 2 matrix of min and max values for R input elements
%S  -- Number of neurons 
%TF -- Transfer function, default = 'hardlim'
%LF -- Learning function, default = 'learnp'
net=newp(pr,s,'logsig','learnwh'); 

net.inputweights{1,1}.initFcn='rands';
net.biases{1}.initFcn='rands';
net=init(net);

%The network is initially trained without noise for a maximum of 20000 epochs or 
%until the network sum-squared error falls beneath 1e-4

net.trainParam.lr=0.5;   %學習率
net.trainParam.show=500;     
net.trainParam.epochs=20000;   %迭代次數
net.trainParam.goal=1e-4;   %門檻值

net=train(net,X,d);

wt=net.IW{1,1}; %加權矩陣

bias=net.b{1};  %偏權值

num_sim=[1 1 1 1;   %缺陷的矩陣  這是一個有缺陷（noise）的「0」，看出來了嗎？
         1 0 0 1;
         1 0 0 1;
         1 0 0 1;
         1 0 0 1;
         0 0 0 1;
         0 0 0 1;
         0 0 0 1;
         1 1 1 1];

%將矩陣轉為向量
for j=1:4
    in((j-1)*9+1:j*9,1)=num_sim(:,j);
end

%做排序大小,抓出哪ㄍ數字中辨識率最高,是以比較原本輸入ㄉ數字是否一樣
out=sim(net,in);  %模擬網路
p=0;

for m=1:10
    if out(m,1)>p
        p=out(m,1);
        num=m;
    else
        p=p;
    end
end

p
num