perceptron and ANN

 %% Perceptron Regression
 close all
 clear

 %%load data
 x = load('ex4x.dat');
 y = load('ex4y.dat');

 x=ones(,);
 for i=:
     x(i,)=mod(i,);
     x(i,)=floor(i/);
 end

 for i=:
     if (x(i,)+x(i,))<
         y(i)=;
     else
         y(i)=;
     end
 end

 [m, n] = size(x);

 % Add intercept term to x
 x = [ones(m, ), x];

 %%draw picture
 % find returns the indices of the
 % rows meeting the specified condition
 pos = find(y == );
 neg = find(y == );
 % Assume the features are in the 2nd and 3rd
 % columns of x
 figure('NumberTitle', 'off', 'Name', '感知机');
 plot(x(pos, ), x(pos,), '+');
 hold on;
 plot(x(neg, ), x(neg, ), 'o');

 %进行初始化
 s = ;                     % 标识符，当s=0时，表示迭代终止
 n = ;                     % 表示迭代的次数
 N = ;                 %定义N为最大分类判别次数，判别次数超过此值则认定样本无法分类。
 w= [,,]';                % 取权向量初始值

 % 开始迭代
 while s
     J = ;                % 假设初始的分类全部正确
     for i = :size(pos)
         if (x(pos(i),:)*w)<=   % 查看x1分类是否错误，在x属于w1却被错误分类的情况下，w'x<0
             w = w+x(pos(i),:)';% 分类错误，对权向量进行修正
             J = ;         % 置错误标志位为1
         end
     end
     for i = :size(neg)
         if (x(neg(i),:)*w)>=    % 查看x2分类是否错误，在x属于w2却被错误分类的情况下，w'x>0
             w = w-x(neg(i),:)';% 分类错误，对权向量进行修正
             J = ;         % 置错误标志位为1
         end
     end
     if J==                 % 代价为0，即分类均正确
         s = ;              % 终止迭代
     end
     n = n+;            % 迭代次数加1
     if n == N
         s=;
     end
 end

 w=[;w()/w();w()/w()];

 %%Calculate the decision boundary line
 plot_x = [min(x(:,)),  max(x(:,))];
 plot_y = (-./w()).*(w().*plot_x +w());
 plot(plot_x, plot_y)
 legend('Admitted', 'Not admitted', 'Decision Boundary')
 hold off