线性神经网络处理异或问题

# -*- coding: UTF-8 -*-import matplotlib  as mplimport numpy  as npimport matplotlib.pyplot as plt#单层感知器的实现X=np.array([[1,0,0,0,0,0],            [1,0,1,0,0,1],            [1,1,0,1,0,0],            [1,1,1,1,1,1]])Y=np.array([-1,1,1,-1])W = (np.random.random(6)-0.5)*2  #w权值的取值范围为-1,1lr = 0.1 #学习率n=0 #计算迭代次数O=0#神经网络输出def  update():    global  X,Y,W,lr,n    n=n+1    O=np.dot(X,W.T)    W_C = lr*((Y-O.T).dot(X))    W = W+W_Cif __name__ == '__main__':    for  _   in range (100):        update()x1=[0,1]y1=[1,0]x2=[0,1]y2=[0,1]xdata=np.linspace(-1,2)a=W[3]b=W[2]+xdata*W[4]c=W[0]+xdata*W[1]+xdata*xdata*W[3]plt.figure()plt.plot(xdata,(-b+np.sqrt(b*b-4*a*c))/(2*a),'r')plt.plot(xdata,(-b-np.sqrt(b*b-4*a*c))/(2*a),'r')plt.plot(x1,y1,'bo')plt.plot(x2,y2,'yo')plt.show()