单层感知器的python实现

# -*- 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([[1,3,3],            [1,4,3],            [1,1,1]])Y=np.array([1,1,-1])W = (np.random.random(3)-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.sign(np.dot(X,W.T))    W_C = lr*((Y-O.T).dot(X))    W = W+W_Cif __name__ == '__main__':    for  _   in range (100):        update()        O=np.sign(np.dot(X,W.T))        print(O)        print(Y)        if(O == Y).all():            print('Finished')            print('epoch:',n)            breakx1=[3,4]y1=[3,3]x2=[1]y2=[1]k=-W[1]/W[2]d=-W[0]/W[2]print('k=',k)print('d=',d)xdata=np.linspace(0,5)plt.figure()plt.plot(xdata,xdata*k+d,'r')plt.plot(x1,y1,'bo')plt.plot(x2,y2,'yo')plt.show()