单层感知器的python实现

# -*- coding: UTF-8 -*-

import matplotlib  as mpl
import numpy  as np
import 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-1,1

lr = 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_C

if __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)
break
x1=[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()