# Demo entry 6653189

123

Submitted by anonymous on Oct 24, 2017 at 17:44
Language: Python. Code size: 1.2 kB.

```import numpy as np
import matplotlib.pyplot as plt
def sigmoid(x):
return 1.0/(1.0+np.exp(-x))

def train_func(train_x,train_y,iter_n,learn_rate):
weight_len=len(train_x)
num = float(len(train_x))

learn_rate=learn_rate
weight=np.zeros((weight_len,1))
b=0.0
iter_n=iter_n
#iter_n=6
for i in range(iter_n):

error=train_y-sigmoid(train_x.dot(weight)+b)

return weight,b

if __name__=="__main__":
train_x=np.array([[1,1],[1,2],[2,2],[1,0],
[4,5],[5,6],[6,7],[6,6]])
train_y=np.array([,,,,,,,])
iter_n=1000
learn_rate=1
weight,b=train_func(train_x,train_y,iter_n,learn_rate)
print weight
print b

x=train_x[0:len(train_x),0]
y=train_x[0:len(train_x),1]
print x
print y
plt.plot(x,y,"ro")
x1=np.linspace(0,7,400)
y1=-weight/weight*x1-b/weight
plt.plot(x1,y1)
plt.xlabel("x1")
plt.ylabel("x2")
plt.xlim(xmin=0,xmax=7)
plt.ylim(ymax=8)
```

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