簡單感知機的實現

感知機演算法初步實現

import numpy as np

# 階躍函式
deff
(y):
return
1if y>
0else
0def
and():
x = np.array([[
0,0]
,[0,
1],[
1,0]
,[1,
1]])
#資料集
w = np.array(
[0.038
,0.044])
c =0.042
#閾值 a =
0.015
#學習速率a
b =0.006
#學習速率b
d=[0
,0,0
,1]#期望輸出
e=0#誤差 
for i in
range(4
):s = np.
sum(w*x[i])-c
y = f(s)
e = d[i]
- y 
w = w+a*e*x[i]
c = np.
sum(c+b*e)
print
(w, c)
if __name__ ==
'__main__'
: and(
)

import numpy as np

# 階躍函式
deff
(y):
return
1if y>
0else
0def
and():
x = np.array([[
0,0]
,[0,
1],[
1,0]
,[1,
1]])
#資料集
w = np.array(
[0.038
,0.044])
c =0.042
#閾值 a =
0.015
#學習速率a
b =0.006
#學習速率b
d=[0
,0,0
,1]#期望輸出
e=0#誤差
while
true
: num_errors=
0for i in
range(4
):s = np.
sum(w*x[i])-c
y = f(s)
e = d[i]
- y 
w = w+a*e*x[i]
c = np.
sum(c+b*e)
# 判斷是否誤分類
if e !=0:
num_errors +=
1#print(e)
if num_errors ==0:
print
(w, c)
break
if __name__ ==
'__main__'
: and(
)

import numpy as np

import matplotlib.pyplot as plt
deff
(y):
return
1if y >
0else
0def
and():
x = np.array([[
0,0]
,[0,
1],[
1,0]
,[1,
1]])
# 資料集
w = np.array(
[0.038
,0.044])
c =0.042
# 閾值
a =0.015
# 學習速率a
b =0.006
# 學習速率b
d =[0
,0,0
,1]# 期望輸出
e =0# 誤差
while
true
: num_errors =
0for i in
range(4
):s = np.
sum(w * x[i]
)- c
y = f(s)
e = d[i]
- y w = w + a * e * x[i]
c = np.
sum(c + b * e)
# 判斷是否誤分類
if e !=0:
num_errors +=
1#print(e)
if num_errors ==0:
print
(w, c)
break
# x = np.random.ranf(20) # 0到1的均勻分布
x = np.random.uniform(
-0.1
,1.2
,100
)# 指定在0.0~1.2均勻分布
y =-(w[0]
* x-c)
/ w[1]
plt.plot(x, y,
'g')
# # # 畫出訓練集中的點
plt.scatter(0,
0, c=
'r')
plt.scatter(0,
1, c=
'r')
plt.scatter(1,
0, c=
'r')
plt.scatter(1,
1, c=
'b')
plt.show(
)if __name__ ==
'__main__'
: and(
)

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Python 實現簡單的感知機演算法

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