感知機（Python實現，簡單）

#!/usr/bin/python

#-*-coding:utf-8 -*-
importrandom
fromnumpyimport*
importnumpyasnp
deftraining():
train_data1 = [[3, 3, 1], [4, 3, 1]]  # 正樣本
train_data2 = [[1, 1, -1]]  # 負樣本
train_datas = train_data1 + train_data2  # 樣本集
leng=len(train_datas[0])
#選取初值
w=[0,0]     #權值
b=0         
#偏置step=1
while(judgeerrorpoint(w,b,train_datas)):
#選取資料(xi,yi)
ranint=random.randint(0,2)
data=train_datas[ranint]
xi,yi=selectxiyi(leng,data)
# print(str(xi)+"----"+str(yi))
ifyi[0]*((calculate(w,xi)+b))<=0:
w=mat(w)+mat(step*yi[0]*xi)
b=b+step*yi[0]
print(w)
print(b)
returnw,b
#矩陣計算
defcalculate(w,xi):
a1=w
a1=mat(a1)
a2=mat(xi).t  #矩陣轉置
a3=a1*a2
val=a3[0,0]  #把矩陣變為乙個數值
# print (str(val)+"-----val")
returnval
#判斷是否有誤分類點
defjudgeerrorpoint(w,b,train_datas):
leng=len(train_datas[0])
judge=false
fordataintrain_datas:
xi,yi=selectxiyi(leng,data)
ifyi[0]*((calculate(w,xi)+b))<=0:
judge=true
returnjudge
returnjudge
#把資料來源中的xi,yi分開
defselectxiyi(leng,data):
ranint=random.randint(0,2)
# print(ranint)
xi=data[:leng-1]
yi=data[-1:leng]
returnxi,yi
training()

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