2.2.1 感知机
PLA 算法简介
PLA 是 Perceptron Learning Algorithm 的缩写，也叫感知机算法。感知机算法与线性回归有很多相似的地方。例如线性回归采用梯度下降法求最优参数的时候对每个数据点都进行了遍历，求误差的平均值。而 PLA 只选取一个点进行处理，所以 PLA 的学习过程也可以叫做 随机梯度下降。
代码
import numpy as np
from numpy import *
import random
def pla():
W=np.ones(4)#initial all weight with 1
count=0
dataset=[[1,0.10723,0.64385, 0.29556 ,1],
[1 ,0.2418, 0.83075, 0.42741, 1],
[1 ,0.23321 ,0.81004 ,0.98691, 1],
[1 ,0.36163, 0.14351 ,0.3153, -1],
[1, 0.46984, 0.32142, 0.00042772, -1],
[1, 0.25969, 0.87208 ,0.075063, -1],
]
while True:
count+=1
iscompleted=True
for i in range(0,len(dataset)):
X=dataset[i][:-1]
Y=np.dot(W,X)#matrix multiply
if sign(Y)==sign(dataset[i][-1]):
continue
else:
iscompleted=False
W=W+(dataset[i][-1])*np.array(X)
if iscompleted:
break
print("final W is :",W)
print("count is :",count)
return W
def main():
pla()
if __name__ == '__main__':
main()
