perceptron

相关原理就不再介绍了， 代码贴一下：
这里解决的是李航统计学习方法书上2.1的习题

# -*- coding:utf-8 -*-
import numpy as np
 
training_set = np.array([[(3, 3), 1], [(4, 3), 1], [(1, 1), -1]])
w = np.zeros(2, dtype=int)
b = 0

def update(item):
    global w, b
    w[0] += 1 * item[1] * item[0][0]
    w[1] += 1 * item[1] * item[0][1]
    b += 1 * item[1]
    print w, b

def cal(item):
    res = 0
    for i in range(len(item[0])):
        res += item[0][i] * w[i]
    res += b
    res *= item[1]
    return res

def check():
    flag = 0
    for item in training_set:
        if cal(item) <= 0:
            flag = 1
            update(item)
    if not flag:
        print "RESULT: w: " + str(w) + " b: " + str(b)
    return flag

if __name__ == "__main__":
    for i in range(1000):
        if not check(): break