《模式识别与智能计算》感知机模型

# coding:utf-8
"""
@author:Darren
@file:perceptron.py
@time:2020/1/14
"""
import numpy as np


class Perceptron:
    def __init__(self, x, y, n, eta):
        self.x = x  # 实例点
        self.y = y  # 实例点的分类
        self.n = n  # 实例点个数
        self.eta = eta  # 学习率

    # 随机梯度下降法
    def SGD(self):
        # 对w,b取初值
        w = np.array([0, 0])
        b = 0
        # 记录一次迭代是否有误分类点
        flag = 1
        while flag == 1:
            flag = 0
            for i in range(n):
                # 如果有误分类点
                if self.y[i] * (w.dot(self.x[i]) + b) <= 0:
                    # 更新w,b，设置flag为1
                    w = w + self.eta * self.y[i] * self.x[i]
                    b = b + self.eta * self.y[i]
                    flag = 1
        return w, b


if __name__ == "__main__":
    x = np.array([[3, 3], [4, 3], [1, 1]])
    y = np.array([1, 1, -1])
    n = 3
    eta = 1
    perceptron = Perceptron(x, y, n, eta)
    w, b = perceptron.SGD()
    print("w is",w)
    print("b is",b)

w is [1 1]
b is -3