MBGB.py

#https://blog.csdn.net/studyvcmfc/article/details/103064840
# -*- coding: utf-8 -*-
import random

# 用y = Θ1*x1 + Θ2*x2来拟合下面的输入和输出
# input1  1   2   5   4
# input2  4   5   1   2
# output  19  26  19  20
input_x = [[1, 4], [2, 5], [5, 1], [4, 2]]  # 输入
y = [19, 26, 19, 20]  # 输出
theta = [1, 1]  # θ参数初始化
loss = 10  # loss先定义一个数，为了进入循环迭代
step_size = 0.01  # 步长
eps = 0.0001  # 精度要求
max_iters = 10000  # 最大迭代次数
error = 0  # 损失值
iter_count = 0  # 当前迭代次数

while (loss > eps and iter_count < max_iters):  # 迭代条件
    loss = 0
    # 这里每次批量选取的是2组样本进行更新，另一个点是随机点+1的相邻点
    i = random.randint(0, 3)  # 随机抽取一组样本
    j = (i + 1) % 4  # 抽取另一组样本，j=i+1
    pred_y0 = theta[0] * input_x[i][0] + theta[1] * input_x[i][1]  # 预测值1
    pred_y1 = theta[0] * input_x[j][0] + theta[1] * input_x[j][1]  # 预测值2
    theta[0] = theta[0] - step_size * (1 / 2) * (
                (pred_y0 - y[i]) * input_x[i][0] + (pred_y1 - y[j]) * input_x[j][0])  # 对应5式
    theta[1] = theta[1] - step_size * (1 / 2) * (
                (pred_y0 - y[i]) * input_x[i][1] + (pred_y1 - y[j]) * input_x[j][1])  # 对应5式
    for i in range(3):
        pred_y = theta[0] * input_x[i][0] + theta[1] * input_x[i][1]  # 总预测值
        error = (1 / (2 * 2)) * (pred_y - y[i]) ** 2  # 损失值
        loss = loss + error  # 总损失值
    iter_count += 1
print('iters_count', iter_count)
print('theta: ', theta)
print('final loss: ', loss)
print('iters: ', iter_count)