一元线性回归代码实现

在代码中需要去预测一些数据，就用到了线性回归方程，其关键是最小二乘

根据公式把代码实现了一下，虽然可以用别人封装好的函数实现，但还是想自己写出来强化记忆

# -*- coding: utf-8 -*-
# @Time    : 2022/11/1
# @Author  : Naihe
# @Email   : 239144498@qq.com
# @File    : LinearRegression.py
# @Software: PyCharm
import matplotlib.pyplot as plt


def avg(*args):
    """求平均值"""
    return sum(args) / len(args)


def subt(x, _x):
    """求差"""
    return x - _x


def square(x, _x=0):
    """求平方"""
    return subt(x, _x) ** 2


def square_sum(gap=0, *args):
    """求平方和"""
    return sum(map(square, args, [gap] * len(args)))


def mult(x, _x, y, _y):
    """求乘积"""
    return subt(x, _x) * subt(y, _y)


def iter_mult_sum(x, y, _x=0, _y=0):
    """求乘积和"""
    for x, y in zip(x, y):
        yield mult(x, _x, y, _y)


if __name__ == '__main__':

    x = list(range(1, 11))
    y = list(range(2, 22, 2))

    avg_x = avg(*x)
    avg_y = avg(*y)
    dived = sum(iter_mult_sum(x, y, avg_x, avg_y))
    div = square_sum(avg_x, *x)
    slope = dived / div
    intercept = avg_y - slope * avg_x
    print(f"斜率:{slope}\t截距{intercept}")

    x_ = 222
    y_ = slope * x_ + intercept
    print(f"预测值:{x_}\t结果:{y_}")

    plt.plot(x, y, color='r', marker='o', linestyle='dashed')
    plt.show()

输出内容

斜率:2.0    截距0.0
预测值:222    结果:444.0

数据画图显示，对于这种简单的数据，还是很好拟合的