Simple linear regression example using least square root for solution.

least_sqrt_root.py

import numpy as np

def linear_regression(x, y):
    """The input are x and y for 2D"""
    assert isinstance(x, np.ndarray)
    assert isinstance(y, np.ndarray)
    w = np.sum((y - np.average(y)) * x) / np.sum((x - np.average(x))**2)
    b = np.average(y) - w * np.average(x)
    return w, b

if __name__ == '__main__':
    w0 = 23.5
    b0 = 12.5
    print(f'{w0=}, {b0=}')
    
    x = np.linspace(1, 100, 50)
    y = x * w0 + b0
    w1, b1 = linear_regression(x, y)
    print(f'{w1=}, {b1=}')