【Deep-ML系列】Linear Regression Using Gradient Descent（手写梯度下降）

题目链接：Deep-ML

这道题主要是要考虑矩阵乘法的维度，保证维度正确，就可以获得最终的theata

import numpy as np
def linear_regression_gradient_descent(X: np.ndarray, y: np.ndarray, alpha: float, iterations: int) -> np.ndarray:
    """
    Linear regression
    :param X: m * n
    :param y:
    :param alpha:
    :param iterations:
    :return:
    """
    m, n = X.shape
    theta = np.zeros((n, 1))
    y = y.reshape(m, 1)     # 保证y是列向量
    for i in range(iterations):
        prediction = np.dot(X, theta)   # m * 1
        error = prediction - y          # m * 1
        gradient = np.dot(X.T, error)   # n * 1
        theta = theta - alpha * (1 / m) * gradient
    theta = np.round(theta, decimals=4)
    return theta

if __name__ == '__main__':
    X = np.array([[1, 1], [1, 2], [1, 3]])
    y = np.array([1, 2, 3])
    alpha = 0.01
    iterations = 1000
    print(linear_regression_gradient_descent(X, y, alpha, iterations))