【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.shapetheta = 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 * 1error = prediction - y          # m * 1gradient = np.dot(X.T, error)   # n * 1theta = theta - alpha * (1 / m) * gradienttheta = np.round(theta, decimals=4)return thetaif __name__ == '__main__':X = np.array([[1, 1], [1, 2], [1, 3]])y = np.array([1, 2, 3])alpha = 0.01iterations = 1000print(linear_regression_gradient_descent(X, y, alpha, iterations))