梯度下降算法python实战

梯度下降算法是一种常用的优化算法，用于求解损失函数的最小值。下面是一个使用Python实现梯度下降算法的示例代码：

import numpy as np

def gradient_descent(X, y, learning_rate, num_epochs):
    num_samples, num_features = X.shape
    theta = np.zeros(num_features)  # 初始化参数
    for epoch in range(num_epochs):
        gradients = np.zeros(num_features)  # 初始化梯度
        for i in range(num_samples):
            x = X[i]
            prediction = np.dot(theta, x)
            error = prediction - y[i]
            gradients += error * x  # 累计梯度
        gradients /= num_samples  # 求平均梯度
        theta -= learning_rate * gradients  # 更新参数
    return theta

# 示例数据
X = np.array([[1, 2], [2, 3], [3, 4], [4, 5]])
y = np.array([2, 3, 4, 5])

# 调用梯度下降算法
learning_rate = 0.01
num_epochs = 1000
theta = gradient_descent(X, y, learning_rate, num_epochs)
print("最优参数：", theta)