逻辑回归梯度下降代码实现

以下是逻辑回归梯度下降的Python代码实现：

import numpy as np

def sigmoid(z):
    """
    sigmoid函数
    """
    return 1 / (1 + np.exp(-z))

def gradient_descent(X, y, theta, alpha, num_iters):
    """
    梯度下降函数
    """
    m = len(y)
    for i in range(num_iters):
        h = sigmoid(np.dot(X, theta))
        theta = theta - (alpha / m) * np.dot(X.T, h - y)
    return theta

# 测试
X = np.array([[1, 2, 3], [1, 3, 4], [1, 4, 5], [1, 5, 6]])
y = np.array([[0], [0], [1], [1]])
theta = np.zeros((3, 1))
alpha = 0.1
num_iters = 1000
theta = gradient_descent(X, y, theta, alpha, num_iters)
print(theta)