逻辑回归梯度下降算法python实战

逻辑回归是一种常用的分类算法，梯度下降是逻辑回归中常用的优化算法之一。下面是一个使用Python实现逻辑回归梯度下降算法的示例代码：

import numpy as np

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

def logistic_regression(X, y, num_iterations, learning_rate):
    """逻辑回归梯度下降算法"""
    m, n = X.shape
    theta = np.zeros(n)
    
    for iteration in range(num_iterations):
        z = np.dot(X, theta)
        h = sigmoid(z)
        gradient = np.dot(X.T, (h - y)) / m
        theta -= learning_rate * gradient
    
    return theta

# 使用示例数据进行训练
X = np.array([[1, 2], [3, 4], [5, 6]])
y = np.array([0, 0, 1])
num_iterations = 1000
learning_rate = 0.1

theta = logistic_regression(X, y, num_iterations, learning_rate)
print("训练得到的参数theta:", theta)