逻辑斯蒂回归最优化python代码

逻辑斯蒂回归的最优化可以使用梯度下降算法实现，以下是Python代码示例：

import numpy as np

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

def cost(theta, X, y):
    """
    逻辑斯蒂回归代价函数
    """
    m = len(y)
    h = sigmoid(np.dot(X, theta))
    J = (-1 / m) * np.sum(y * np.log(h) + (1 - y) * np.log(1 - h))
    return J

def gradient(theta, X, y):
    """
    逻辑斯蒂回归梯度计算
    """
    m = len(y)
    h = sigmoid(np.dot(X, theta))
    grad = (1 / m) * np.dot(X.T, (h - y))
    return grad

def logistic_regression(X, y, alpha, num_iters):
    """
    逻辑斯蒂回归训练函数
    """
    m, n = X.shape
    theta = np.zeros((n, 1))
    J_history = []
    
    for i in range(num_iters):
        J = cost(theta, X, y)
        grad = gradient(theta, X, y)
        theta = theta - alpha * grad
        J_history.append(J)
    
    return theta, J_history

其中，sigmoid函数用于计算逻辑斯蒂回归模型的预测值，cost函数用于计算代价函数，gradient函数用于计算梯度，logistic_regression函数用于训练模型。其中，alpha为学习率，num_iters为迭代次数。