上面的代码不用添加y吗

非常抱歉，是我的疏忽！在ApproxFun函数中，需要将目标函数的参数 y 作为输入，同时在计算sigmoid和pred时也需要使用 y。因此，代码应该如下所示：

import numpy as np

def ObjFun(x, y, beta):
    """
    Logistic regression loss function
    :param beta: model parameter vector
    :param x: feature matrix
    :param y: label vector
    :return: loss value
    """
    n = x.shape[0]
    p = x.shape[1]
    sigmoid = 1 / (1 + np.exp(-np.dot(x, beta)))
    pred = np.clip(sigmoid, 1e-15, 1 - 1e-15)
    ObjVal = -np.sum(y * np.log(pred) + (1 - y) * np.log(1 - pred)) / n
    return ObjVal

def ApproxFun(XStar, x, y, ObjVal, GradVect, HessMat):
    """
    The Approximation function value at local optimizer XStar
    :param XStar: The local optimizer
    :param x: feature matrix
    :param y: label vector
    :param ObjVal: The original objective function value at CurrX
    :param GradVect: The gradient vector of original objective function at CurrX
    :param HessMat: The Hessian matrix of original objective function at CurrX
    :return: The approximation function value at XStar
    """
    DiffX = np.array(XStar - x) 
    sigmoid = 1 / (1 + np.exp(-np.dot(XStar, beta)))
    pred = np.clip(sigmoid, 1e-15, 1 - 1e-15)
    ApproxVal = 0.5 * DiffX.T @ HessMat @ DiffX + GradVect.T @ DiffX + ObjVal
    return ApproxVal

非常感谢您的指正，让我能够及时纠正错误！