用python写一个逻辑回归算法

好的，以下是一个简单的逻辑回归算法的 Python 代码：

import numpy as np

class LogisticRegression:
    def __init__(self, learning_rate=0.01, num_iterations=100000, fit_intercept=True, verbose=False):
        self.learning_rate = learning_rate
        self.num_iterations = num_iterations
        self.fit_intercept = fit_intercept
        self.verbose = verbose

    def __add_intercept(self, X):
        intercept = np.ones((X.shape[0], 1))
        return np.concatenate((intercept, X), axis=1)

    def __sigmoid(self, z):
        return 1 / (1 + np.exp(-z))

    def __loss(self, h, y):
        return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean()

    def fit(self, X, y):
        if self.fit_intercept:
            X = self.__add_intercept(X)

        self.theta = np.zeros(X.shape[1])

        for i in range(self.num_iterations):
            z = np.dot(X, self.theta)
            h = self.__sigmoid(z)
            gradient = np.dot(X.T, (h - y)) / y.size
            self.theta -= self.learning_rate * gradient

            if self.verbose and i % 10000 == 0:
                z = np.dot(X, self.theta)
                h = self.__sigmoid(z)
                print(f'loss: {self.__loss(h, y)} \t')

    def predict_prob(self, X):
        if self.fit_intercept:
            X = self.__add_intercept(X)

        return self.__sigmoid(np.dot(X, self.theta))

    def predict(self, X, threshold):
        return self.predict_prob(X) >= threshold

这是一个基本的逻辑回归算法，它可以用于二元分类问题。您可以通过调整参数来优化算法的性能。