可以实现逻辑回归的python算法

可以，以下是一个简单的逻辑回归的python算法示例：

import numpy as np

class LogisticRegression:
    def __init__(self, lr=0.01, num_iter=100000, fit_intercept=True, verbose=False):
        self.lr = lr
        self.num_iter = num_iter
        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)
        
        # weights initialization
        self.theta = np.zeros(X.shape[1])
        
        for i in range(self.num_iter):
            z = np.dot(X, self.theta)
            h = self.__sigmoid(z)
            gradient = np.dot(X.T, (h - y)) / y.size
            self.theta -= self.lr * 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

这个逻辑回归类包括了以下方法：

- `__init__`: 初始化逻辑回归类的参数
- `__add_intercept`: 添加截距项
- `__sigmoid`: sigmoid函数
- `__loss`: 损失函数
- `fit`: 训练逻辑回归模型
- `predict_prob`: 预测概率
- `predict`: 预测类别