sklearn调用逻辑回归模块，自编程实现逻辑回归算法代码

好的，下面是自编程实现逻辑回归算法的一份Python代码：

import numpy as np

class MyLogisticRegression:
    def __init__(self, learning_rate=0.01, num_iterations=10000):
        self.learning_rate = learning_rate
        self.num_iterations = num_iterations
        
    def sigmoid(self, z):
        return 1 / (1 + np.exp(-z))
    
    def cost_function(self, X, y, theta):
        m = len(y)
        h = self.sigmoid(np.dot(X, theta))
        J = -1/m * np.sum(y * np.log(h) + (1-y) * np.log(1-h))
        return J
    
    def gradient_descent(self, X, y, theta):
        m = len(y)
        for i in range(self.num_iterations):
            h = self.sigmoid(np.dot(X, theta))
            gradient = np.dot(X.T, (h - y)) / m
            theta -= self.learning_rate * gradient
        return theta
    
    def fit(self, X, y):
        X = np.insert(X, 0, 1, axis=1) # add bias term
        n = X.shape[1]
        self.theta = np.zeros((n,1))
        self.theta = self.gradient_descent(X, y, self.theta)
        
    def predict(self, X):
        X = np.insert(X, 0, 1, axis=1) # add bias term
        y_pred = np.round(self.sigmoid(np.dot(X, self.theta)))
        return y_pred

这里使用了梯度下降算法求解逻辑回归模型，其中`sigmoid`函数用于计算逻辑回归模型的预测值，`cost_function`函数用于计算模型的损失函数，`gradient_descent`函数用于求解模型参数，`fit`函数用于训练模型，`predict`函数用于对新数据进行预测。