用python语言自编程实现逻辑回归算法

好的，以下是一个简单的 Python 实现逻辑回归算法的代码：

import numpy as np

class LogisticRegression:
    def __init__(self, learning_rate=0.01, iterations=1000):
        self.learning_rate = learning_rate
        self.iterations = iterations
        self.weights = None
        self.bias = None

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

    def fit(self, X, y):
        m, n = X.shape
        self.weights = np.zeros(n)
        self.bias = 0

        for i in range(self.iterations):
            z = np.dot(X, self.weights) + self.bias
            a = self.sigmoid(z)

            dz = a - y
            dw = np.dot(X.T, dz) / m
            db = np.sum(dz) / m

            self.weights -= self.learning_rate * dw
            self.bias -= self.learning_rate * db

    def predict(self, X):
        z = np.dot(X, self.weights) + self.bias
        a = self.sigmoid(z)
        y_pred = np.round(a)
        return y_pred

其中，sigmoid 函数用于将输入值映射到 0~1 的范围内，fit 函数用于训练模型，predict 函数用于预测结果。这里使用的是梯度下降算法来最小化损失函数，每次迭代更新权重和偏差，直到达到最大迭代次数或者损失函数的变化小于某个阈值。