用python编写一个逻辑回归

下面是一个简单的示例代码，用 Python 实现逻辑回归模型：

import numpy as np

class LogisticRegression:
    def __init__(self, learning_rate=0.01, num_iterations=100000):
        self.learning_rate = learning_rate
        self.num_iterations = num_iterations
        self.weights = None
        self.bias = None
        
    def sigmoid(self, z):
        return 1 / (1 + np.exp(-z))
    
    def fit(self, X, y):
        num_samples, num_features = X.shape
        
        # 初始化权重和偏置
        self.weights = np.zeros(num_features)
        self.bias = 0
        
        # 梯度下降训练模型
        for i in range(self.num_iterations):
            # 计算预测值和损失
            z = np.dot(X, self.weights) + self.bias
            y_pred = self.sigmoid(z)
            cost = (-1 / num_samples) * np.sum(y * np.log(y_pred) + (1 - y) * np.log(1 - y_pred))
            
            # 计算梯度
            dw = (1 / num_samples) * np.dot(X.T, (y_pred - y))
            db = (1 / num_samples) * np.sum(y_pred - y)
            
            # 更新权重和偏置
            self.weights -= self.learning_rate * dw
            self.bias -= self.learning_rate * db
            
        return self
    
    def predict(self, X):
        z = np.dot(X, self.weights) + self.bias
        y_pred = self.sigmoid(z)
        y_pred_class = [1 if i > 0.5 else 0 for i in y_pred]
        return y_pred_class

其中，sigmoid 函数用于将预测值转换为概率值，fit 方法用于训练模型，predict 方法用于预测新样本的类别。在训练时，使用梯度下降法更新模型的参数，以最小化损失函数。