逻辑回归梯度下降代码

逻辑回归是一种常用的分类算法，梯度下降是逻辑回归中常用的优化算法。下面是逻辑回归梯度下降的代码示例：

import numpy as np

class LogisticRegression:
    def __init__(self, learning_rate=0.01, num_iterations=1000):
        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 initialize_parameters(self, n_features):
        self.weights = np.zeros(n_features)
        self.bias = 0
    
    def fit(self, X, y):
        n_samples, n_features = X.shape
        self.initialize_parameters(n_features)
        
        for _ in range(self.num_iterations):
            linear_model = np.dot(X, self.weights) + self.bias
            y_predicted = self.sigmoid(linear_model)
            
            dw = (1 / n_samples) * np.dot(X.T, (y_predicted - y))
            db = (1 / n_samples) * np.sum(y_predicted - y)
            
            self.weights -= self.learning_rate * dw
            self.bias -= self.learning_rate * db
    
    def predict(self, X):
        linear_model = np.dot(X, self.weights) + self.bias
        y_predicted = self.sigmoid(linear_model)
        y_predicted_cls = [1 if i > 0.5 else 0 for i in y_predicted]
        return y_predicted_cls

上述代码实现了一个简单的逻辑回归类，其中包括了sigmoid函数、参数初始化、模型训练和预测等功能。你可以根据自己的需求进行调整和扩展。