逻辑回归算法python实现

以下是使用原生Python实现逻辑回归算法的示例：

import numpy as np

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

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

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

        for _ in range(self.n_iterations):
            model = np.dot(X, self.weights) + self.bias
            predictions = self.sigmoid(model)

            dw = (1 / n_samples) * np.dot(X.T, (predictions - y))
            db = (1 / n_samples) * np.sum(predictions - y)

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

    def predict(self, X):
        model = np.dot(X, self.weights) + self.bias
        predictions = self.sigmoid(model)
        return [1 if i > 0.5 else 0 for i in predictions]

这是一个简单的逻辑回归算法的Python实现示例，包括了模型的训练和预测方法。