个性化网约车系统创建

// 示例代码：使用WebSocket实现实时通信
const WebSocket = require('ws');
const wss = new WebSocket.Server({ port: 8080 });

wss.on('connection', function connection(ws) {
  ws.on('message', function incoming(message) {
    console.log('received: %s', message);
    // 广播消息给所有连接的客户端
    wss.clients.forEach(function each(client) {
      if (client.readyState === WebSocket.OPEN) {
        client.send(message);
      }
    });
  });
});

-- 示例代码：创建用户表
CREATE TABLE users (
  id INT AUTO_INCREMENT PRIMARY KEY,
  name VARCHAR(255) NOT NULL,
  email VARCHAR(255) UNIQUE NOT NULL,
  phone VARCHAR(15),
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

// 示例代码：使用OAuth2.0进行用户认证
const OAuth2Client = require('google-auth-library').OAuth2Client;

const client = new OAuth2Client(CLIENT_ID);

async function verify() {
  const ticket = await client.verifyIdToken({
      idToken: token,
      audience: CLIENT_ID,
  });
  const payload = ticket.getPayload();
  const userid = payload['sub'];
  return {
      userid: userid,
  };
}

# 示例代码：使用机器学习构建推荐系统
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel

tfidf = TfidfVectorizer(stop_words='english')
tfidf_matrix = tfidf.fit_transform(movie_data['description'])

cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)

def get_recommendations(title, cosine_sim=cosine_sim):
    idx = movie_data.index[movie_data['title'] == title].tolist()[0]
    sim_scores = list(enumerate(cosine_sim[idx]))
    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
    sim_scores = sim_scores[1:11]
    movie_indices = [i[0] for i in sim_scores]
    return movie_data['title'].iloc[movie_indices]