# Simple example: Predicting user behavior
from sklearn.linear_model import LinearRegression

# Train model on historical data
model = LinearRegression()
model.fit(user_features, purchase_behavior)

# Predict for new users
prediction = model.predict(new_user_features)

// AI can help complete this function
function calculateOptimalRoute(start, end, preferences) {
  // AI suggests: Consider traffic patterns, user preferences, and real-time data
  const routeOptions = await routingService.getRoutes(start, end);
  
  return routeOptions
    .filter(route => matchesPreferences(route, preferences))
    .sort((a, b) => calculateScore(b) - calculateScore(a))[0];
}

// AI-generated test cases based on function analysis
describe('calculateOptimalRoute', () => {
  it('should return fastest route when time is priority', async () => {
    const result = await calculateOptimalRoute(
      { lat: 40.7128, lng: -74.0060 },
      { lat: 40.7589, lng: -73.9851 },
      { priority: 'time' }
    );
    expect(result.type).toBe('fastest');
  });
  
  it('should handle invalid coordinates gracefully', async () => {
    await expect(calculateOptimalRoute(null, null, {}))
      .rejects.toThrow('Invalid coordinates');
  });
});

import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

async function generateCodeDocumentation(code) {
  const response = await openai.chat.completions.create({
    model: "gpt-3.5-turbo",
    messages: [{
      role: "user",
      content: `Generate clear documentation for this code:\n${code}`
    }],
    max_tokens: 500,
  });
  
  return response.choices[0].message.content;
}

# Simple recommendation system
class ProductRecommender:
    def __init__(self):
        self.model = self.load_trained_model()
    
    def get_recommendations(self, user_id, num_recommendations=5):
        user_profile = self.get_user_profile(user_id)
        predictions = self.model.predict(user_profile)
        
        return self.format_recommendations(
            predictions.argsort()[-num_recommendations:][::-1]
        )
    
    def update_model(self, new_interaction_data):
        """Continuously improve recommendations"""
        self.model.partial_fit(new_interaction_data)

// Semantic search instead of exact keyword matching
async function semanticSearch(query, documents) {
  const embeddings = await generateEmbeddings(query);
  const similarities = documents.map(doc => 
    calculateSimilarity(embeddings, doc.embeddings)
  );
  
  return documents
    .map((doc, index) => ({ ...doc, score: similarities[index] }))
    .filter(doc => doc.score > 0.7)
    .sort((a, b) => b.score - a.score);
}

// Implement privacy-preserving AI
class PrivacyAwareAI {
  constructor() {
    this.dataProcessor = new AnonymizedDataProcessor();
  }
  
  async processUserData(userData) {
    // Remove personally identifiable information
    const anonymizedData = this.dataProcessor.anonymize(userData);
    
    // Use only necessary features
    const features = this.extractRelevantFeatures(anonymizedData);
    
    return this.model.predict(features);
  }
}