advanced_learning.py

# advanced_learning.py
from typing import Dict, List, Any
import asyncio
import numpy as np
from datetime import datetime
import json
import sqlite3

class MetaLearningController:
    def __init__(self):
        self.learning_strategies = {}
        self.policy_pool = {}
        self.adaptation_history = []
        
    async def adapt_learning_strategy(self, performance_metrics: Dict):
        """Adapt learning strategies based on performance"""
        strategy_performance = self.evaluate_strategy_performance(performance_metrics)
        
        # Update policy pool
        for strategy, performance in strategy_performance.items():
            self.policy_pool[strategy] = {
                'weight': self.calculate_strategy_weight(performance),
                'adaptation_rate': self.determine_adaptation_rate(performance)
            }
        
        return await self.generate_optimal_strategy()
    
    def calculate_strategy_weight(self, performance: Dict) -> float:
        """Calculate strategy weight based on performance metrics"""
        return np.mean([
            performance.get('accuracy', 0),
            performance.get('efficiency', 0),
            performance.get('adaptation_speed', 0)
        ])

class KnowledgeSharingNetwork:
    def __init__(self):
        self.shared_knowledge = sqlite3.connect('shared_knowledge.db')
        self.knowledge_buffer = asyncio.Queue()
        self.subscribers = {}
        
    async def share_knowledge(self, source_agent: str, knowledge: Dict):
        """Share knowledge across agents"""
        # Process and enrich knowledge
        enriched_knowledge = self.enrich_knowledge(knowledge)
        
        # Store in shared knowledge base
        await self.store_shared_knowledge(enriched_knowledge)
        
        # Notify relevant agents
        await self.notify_subscribers(source_agent, enriched_knowledge)
    
    def enrich_knowledge(self, knowledge: Dict) -> Dict:
        """Enrich knowledge with metadata and relationships"""
        return {
            **knowledge,
            'metadata': {
                'timestamp': datetime.now().isoformat(),
                'confidence': self.calculate_confidence(knowledge),
                'relationships': self.identify_relationships(knowledge)
            }
        }

class RealTimeMonitor:
    def __init__(self):
        self.metrics_store = sqlite3.connect('metrics.db')
        self.alert_thresholds = {}
        self.performance_history = []
        
    async def monitor_performance(self):
        """Continuous performance monitoring"""
        while True:
            metrics = await self.collect_metrics()
            analysis = self.analyze_metrics(metrics)
            
            if self.should_trigger_action(analysis):
                await self.trigger_adaptive_action(analysis)
            
            await asyncio.sleep(1)  # Monitor frequency
    
    def analyze_metrics(self, metrics: Dict) -> Dict:
        """Analyze collected metrics"""
        return {
            'system_health': self.calculate_system_health(metrics),
            'performance_trends': self.analyze_trends(metrics),
            'bottlenecks': self.identify_bottlenecks(metrics),
            'recommendations': self.generate_recommendations(metrics)
        }

class AdaptivePromptOptimizer:
    def __init__(self):
        self.template_store = {}
        self.performance_history = []
        self.optimization_rules = self.load_optimization_rules()
        
    async def optimize_prompt(self, template_id: str, performance: Dict):
        """Optimize prompt template based on performance"""
        template = self.template_store.get(template_id)
        if not template:
            return None
            
        optimization_score = self.calculate_optimization_score(performance)
        
        if optimization_score < self.optimization_threshold:
            optimized_template = await self.generate_optimized_template(
                template,
                performance
            )
            await self.update_template(template_id, optimized_template)
            
            return optimized_template
        
        return template
    
    async def generate_optimized_template(self, template: str, performance: Dict) -> str:
        """Generate optimized version of template"""
        improvements = []
        for rule in self.optimization_rules:
            if rule.should_apply(template, performance):
                improvements.append(rule.apply(template))
        
        return self.combine_improvements(template, improvements)

class BackupRecoverySystem:
    def __init__(self):
        self.backup_store = sqlite3.connect('backups.db')
        self.backup_config = self.load_backup_config()
        
    async def schedule_backup(self):
        """Schedule and manage system backups"""
        while True:
            if self.should_create_backup():
                await self.create_backup()
            await asyncio.sleep(self.backup_config['interval'])
    
    async def create_backup(self):
        """Create system-wide backup"""
        backup_data = {
            'timestamp': datetime.now().isoformat(),
            'knowledge_bases': await self.backup_knowledge_bases(),
            'system_state': await self.backup_system_state(),
            'agent_states': await self.backup_agent_states()
        }
        
        backup_id = await self.store_backup(backup_data)
        await self.cleanup_old_backups()
        
        return backup_id
    
    async def recover_from_backup(self, backup_id: str):
        """Recover system from backup"""
        backup_data = await self.load_backup(backup_id)
        
        # Systematic recovery process
        await self.restore_knowledge_bases(backup_data['knowledge_bases'])
        await self.restore_system_state(backup_data['system_state'])
        await self.restore_agent_states(backup_data['agent_states'])
        
        return await self.verify_recovery()

# Integration with main system
class EnhancedMOPSystem:
    def __init__(self):
        self.meta_learning = MetaLearningController()
        self.knowledge_sharing = KnowledgeSharingNetwork()
        self.monitor = RealTimeMonitor()
        self.prompt_optimizer = AdaptivePromptOptimizer()
        self.backup_system = BackupRecoverySystem()
        
    async def initialize(self):
        """Initialize enhanced system"""
        # Start monitoring
        asyncio.create_task(self.monitor.monitor_performance())
        
        # Schedule backups
        asyncio.create_task(self.backup_system.schedule_backup())
        
        # Initialize knowledge sharing
        await self.knowledge_sharing.initialize()
        
        # Initialize prompt optimization
        await self.prompt_optimizer.initialize()
        
    async def process_input(self, input_data: Dict):
        """Process input with enhanced features"""
        try:
            # Optimize prompt
            optimized_prompt = await self.prompt_optimizer.optimize_prompt(
                input_data['template_id'],
                self.monitor.get_current_performance()
            )
            
            # Process with optimized prompt
            result = await self.process_with_prompt(optimized_prompt, input_data)
            
            # Share knowledge
            await self.knowledge_sharing.share_knowledge(
                source_agent='main',
                knowledge=result
            )
            
            # Adapt learning strategy
            await self.meta_learning.adapt_learning_strategy(
                self.monitor.get_performance_metrics()
            )
            
            return result
            
        except Exception as e:
            # Trigger recovery if needed
            await self.handle_system_error(e)
            raise

def main():
    """Main entry point"""
    system = EnhancedMOPSystem()
    
    async def run():
        await system.initialize()
        
        # Example usage
        input_data = {
            'template_id': 'memory_processing',
            'content': 'Example input for processing',
            'context': {'previous_state': 'some_state'}
        }
        
        result = await system.process_input(input_data)
        print("Processing Result:", result)
    
    asyncio.run(run())

if __name__ == "__main__":
    main()