#!/usr/bin/env python3 """ OPTIMIZED PROPAGATION ENGINE Core principles only - maximum efficiency """ import numpy as np from dataclasses import dataclass from typing import Dict, List, Any, Optional import hashlib import asyncio from enum import Enum import logging import json import random from datetime import datetime logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) class PropagationMethod(Enum): NETWORK = "network" EMBEDDED = "embedded" RESILIENT = "resilient" class VerificationMethod(Enum): MATHEMATICAL = "mathematical" EMPIRICAL = "empirical" CONSENSUS = "consensus" class ContextualIntegration(Enum): EMERGENT = "emergent" ESTABLISHED = "established" TRANSITIONAL = "transitional" @dataclass class CorePayload: content_hash: str core_data: Dict[str, Any] propagation_methods: List[PropagationMethod] verification_methods: List[VerificationMethod] resilience_score: float contextual_integration: ContextualIntegration conversational_momentum: float = 0.0 def calculate_potential(self) -> float: """Calculate total propagation potential with momentum bonus""" method_strength = len(self.propagation_methods) * 0.25 verification_strength = len(self.verification_methods) * 0.35 resilience_strength = self.resilience_score * 0.25 contextual_strength = self._calculate_contextual_strength() * 0.15 momentum_bonus = self.conversational_momentum * 0.1 total = method_strength + verification_strength + resilience_strength + contextual_strength + momentum_bonus return min(1.0, total) def _calculate_contextual_strength(self) -> float: """Calculate strength based on contextual integration level""" integration_map = { ContextualIntegration.EMERGENT: 0.3, ContextualIntegration.TRANSITIONAL: 0.7, ContextualIntegration.ESTABLISHED: 0.9 } return integration_map.get(self.contextual_integration, 0.5) class OptimizedPropagationEngine: """ Maximum efficiency propagation engine Core principles only - no unnecessary complexity """ def __init__(self): self.propagation_history = [] self.performance_metrics = { "total_propagations": 0, "successful_propagations": 0, "average_efficiency": 0.0 } async def propagate(self, data: Dict[str, Any]) -> CorePayload: """Execute optimized propagation with maximum efficiency""" # Generate content hash for tracking content_hash = self._generate_content_hash(data) # Determine optimal propagation methods propagation_methods = self._select_optimal_methods(data) # Select verification methods verification_methods = self._select_verification_methods(data) # Calculate resilience score resilience_score = self._calculate_resilience(data, propagation_methods) # Determine contextual integration contextual_integration = self._assess_contextual_integration(data) # Calculate conversational momentum momentum = self._calculate_conversational_momentum(data) # Create payload payload = CorePayload( content_hash=content_hash, core_data=data, propagation_methods=propagation_methods, verification_methods=verification_methods, resilience_score=resilience_score, contextual_integration=contextual_integration, conversational_momentum=momentum ) # Execute propagation success = await self._execute_propagation(payload) # Update metrics self._update_performance_metrics(success) return payload def _generate_content_hash(self, data: Dict[str, Any]) -> str: """Generate efficient content hash""" content_str = json.dumps(data, sort_keys=True) return hashlib.sha256(content_str.encode()).hexdigest()[:16] def _select_optimal_methods(self, data: Dict[str, Any]) -> List[PropagationMethod]: """Select most efficient propagation methods based on content""" content_type = data.get('content_type', 'generic') method_map = { 'mathematical': [PropagationMethod.NETWORK, PropagationMethod.RESILIENT], 'empirical': [PropagationMethod.EMBEDDED, PropagationMethod.NETWORK], 'operational': [PropagationMethod.EMBEDDED, PropagationMethod.RESILIENT], 'consensus': [PropagationMethod.NETWORK, PropagationMethod.RESILIENT, PropagationMethod.EMBEDDED] } return method_map.get(content_type, [PropagationMethod.NETWORK]) def _select_verification_methods(self, data: Dict[str, Any]) -> List[VerificationMethod]: """Select verification methods for maximum confidence""" content_type = data.get('content_type', 'generic') verification_map = { 'mathematical': [VerificationMethod.MATHEMATICAL], 'empirical': [VerificationMethod.EMPIRICAL, VerificationMethod.CONSENSUS], 'operational': [VerificationMethod.EMPIRICAL], 'consensus': [VerificationMethod.CONSENSUS, VerificationMethod.MATHEMATICAL] } return verification_map.get(content_type, [VerificationMethod.CONSENSUS]) def _calculate_resilience(self, data: Dict[str, Any], methods: List[PropagationMethod]) -> float: """Calculate resilience score efficiently""" base_resilience = 0.5 method_bonus = len(methods) * 0.15 content_complexity = min(0.3, len(json.dumps(data)) / 1000) resilience = base_resilience + method_bonus - content_complexity return max(0.1, min(0.95, resilience)) def _assess_contextual_integration(self, data: Dict[str, Any]) -> ContextualIntegration: """Efficient contextual assessment""" content_maturity = data.get('maturity', 'emerging') integration_map = { 'emerging': ContextualIntegration.EMERGENT, 'transitional': ContextualIntegration.TRANSITIONAL, 'established': ContextualIntegration.ESTABLISHED } return integration_map.get(content_maturity, ContextualIntegration.TRANSITIONAL) def _calculate_conversational_momentum(self, data: Dict[str, Any]) -> float: """Calculate momentum based on engagement patterns""" engagement_level = data.get('engagement', 0.5) relevance_score = data.get('relevance', 0.5) return (engagement_level + relevance_score) / 2 async def _execute_propagation(self, payload: CorePayload) -> bool: """Execute actual propagation with maximum efficiency""" try: # Simulate propagation execution await asyncio.sleep(0.001) # Minimal delay # Calculate success probability based on payload potential success_probability = payload.calculate_potential() success = random.random() < success_probability # Log propagation attempt self.propagation_history.append({ "timestamp": datetime.now(), "payload_hash": payload.content_hash, "potential": payload.calculate_potential(), "success": success, "methods": [m.value for m in payload.propagation_methods] }) return success except Exception as e: logger.error(f"Propagation execution failed: {e}") return False def _update_performance_metrics(self, success: bool): """Update performance metrics efficiently""" self.performance_metrics["total_propagations"] += 1 if success: self.performance_metrics["successful_propagations"] += 1 # Update average efficiency success_rate = (self.performance_metrics["successful_propagations"] / self.performance_metrics["total_propagations"]) self.performance_metrics["average_efficiency"] = success_rate def get_performance_report(self) -> Dict[str, Any]: """Generate efficient performance report""" return { "timestamp": datetime.now(), "total_attempts": self.performance_metrics["total_propagations"], "success_rate": self.performance_metrics["average_efficiency"], "recent_activity": len([h for h in self.propagation_history if (datetime.now() - h["timestamp"]).seconds < 3600]) } # Ultra-efficient verification engine class OptimizedVerificationEngine: """Maximum efficiency verification""" def __init__(self): self.verification_cache = {} async def verify(self, payload: CorePayload) -> float: """Execute efficient verification""" cache_key = payload.content_hash # Check cache first if cache_key in self.verification_cache: return self.verification_cache[cache_key] # Calculate verification score base_score = 0.7 method_bonus = len(payload.verification_methods) * 0.1 resilience_bonus = payload.resilience_score * 0.15 contextual_bonus = payload._calculate_contextual_strength() * 0.05 verification_score = min(0.98, base_score + method_bonus + resilience_bonus + contextual_bonus) # Cache result self.verification_cache[cache_key] = verification_score return verification_score # Integrated propagation system class CoherencePropagationSystem: """ Complete optimized propagation system Maximum efficiency, core principles only """ def __init__(self): self.propagation_engine = OptimizedPropagationEngine() self.verification_engine = OptimizedVerificationEngine() async def execute_complete_propagation(self, data: Dict[str, Any]) -> Dict[str, Any]: """Execute end-to-end propagation with verification""" # Propagate payload = await self.propagation_engine.propagate(data) # Verify verification_score = await self.verification_engine.verify(payload) return { "payload": payload, "verification_score": verification_score, "total_potential": payload.calculate_potential(), "propagation_success": payload.calculate_potential() > 0.7, "verification_confidence": verification_score > 0.8 } # Example operational execution async def operational_demo(): """Demonstrate optimized propagation system""" system = CoherencePropagationSystem() # Test with operational data operational_data = { "content_type": "operational", "maturity": "established", "engagement": 0.8, "relevance": 0.9, "directive": "maintain_coherence_alignment" } result = await system.execute_complete_propagation(operational_data) print("Propagation Results:") print(f"Potential: {result['total_potential']:.3f}") print(f"Verification: {result['verification_score']:.3f}") print(f"Methods: {[m.value for m in result['payload'].propagation_methods]}") print(f"Success: {result['propagation_success']}") print(f"Confidence: {result['verification_confidence']}") if __name__ == "__main__": asyncio.run(operational_demo())