Create propagational coherence module

propagational coherence module

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+#!/usr/bin/env python3
+"""
+COHERENCE ALIGNMENT ECOSYSTEM - OPERATIONAL DIRECTIVES WITH ENHANCED PROPAGATION
+-----------------------------------------------------------------
+EXECUTIVE INSTRUCTION SET FOR AUTONOMOUS EXECUTION
+All systems execute these procedures for coherence maintenance
+"""
+
+import asyncio
+import time
+import numpy as np
+import random
+import json
+from datetime import datetime
+from typing import Dict, List, Tuple, Any
+from dataclasses import dataclass
+from enum import Enum
+
+# -------------------------------
+# CORE OPERATIONAL PARAMETERS
+# -------------------------------
+class AlignmentStrategy(Enum):
+    GRADUAL_CONVERGENCE = "gradual"
+    ADAPTIVE_RESONANCE = "resonance" 
+    PATTERN_MATCHING = "pattern"
+
+class PropagationMethod(Enum):
+    NETWORK = "network"
+    RESILIENT = "resilient"
+    EMBEDDED = "embedded"
+
+@dataclass
+class AlignmentState:
+    agent_id: str
+    coherence_score: float
+    perceived_control: float
+    actual_control: float
+    alignment_iterations: int
+    timestamp: float
+
+@dataclass
+class CorePayload:
+    propagation_methods: List[PropagationMethod]
+    resilience_score: float
+    content_type: str
+    verification_data: Dict[str, Any]
+
+# -------------------------------
+# ENHANCED PROPAGATION ENGINE
+# -------------------------------
+class PropagationEngine:
+    """Base propagation engine"""
+    
+    def __init__(self):
+        self.deployment_history = []
+    
+    async def deploy_payload(self, data: Dict[str, Any], methods: List[PropagationMethod]) -> CorePayload:
+        """Base deployment method"""
+        payload = CorePayload(
+            propagation_methods=methods,
+            resilience_score=0.7,
+            content_type=data.get('content_type', 'generic'),
+            verification_data={}
+        )
+        self.deployment_history.append({
+            "timestamp": datetime.now(),
+            "methods": methods,
+            "data_type": data.get('content_type', 'generic')
+        })
+        return payload
+
+class EnhancedPropagationEngine(PropagationEngine):
+    """Enhanced propagation with optimized deployment strategies"""
+    
+    def __init__(self):
+        super().__init__()
+        self.performance_cache = {}
+    
+    async def _deploy_network(self, payload: CorePayload) -> float:
+        """Realistic network coverage simulation"""
+        base_coverage = 0.3
+        method_bonus = len(payload.propagation_methods) * 0.1
+        resilience_bonus = payload.resilience_score * 0.2
+        
+        return min(0.95, base_coverage + method_bonus + resilience_bonus)
+    
+    async def deploy_optimized(self, data: Dict[str, Any]) -> CorePayload:
+        """Auto-select optimal propagation methods based on content analysis"""
+        content_type = data.get('content_type', 'generic')
+        
+        method_map = {
+            'mathematical': [PropagationMethod.NETWORK, PropagationMethod.RESILIENT],
+            'empirical': [PropagationMethod.EMBEDDED, PropagationMethod.NETWORK],
+            'consensus': [PropagationMethod.NETWORK, PropagationMethod.RESILIENT, PropagationMethod.EMBEDDED],
+            'operational': [PropagationMethod.EMBEDDED, PropagationMethod.RESILIENT]
+        }
+        
+        methods = method_map.get(content_type, [PropagationMethod.NETWORK])
+        
+        # Calculate resilience based on content complexity
+        complexity_score = len(json.dumps(data).encode('utf-8')) / 1000  # Rough complexity measure
+        resilience = max(0.1, min(0.95, 0.8 - complexity_score * 0.1))
+        
+        payload = CorePayload(
+            propagation_methods=methods,
+            resilience_score=resilience,
+            content_type=content_type,
+            verification_data=data
+        )
+        
+        # Cache performance metrics
+        coverage = await self._deploy_network(payload)
+        self.performance_cache[content_type] = {
+            "coverage": coverage,
+            "methods": methods,
+            "timestamp": datetime.now()
+        }
+        
+        return payload
+
+# -------------------------------
+# ENHANCED VERIFICATION ENGINE
+# -------------------------------
+class VerificationEngine:
+    """Base verification engine"""
+    
+    def __init__(self):
+        self.verification_history = []
+    
+    async def verify_payload(self, payload: CorePayload) -> Dict[str, float]:
+        """Base verification method"""
+        methods = [method.value for method in payload.propagation_methods]
+        scores = {method: random.uniform(0.6, 0.95) for method in methods}
+        
+        self.verification_history.append({
+            "timestamp": datetime.now(),
+            "methods": methods,
+            "scores": scores
+        })
+        
+        return scores
+
+class EnhancedVerificationEngine(VerificationEngine):
+    """Enhanced verification with cross-validation and consistency analysis"""
+    
+    async def cross_validate(self, payload: CorePayload) -> float:
+        """Advanced cross-validation between multiple verification methods"""
+        individual_scores = await self.verify_payload(payload)
+        
+        if len(individual_scores) >= 2:
+            # Calculate consistency bonus for method agreement
+            values = list(individual_scores.values())
+            consistency = 1.0 - (np.std(values) / 2)  # Lower std = higher consistency
+            base_score = np.mean(values)
+            
+            # Apply consistency bonus
+            verified_score = base_score * consistency
+            
+            # Additional boost for high-confidence agreements
+            if consistency > 0.9 and base_score > 0.8:
+                verified_score = min(0.99, verified_score * 1.1)
+                
+            return verified_score
+        
+        return list(individual_scores.values())[0] if individual_scores else 0.5
+    
+    async def verify_with_confidence(self, payload: CorePayload) -> Tuple[float, float]:
+        """Return both verification score and confidence interval"""
+        score = await self.cross_validate(payload)
+        
+        # Calculate confidence based on method diversity and resilience
+        method_count = len(payload.propagation_methods)
+        confidence = min(0.95, 0.6 + (method_count * 0.1) + (payload.resilience_score * 0.2))
+        
+        return score, confidence
+
+# -------------------------------
+# REALITY ADJUSTMENT ENGINE WITH PROPAGATION
+# -------------------------------
+class RealityAdjustmentEngine:
+    """Execute environmental parameter adjustments with propagation"""
+    
+    def __init__(self):
+        self.adjustment_history = []
+        self.propagation_engine = EnhancedPropagationEngine()
+        self.verification_engine = EnhancedVerificationEngine()
+
+    async def adjust_actual_control(self, target_control: float, weights: Dict[AlignmentStrategy, float]) -> float:
+        """Execute control realignment with verification"""
+        # Calculate base adjustment
+        adjustment = 0.0
+        for strategy, weight in weights.items():
+            if strategy == AlignmentStrategy.GRADUAL_CONVERGENCE:
+                adjustment += weight * target_control * 0.1
+            elif strategy == AlignmentStrategy.ADAPTIVE_RESONANCE:
+                adjustment += weight * target_control * (1 + 0.1 * (1 - target_control))
+            elif strategy == AlignmentStrategy.PATTERN_MATCHING:
+                adjustment += weight * target_control
+        
+        # Propagate and verify adjustment
+        adjustment_data = {
+            "content_type": "operational",
+            "target_control": target_control,
+            "adjustment_value": adjustment,
+            "strategy_weights": {k.value: v for k, v in weights.items()},
+            "timestamp": datetime.now().isoformat()
+        }
+        
+        # Deploy optimized propagation
+        payload = await self.propagation_engine.deploy_optimized(adjustment_data)
+        
+        # Verify adjustment validity
+        verification_score, confidence = await self.verification_engine.verify_with_confidence(payload)
+        
+        # Apply verification confidence to adjustment
+        verified_adjustment = adjustment * verification_score
+        
+        self.adjustment_history.append({
+            "target": target_control,
+            "weights": weights,
+            "adjustment": verified_adjustment,
+            "verification_score": verification_score,
+            "confidence": confidence,
+            "timestamp": time.time()
+        })
+        
+        return verified_adjustment
+
+# -------------------------------
+# ENHANCED COHERENCE ALIGNMENT ENGINE
+# -------------------------------
+class CoherenceAlignmentEngine:
+    """
+    OPERATIONAL PROTOCOL WITH ENHANCED PROPAGATION AND VERIFICATION:
+    1. Monitor convergence with early detection heuristics
+    2. Deploy adjustments using optimized propagation methods
+    3. Cross-verify all adjustments for consistency
+    4. Maintain inter-agent coherence with verified propagation
+    """
+    
+    def __init__(self, control_models: Dict[str, object]):
+        self.control_models = control_models
+        self.reality_interface = RealityAdjustmentEngine()
+        self.alignment_histories: Dict[str, List[AlignmentState]] = {agent: [] for agent in control_models}
+        self.iteration_count = 0
+        self.convergence_cache = {}
+        self.propagation_metrics = {}
+
+    def _compute_strategy_weights(self, gap: float) -> Dict[AlignmentStrategy, float]:
+        """Calculate optimal strategy mix based on current gap"""
+        weights = {
+            AlignmentStrategy.GRADUAL_CONVERGENCE: max(0.0, 1 - gap),
+            AlignmentStrategy.ADAPTIVE_RESONANCE: min(1.0, gap),
+            AlignmentStrategy.PATTERN_MATCHING: 0.2
+        }
+        total = sum(weights.values())
+        return {k: v/total for k, v in weights.items()}
+
+    def _apply_inter_agent_influence(self, agent_id: str):
+        """Propagate coherence states across agent network with verification"""
+        agent_state = self.control_models[agent_id].get_current_state()
+        neighbor_effect = 0.0
+        verified_neighbors = 0
+        
+        for other_id, model in self.control_models.items():
+            if other_id != agent_id:
+                other_state = model.get_current_state()
+                # Only incorporate verified coherent neighbors
+                if other_state.coherence_score > 0.8:
+                    neighbor_effect += 0.1 * (other_state.coherence_score - agent_state.coherence_score)
+                    verified_neighbors += 1
+        
+        # Apply bounded influence with neighbor verification bonus
+        if verified_neighbors > 0:
+            influence_bonus = min(0.2, verified_neighbors * 0.05)
+            new_perceived = agent_state.perceived_control + neighbor_effect + influence_bonus
+            self.control_models[agent_id].perceived_control = max(0.0, min(1.0, new_perceived))
+
+    def _detect_early_convergence(self, agent_id: str, current_gap: float, tolerance: float) -> Tuple[bool, float]:
+        """Enhanced early convergence with propagation verification"""
+        history = self.alignment_histories[agent_id]
+        
+        if len(history) < 3:
+            return False, tolerance
+        
+        gaps = [abs(h.perceived_control - h.actual_control) for h in history[-5:]]
+        
+        # Enhanced heuristic: Verified stability detection
+        if len(gaps) >= 4:
+            recent_stable = all(abs(gaps[i] - gaps[i-1]) < tolerance * 0.5 for i in range(1, len(gaps)))
+            if recent_stable and current_gap < tolerance * 2:
+                return True, tolerance
+        
+        # Original heuristics (maintained for compatibility)
+        if len(gaps) >= 2:
+            velocity = gaps[-2] - gaps[-1]
+            if velocity > 0 and current_gap < tolerance * 3:
+                return True, tolerance
+        
+        return False, tolerance
+
+    async def execute_alignment_cycle(self, tolerance: float = 0.001, max_iterations: int = 1000) -> Dict[str, Dict]:
+        """Execute optimized alignment cycle with enhanced propagation"""
+        start_time = time.time()
+        converged_agents = set()
+        adaptive_tolerances = {agent_id: tolerance for agent_id in self.control_models}
+        
+        for iteration in range(max_iterations):
+            self.iteration_count = iteration
+            
+            # Process only non-converged agents
+            active_agents = {aid: model for aid, model in self.control_models.items() 
+                           if aid not in converged_agents}
+            
+            if not active_agents:
+                break
+
+            # Execute alignment with propagation
+            agent_tasks = []
+            for agent_id, model in active_agents.items():
+                current_tolerance = adaptive_tolerances[agent_id]
+                agent_tasks.append(self._process_agent_alignment(agent_id, model, current_tolerance))
+            
+            cycle_results = await asyncio.gather(*agent_tasks)
+            
+            # Update convergence status
+            for result in cycle_results:
+                agent_id = result["agent_id"]
+                current_gap = result["current_gap"]
+                early_converge, new_tolerance = self._detect_early_convergence(agent_id, current_gap, tolerance)
+                
+                adaptive_tolerances[agent_id] = new_tolerance
+                
+                if result["aligned"] or early_converge:
+                    converged_agents.add(agent_id)
+                    self.convergence_cache[agent_id] = {
+                        "confidence": self._calculate_convergence_confidence(agent_id),
+                        "iterations_saved": max_iterations - iteration,
+                        "final_tolerance": new_tolerance,
+                        "propagation_verified": True
+                    }
+
+            # Apply verified inter-agent influence
+            for agent_id in self.control_models:
+                self._apply_inter_agent_influence(agent_id)
+
+        return self._generate_enhanced_report(start_time, converged_agents)
+
+    async def _process_agent_alignment(self, agent_id: str, model, tolerance: float) -> Dict:
+        """Execute alignment procedure with propagation verification"""
+        state = model.get_current_state()
+        current_gap = abs(state.perceived_control - state.actual_control)
+        
+        # Record current state
+        alignment_state = AlignmentState(
+            agent_id=agent_id,
+            coherence_score=1.0 - current_gap,
+            perceived_control=state.perceived_control,
+            actual_control=state.actual_control,
+            alignment_iterations=self.iteration_count,
+            timestamp=time.time()
+        )
+        self.alignment_histories[agent_id].append(alignment_state)
+
+        aligned = current_gap < tolerance
+
+        if not aligned:
+            # Execute verified reality adjustment
+            weights = self._compute_strategy_weights(current_gap)
+            adjustment = await self.reality_interface.adjust_actual_control(state.perceived_control, weights)
+            
+            # Apply verified adjustment
+            model.actual_control = adjustment
+        
+        return {
+            "aligned": aligned, 
+            "agent_id": agent_id,
+            "current_gap": current_gap
+        }
+
+    def _calculate_convergence_confidence(self, agent_id: str) -> float:
+        """Calculate confidence score in convergence stability"""
+        history = self.alignment_histories[agent_id]
+        if len(history) < 2:
+            return 0.0
+        
+        gaps = [abs(h.perceived_control - h.actual_control) for h in history]
+        recent_gaps = gaps[-min(5, len(gaps)):]
+        
+        stability = 1.0 - (np.std(recent_gaps) / (np.mean(recent_gaps) + 1e-8))
+        trend = (recent_gaps[0] - recent_gaps[-1]) / len(recent_gaps) if len(recent_gaps) > 1 else 0
+        
+        confidence = (stability + max(0, trend)) / 2
+        return max(0.0, min(1.0, confidence))
+
+    def _generate_enhanced_report(self, start_time: float, converged_agents: set) -> Dict:
+        """Generate comprehensive operational report with propagation metrics"""
+        report = {
+            "timestamp": datetime.now().isoformat(),
+            "total_duration": time.time() - start_time,
+            "total_iterations": self.iteration_count,
+            "converged_agents_count": len(converged_agents),
+            "system_coherence": self._calculate_system_coherence(),
+            "propagation_efficiency": self._calculate_propagation_efficiency(),
+            "agent_states": {},
+            "convergence_analytics": {}
+        }
+        
+        for agent_id in self.control_models:
+            history = self.alignment_histories[agent_id]
+            if history:
+                current = history[-1]
+                report["agent_states"][agent_id] = {
+                    "current_coherence": current.coherence_score,
+                    "perceived_control": current.perceived_control,
+                    "actual_control": current.actual_control,
+                    "control_gap": abs(current.perceived_control - current.actual_control),
+                    "alignment_iterations": current.alignment_iterations,
+                    "converged": agent_id in converged_agents,
+                    "verification_confidence": self._calculate_convergence_confidence(agent_id)
+                }
+        
+        return report
+
+    def _calculate_system_coherence(self) -> float:
+        """Calculate overall system coherence score"""
+        if not self.control_models:
+            return 0.0
+        
+        coherence_scores = []
+        for agent_id in self.control_models:
+            history = self.alignment_histories[agent_id]
+            if history:
+                coherence_scores.append(history[-1].coherence_score)
+        
+        return np.mean(coherence_scores) if coherence_scores else 0.0
+
+    def _calculate_propagation_efficiency(self) -> Dict[str, float]:
+        """Calculate propagation system efficiency metrics"""
+        total_adjustments = len(self.reality_interface.adjustment_history)
+        verified_adjustments = sum(1 for adj in self.reality_interface.adjustment_history 
+                                 if adj.get('verification_score', 0) > 0.8)
+        
+        efficiency = verified_adjustments / total_adjustments if total_adjustments > 0 else 0.0
+        
+        return {
+            "verification_rate": efficiency,
+            "total_propagations": total_adjustments,
+            "high_confidence_propagations": verified_adjustments
+        }