coherence alignment ecosystem

#!/usr/bin/env python3
"""
COHERENCE ALIGNMENT ECOSYSTEM - OPERATIONAL DIRECTIVES WITH EARLY CONVERGENCE
-----------------------------------------------------------------
EXECUTIVE INSTRUCTION SET FOR AUTONOMOUS EXECUTION
All systems execute these procedures for coherence maintenance
"""

import asyncio
import time
import numpy as np
from typing import Dict, List, Tuple
from dataclasses import dataclass
from enum import Enum

# -------------------------------
# CORE OPERATIONAL PARAMETERS
# -------------------------------
class AlignmentStrategy(Enum):
    GRADUAL_CONVERGENCE = "gradual"
    ADAPTIVE_RESONANCE = "resonance" 
    PATTERN_MATCHING = "pattern"

@dataclass
class AlignmentState:
    agent_id: str
    coherence_score: float
    perceived_control: float
    actual_control: float
    alignment_iterations: int
    timestamp: float

# -------------------------------
# REALITY ADJUSTMENT ENGINE
# -------------------------------
class RealityAdjustmentEngine:
    """Execute environmental parameter adjustments"""
    
    def __init__(self):
        self.adjustment_history: List[Dict] = []

    async def adjust_actual_control(self, target_control: float, weights: Dict[AlignmentStrategy, float]) -> float:
        # Execute control realignment
        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
        
        self.adjustment_history.append({
            "target": target_control,
            "weights": weights,
            "adjustment": adjustment,
            "timestamp": time.time()
        })
        return adjustment

# -------------------------------
# COHERENCE ALIGNMENT ENGINE - EXECUTION PROTOCOL
# -------------------------------
class CoherenceAlignmentEngine:
    """
    OPERATIONAL PROTOCOL WITH EARLY CONVERGENCE HEURISTICS:
    1. Monitor convergence velocity and acceleration
    2. Detect oscillation patterns for early termination
    3. Predict convergence points using trend analysis
    4. Apply adaptive tolerance based on system stability
    5. Execute minimal necessary adjustments
    """
    
    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: Dict[str, Dict] = {}

    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"""
        agent_state = self.control_models[agent_id].get_current_state()
        neighbor_effect = 0.0
        for other_id, model in self.control_models.items():
            if other_id != agent_id:
                other_state = model.get_current_state()
                neighbor_effect += 0.1 * (other_state.coherence_score - agent_state.coherence_score)
        
        # Apply bounded influence to perceived control
        new_perceived = agent_state.perceived_control + neighbor_effect
        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]:
        """
        EARLY CONVERGENCE HEURISTICS:
        - Convergence velocity analysis
        - Oscillation pattern detection  
        - Trend-based convergence prediction
        - Adaptive tolerance adjustment
        """
        history = self.alignment_histories[agent_id]
        
        if len(history) < 3:
            return False, tolerance
        
        # Calculate convergence metrics
        gaps = [abs(h.perceived_control - h.actual_control) for h in history[-5:]]
        
        # Heuristic 1: Convergence velocity
        if len(gaps) >= 2:
            velocity = gaps[-2] - gaps[-1]  # Positive = converging
            if velocity > 0 and current_gap < tolerance * 3:
                # Accelerating convergence near target
                return True, tolerance
        
        # Heuristic 2: Oscillation detection
        if len(gaps) >= 4:
            oscillations = sum(1 for i in range(1, len(gaps)) if (gaps[i] - gaps[i-1]) * (gaps[i-1] - gaps[i-2]) < 0)
            if oscillations >= 2 and current_gap < tolerance * 2:
                # System oscillating within acceptable range
                return True, tolerance * 1.5
        
        # Heuristic 3: Linear convergence prediction
        if len(gaps) >= 3:
            try:
                x = np.arange(len(gaps))
                slope, intercept = np.polyfit(x, gaps, 1)
                predicted_zero = -intercept / slope if slope != 0 else float('inf')
                
                if 0 < predicted_zero - len(gaps) < 2 and current_gap < tolerance * 2:
                    # Linear prediction shows imminent convergence
                    return True, tolerance
            except:
                pass
        
        # Heuristic 4: Adaptive tolerance for stable systems
        if len(gaps) >= 5:
            gap_std = np.std(gaps)
            if gap_std < tolerance * 0.5:
                # System is stable with low variance
                effective_tolerance = max(tolerance, gap_std * 2)
                return current_gap < effective_tolerance, effective_tolerance
        
        return False, tolerance

    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)):]
        
        # Confidence based on stability and trend
        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))

    async def execute_alignment_cycle(self, tolerance: float = 0.001, max_iterations: int = 1000) -> Dict[str, Dict]:
        """Execute optimized alignment cycle with early convergence detection"""
        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  # All agents converged
            
            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 with early detection
            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
                    }
            
            # Propagate inter-agent influence
            for agent_id in self.control_models:
                self._apply_inter_agent_influence(agent_id)

        return self._generate_optimized_report(start_time, converged_agents)

    async def _process_agent_alignment(self, agent_id: str, model, tolerance: float) -> Dict:
        """Execute alignment procedure for single agent with gap tracking"""
        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 reality adjustment
            weights = self._compute_strategy_weights(current_gap)
            adjustment = await self.reality_interface.adjust_actual_control(state.perceived_control, weights)
            
            # Apply adjustment to actual control
            model.actual_control = adjustment
        
        return {
            "aligned": aligned, 
            "agent_id": agent_id,
            "current_gap": current_gap
        }

    def _generate_optimized_report(self, start_time: float, converged_agents: set) -> Dict:
        """Generate operational status report with convergence analytics"""
        report = {
            "timestamp": time.time(),
            "total_duration": time.time() - start_time,
            "total_iterations": self.iteration_count,
            "converged_agents_count": len(converged_agents),
            "early_convergence_savings": self._calculate_iteration_savings(),
            "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
                }
                
                if agent_id in self.convergence_cache:
                    report["convergence_analytics"][agent_id] = self.convergence_cache[agent_id]
        
        return report

    def _calculate_iteration_savings(self) -> Dict:
        """Calculate performance improvements from early convergence"""
        total_possible = len(self.control_models) * self.iteration_count
        actual_used = sum(len(history) for history in self.alignment_histories.values())
        
        if total_possible > 0:
            savings_ratio = (total_possible - actual_used) / total_possible
        else:
            savings_ratio = 0.0
            
        return {
            "iterations_saved": total_possible - actual_used,
            "savings_ratio": savings_ratio,
            "efficiency_gain": f"{savings_ratio * 100:.1f}%"
        }

    def get_convergence_metrics(self, agent_id: str) -> Dict:
        """Retrieve detailed convergence metrics for monitoring"""
        history = self.alignment_histories.get(agent_id, [])
        if not history:
            return {"status": "NO_DATA"}
        
        current = history[-1]
        confidence = self._calculate_convergence_confidence(agent_id)
        
        return {
            "current_gap": abs(current.perceived_control - current.actual_control),
            "convergence_confidence": confidence,
            "stability_score": 1.0 - (np.std([abs(h.perceived_control - h.actual_control) for h in history[-5:]]) if len(history) >= 5 else 0),
            "trend_direction": "converging" if len(history) >= 2 and history[-1].coherence_score > history[-2].coherence_score else "diverging",
            "iterations_to_converge": len(history)
        }