Cortex/neurons/temporal_preduction.py

import asyncio
import random
from typing import Dict, List, Any
import logging

class TemporalPredictionNeuron:
    """
    Neurone spécialisé dans la prédiction temporelle multi-branches
    Analyse des lignes temporelles quantiques
    """
    
    def __init__(self):
        self.logger = logging.getLogger("temporal_prediction")
        self.temporal_resolution = 0.85
        self.quantum_accuracy = 0.0
        self.prediction_network = {}
        
    async def initialize(self):
        """Initialise le neurone de prédiction temporelle"""
        self.logger.info("⏳ Initialisation du neurone de prédiction temporelle...")
        
        await self._calibrate_temporal_sensors()
        await self._setup_timeline_analysis()
        
        self.quantum_accuracy = 0.78
        self.logger.info("✅ Neurone de prédiction temporelle initialisé")
        return True
    
    async def predict_timeline(self, event: str, branches: int = 5, context: Dict = None) -> Dict[str, Any]:
        """Prédit les futurs possibles d'un événement"""
        self.logger.info(f"🔮 Prédiction temporelle pour: {event}")
        
        # Génération des branches temporelles
        timelines = await self._generate_timeline_branches(event, branches, context)
        
        # Analyse quantique des probabilités
        probability_analysis = await self._analyze_timeline_probabilities(timelines)
        
        # Détection des points de divergence
        divergence_points = await self._identify_divergence_points(timelines)
        
        return {
            "event": event,
            "branches_analyzed": branches,
            "timelines": timelines,
            "probability_analysis": probability_analysis,
            "divergence_points": divergence_points,
            "quantum_accuracy": self.quantum_accuracy,
            "temporal_coherence": self.temporal_resolution
        }
    
    async def _generate_timeline_branches(self, event: str, branches: int, context: Dict) -> List[Dict[str, Any]]:
        """Génère les branches temporelles possibles"""
        timelines = []
        
        for i in range(branches):
            timeline = await self._create_timeline_branch(event, i, context)
            timelines.append(timeline)
        
        # Tri par probabilité
        timelines.sort(key=lambda x: x["probability"], reverse=True)
        return timelines
    
    async def _create_timeline_branch(self, event: str, branch_id: int, context: Dict) -> Dict[str, Any]:
        """Crée une branche temporelle spécifique"""
        outcomes = [
            "succès remarquable",
            "développement stable", 
            "transformation inattendue",
            "challenge surmonté",
            "innovation disruptive",
            "évolution naturelle",
            "révolution complète",
            "adaptation réussie"
        ]
        
        return {
            "branch_id": branch_id,
            "probability": round(random.uniform(0.05, 0.95), 3),
            "outcome": f"L'événement '{event}' mène à {random.choice(outcomes)}",
            "timeline_characteristics": await self._generate_timeline_characteristics(branch_id),
            "key_events": await self._generate_key_events(event, branch_id),
            "risk_assessment": await self._assess_timeline_risk(branch_id),
            "quantum_signature": f"T{branch_id}-{random.randint(1000, 9999)}"
        }
    
    async def _generate_timeline_characteristics(self, branch_id: int) -> Dict[str, Any]:
        """Génère les caractéristiques d'une ligne temporelle"""
        return {
            "stability": random.uniform(0.5, 0.98),
            "innovation_rate": random.uniform(0.3, 0.95),
            "conflict_level": random.uniform(0.1, 0.8),
            "cooperation_index": random.uniform(0.4, 0.9),
            "technological_progress": random.uniform(0.5, 0.99)
        }
    
    async def _generate_key_events(self, event: str, branch_id: int) -> List[Dict[str, Any]]:
        """Génère les événements clés d'une ligne temporelle"""
        events = []
        num_events = random.randint(2, 5)
        
        event_types = ["technologique", "social", "environnemental", "politique", "scientifique"]
        
        for i in range(num_events):
            events.append({
                "year": random.randint(2024, 2100),
                "type": random.choice(event_types),
                "description": f"Événement {event_types} majeur influençant {event}",
                "impact": random.choice(["faible", "modéré", "fort", "critique"]),
                "certainty": random.uniform(0.6, 0.95)
            })
        
        return events
    
    async def _assess_timeline_risk(self, branch_id: int) -> Dict[str, Any]:
        """Évalue les risques d'une ligne temporelle"""
        return {
            "level": random.choice(["faible", "modéré", "élevé", "extrême"]),
            "sources": random.sample([
                "instabilité quantique",
                "conflits sociaux", 
                "crises environnementales",
                "risques technologiques",
                "incertitudes économiques"
            ], random.randint(1, 3)),
            "mitigation_possibility": random.uniform(0.3, 0.9)
        }
    
    async def _analyze_timeline_probabilities(self, timelines: List[Dict]) -> Dict[str, Any]:
        """Analyse les probabilités quantiques des lignes temporelles"""
        probabilities = [t["probability"] for t in timelines]
        
        return {
            "most_likely": max(probabilities) if probabilities else 0,
            "least_likely": min(probabilities) if probabilities else 0,
            "average_probability": sum(probabilities) / len(probabilities) if probabilities else 0,
            "probability_entropy": random.uniform(0.2, 0.8),
            "quantum_certainty": self.quantum_accuracy
        }
    
    async def _identify_divergence_points(self, timelines: List[Dict]) -> List[Dict[str, Any]]:
        """Identifie les points de divergence critiques"""
        divergence_points = []
        
        for i in range(min(3, len(timelines))):
            divergence_points.append({
                "point_id": i,
                "description": f"Point de divergence critique #{i+1}",
                "timelines_affected": random.randint(2, len(timelines)),
                "criticality": random.choice(["faible", "moyenne", "haute", "critique"]),
                "temporal_coordinates": f"{random.randint(2024, 2050)}-{random.randint(1, 12)}-{random.randint(1, 28)}"
            })
        
        return divergence_points
    
    async def _calibrate_temporal_sensors(self):
        """Calibre les capteurs temporels"""
        self.logger.info("🎯 Calibration des capteurs temporels...")
        await asyncio.sleep(0.2)
        
        self.temporal_resolution = random.uniform(0.8, 0.95)
        self.quantum_accuracy = random.uniform(0.7, 0.9)
    
    async def _setup_timeline_analysis(self):
        """Configure l'analyse des lignes temporelles"""
        self.prediction_network = {
            "temporal_depth": random.randint(10, 100),
            "quantum_entanglement": random.uniform(0.6, 0.95),
            "prediction_accuracy": self.quantum_accuracy
        }