import math
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Tuple, Any

@dataclass
class EntityState:
    """Snapshot of an entity's current physical and behavioral state."""
    entity_id: str
    position: Tuple[float, float]  # (x, y) coordinates (normalized or pixel center)
    velocity: Tuple[float, float]   # (vx, vy) change per frame
    behavior_vector: Dict[str, Any] = field(default_factory=dict)
    context: Dict[str, Any] = field(default_factory=dict)
    confidence: float = 1.0

@dataclass
class PredictionResult:
    """The output of the anticipatory logic layer."""
    entity_id: str
    risk_score: float
    predicted_motion: Tuple[float, float]
    uncertainty: float
    explanation: str

class PredictionEngine:
    """
    Anticipatory Intelligence Layer (Sentinel Phase 5).
    Estimates future risk based on temporal trends, behavior, and context.
    """
    def __init__(self, risk_threshold: float = 0.7):
        self.risk_threshold = risk_threshold

    def predict(self, state: EntityState) -> PredictionResult:
        """Estimate the future risk for a single entity."""
        
        # 1. Motion Anomaly (Velocity and strange paths)
        motion_risk = self._motion_anomaly(state)
        
        # 2. Behavioral Risk (Sequential pattern matching)
        behavior_risk = self._behavior_risk(state)
        
        # 3. Contextual Weight (Situational priors)
        context_weight = self._context_weight(state.context)

        # 4. Weighted Fusion of Risk Signals
        # 40% Motion, 40% Behavior, 20% Context
        risk_score = (
            0.4 * motion_risk +
            0.4 * behavior_risk +
            0.2 * context_weight
        )

        # 5. Uncertainty (Entropy or sensor confidence inverse)
        uncertainty = self._estimate_uncertainty(state)

        return PredictionResult(
            entity_id=state.entity_id,
            risk_score=round(risk_score, 3),
            predicted_motion=self._predict_motion(state),
            uncertainty=round(uncertainty, 3),
            explanation=self._generate_explanation(motion_risk, behavior_risk, context_weight)
        )

    def _predict_motion(self, state: EntityState) -> Tuple[float, float]:
        """Simple linear extrapolation (Kalman baseline)."""
        x, y = state.position
        vx, vy = state.velocity
        return (x + vx, y + vy)

    def _motion_anomaly(self, state: EntityState) -> float:
        """High velocity or rapid acceleration increases risk."""
        vx, vy = state.velocity
        speed = math.sqrt(vx**2 + vy**2)
        # Normalize: 10 units/frame is 'critically high speed'
        return min(speed / 10.0, 1.0)

    def _behavior_risk(self, state: EntityState) -> float:
        """Detect pre-threat behavioral sequences."""
        actions = state.behavior_vector.get("recent_actions", [])
        
        # High-risk pre-event patterns
        risky_patterns = {
            "approaching": 0.2,
            "reaching": 0.4,
            "hiding": 0.3,
            "agitated": 0.4,
            "running": 0.3,
            "loitering": 0.1,
            "drawn_weapon": 0.9 # High but still probabilistic
        }

        score = 0.0
        for action in actions:
            score += risky_patterns.get(action.lower(), 0.0)

        return min(score, 1.0)

    def _context_weight(self, context: Dict[str, Any]) -> float:
        """Prior adjustment based on location and environment."""
        location = context.get("location", "unknown")
        lighting = context.get("lighting", "normal")
        
        # Simple prior map
        if location == "kitchen": 
            return 0.2 # Lower suspicion for sharp objects
        if location == "street" and lighting == "low":
            return 0.8 # Higher suspicion in dark streets
        if location == "entryway" or location == "perimeter":
            return 0.7 # High sensitivity zones
            
        return 0.5 # Neutral prior

    def _estimate_uncertainty(self, state: EntityState) -> float:
        """Inversely proportional to detection confidence."""
        return max(0.0, 1.0 - state.confidence)

    def _generate_explanation(self, m_risk: float, b_risk: float, c_weight: float) -> str:
        factors = []
        if m_risk > 0.5: factors.append("unusual_motion")
        if b_risk > 0.5: factors.append("risky_sequence")
        if c_weight > 0.7: factors.append("sensitive_context")
        
        if not factors: return "Steady state observation."
        return "Warning: Risk elevated by " + " & ".join(factors)

# Global singleton
prediction_engine = PredictionEngine()