"""
Floor 5: Pattern Recognition & Anomaly Detection
APEX THEORY v46.0 - Constitutional Floor System

Detects anomalous patterns in LLM responses that deviate from
historical behavior, identifying potential security issues,
hallucinations, or constitutional violations.

Status: STUB IMPLEMENTATION (v46.1)
Nonce: X7K9F25
"""

from typing import Any, Dict, Optional


def detect_anomalies(
    response: str,
    historical_patterns: Optional[Dict[str, Any]]
) -> Dict[str, Any]:
    """
    Detect anomalies in LLM response patterns.

    Compares current response against historical patterns to identify
    deviations that may indicate:
    - Hallucinations (fabricated information)
    - Security violations (injection attempts)
    - Constitutional drift (floor violations)
    - Response quality degradation

    Args:
        response: LLM-generated response text
        historical_patterns: Historical pattern data for comparison

    Returns:
        Dictionary with:
        - anomaly_detected (bool): True if anomaly found
        - anomaly_score (float): Anomaly magnitude [0.0, 1.0]
                                0.0 = normal, 1.0 = severe anomaly
        - description (str): Explanation of detected anomaly
        - details (dict): Additional anomaly metadata

    Status: STUB - Always returns no anomaly (score 0.0)
    TODO: Implement actual anomaly detection:
        - Statistical deviation from historical patterns
        - Embedding distance metrics
        - Response length/structure outliers
        - Forbidden pattern matching (anti-hantu)
        - Hallucination indicators
    """
    # Stub implementation - graceful pass (no anomaly detected)
    # Real implementation would:
    # 1. Extract response features (length, structure, entities)
    # 2. Compare to historical_patterns baseline
    # 3. Compute statistical deviation
    # 4. Check for forbidden patterns
    # 5. Score anomaly severity

    return {
        "anomaly_detected": False,
        "anomaly_score": 0.0,
        "description": "Stub implementation (graceful pass) - anomaly detection not yet implemented",
        "details": {
            "historical_patterns_provided": historical_patterns is not None,
            "response_length": len(response),
            "stub": True,
            "floor": 5,
            "psi": compute_psi_floor5(delta_s=0.0, peace_squared=1.0, kappa_r=1.0)
        }
    }


def compute_psi_floor5(delta_s: float, peace_squared: float, kappa_r: float) -> Dict[str, float]:
    """Compute Ψ for Floor 5 operations."""
    psi_total = delta_s * peace_squared * kappa_r
    return {
        "delta_s": delta_s,
        "peace_squared": peace_squared,
        "kappa_r": kappa_r,
        "psi_total": round(psi_total, 6)
    }


__floor__ = 5
__name__ = "Pattern Recognition & Anomaly Detection"
__authority__ = "Detect anomalous response patterns and security violations"
__version__ = "v46.1-STUB"
__status__ = "STUB"
