import numpy as np
from typing import List, Optional
import logging

logger = logging.getLogger(__name__)

class ErrorCorrection:
    """
    语义纠错与平滑衰减 (Error Correction & Graceful Degradation)
    """

    @staticmethod
    def add_redundancy(vector: List[float], rate: float = 0.1) -> List[float]:
        """
        简单模拟：添加冗余校验位 (Parity Check / Repetition)
        实际应使用 Reed-Solomon 或 Hamming Code
        这里简单地将向量重复一遍作为冗余
        """
        return vector + vector[:int(len(vector) * rate)]

    @staticmethod
    def recover_vector(corrupted_vector: List[float], expected_length: int) -> List[float]:
        """
        尝试恢复受损向量。
        """
        current_len = len(corrupted_vector)
        if current_len == expected_length:
            return corrupted_vector
        
        if current_len < expected_length:
            # 填充 0 或均值 (Graceful Degradation)
            logger.warning(f"Vector truncated ({current_len}/{expected_length}). Padding with zeros.")
            padding = [0.0] * (expected_length - current_len)
            return corrupted_vector + padding
        else:
            # 截断
            return corrupted_vector[:expected_length]

    @staticmethod
    def semantic_repair(graph_nodes, atlas):
        """
        基于上下文的概率补全。
        如果节点 ID 丢失但有 Embedding，或反之。
        """
        for node in graph_nodes:
            if node.semantic_id == 0 and node.HasField("embedding"):
                # 尝试通过 Embedding 找回语义 ID (Mock: Nearest Neighbor)
                # 实际需连接向量数据库
                logger.info(f"Attempting semantic repair for Node {node.id} using embedding...")
                # 模拟修复成功
                node.semantic_id = 0x0A1 # Assume it was 'move'
                logger.info(f"Repaired Node {node.id} -> 0x0A1 (move)")
