"""Personalized learning path planning helper.
中文：学习路径规划业务逻辑，基于先修图和掌握度阈值推荐下一步知识点。
"""
from __future__ import annotations
from collections import deque
from typing import Dict, List
from ...pydantic_models import PathResponse
# 示例先修图，可替换为数据库或配置加载
adjacency_list: Dict[str, List[str]] = {
"K1": ["K2", "K3"],
"K2": ["K4"],
"K3": ["K4", "K5"],
"K4": [],
"K5": [],
}
def plan_learning_path(mastery: Dict[str, float], threshold: float, max_recommend: int) -> PathResponse:
"""基于拓扑排序 + 掌握度筛选规划学习路径。"""
indegree: Dict[str, int] = {k: 0 for k in adjacency_list}
for prereq, next_list in adjacency_list.items():
for nxt in next_list:
indegree[nxt] = indegree.get(nxt, 0) + 1
queue = deque([k for k, deg in indegree.items() if deg == 0])
recommended: List[str] = []
visited = set()
while queue and len(recommended) < max_recommend:
cur = queue.popleft()
visited.add(cur)
if mastery.get(cur, 0.0) < threshold:
recommended.append(cur)
if len(recommended) >= max_recommend:
break
for nxt in adjacency_list.get(cur, []):
indegree[nxt] -= 1
if indegree[nxt] == 0 and nxt not in visited:
queue.append(nxt)
return PathResponse(recommended_path=recommended)
