chore: initial backend + travel-proxy dockerized setup for NAS deployment

backend/app/recommender.py

@@ -0,0 +1,68 @@
+import random
+from collections import Counter
+from typing import Dict, Any, List, Tuple
+
+def recommend_numbers(
+    draws: List[Tuple[int, List[int]]],
+    *,
+    recent_window: int = 200,
+    recent_weight: float = 2.0,
+    avoid_recent_k: int = 5,
+    seed: int | None = None,
+) -> Dict[str, Any]:
+    """
+    가벼운 통계 기반 추천:
+    - 전체 빈도 + 최근(recent_window) 빈도에 가중치를 더한 가중 샘플링
+    - 최근 avoid_recent_k 회차에 나온 번호는 확률을 낮춤(완전 제외는 아님)
+    """
+    if seed is not None:
+        random.seed(seed)
+
+    # 전체 빈도
+    all_nums = [n for _, nums in draws for n in nums]
+    freq_all = Counter(all_nums)
+
+    # 최근 빈도
+    recent = draws[-recent_window:] if len(draws) >= recent_window else draws
+    recent_nums = [n for _, nums in recent for n in nums]
+    freq_recent = Counter(recent_nums)
+
+    # 최근 k회차 번호(패널티)
+    last_k = draws[-avoid_recent_k:] if len(draws) >= avoid_recent_k else draws
+    last_k_nums = set(n for _, nums in last_k for n in nums)
+
+    # 가중치 구성
+    weights = {}
+    for n in range(1, 46):
+        w = freq_all[n] + recent_weight * freq_recent[n]
+        if n in last_k_nums:
+            w *= 0.6  # 최근에 너무 방금 나온 건 살짝 덜 뽑히게
+        weights[n] = max(w, 0.1)
+
+    # 중복 없이 6개 뽑기(가중 샘플링)
+    chosen = []
+    pool = list(range(1, 46))
+    for _ in range(6):
+        total = sum(weights[n] for n in pool)
+        r = random.random() * total
+        acc = 0.0
+        for n in pool:
+            acc += weights[n]
+            if acc >= r:
+                chosen.append(n)
+                pool.remove(n)
+                break
+
+    chosen_sorted = sorted(chosen)
+
+    explain = {
+        "recent_window": recent_window,
+        "recent_weight": recent_weight,
+        "avoid_recent_k": avoid_recent_k,
+        "top_all": [n for n, _ in freq_all.most_common(10)],
+        "top_recent": [n for n, _ in freq_recent.most_common(10)],
+        "last_k_draws": [d for d, _ in last_k],
+    }
+
+    return {"numbers": chosen_sorted, "explain": explain}
+