From f17b7c249e52f8c39597583cf6f8a6f586c5fbb2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=EC=95=88=ED=9D=AC=EC=B0=AC?= <kashn4655@gmail.com>
Date: Thu, 6 Aug 2026 16:01:34 +0900
Subject: [PATCH] simulate: annealing algo best version

---
 algorithm/deploy.py | 207 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 207 insertions(+)
 create mode 100644 algorithm/deploy.py

diff --git a/algorithm/deploy.py b/algorithm/deploy.py
new file mode 100644
index 0000000..6f3f986
--- /dev/null
+++ b/algorithm/deploy.py
@@ -0,0 +1,207 @@
+"""
+사과게임 배포 모델 (best) — worst-이웃 Simulated Annealing + 병렬 max.
+
+성능 (실측):
+  - 랜덤 보드 평균 ~135 (max-of-10, 2800 iter, 판당 ~70초)
+  - 판별로 near-최적 (추정 천장 대비 -2 정도)
+  - 여러 독립 방법(빔·MCTS·부분 브루트포스) 중 최고
+
+구조:
+  1) 해 = 한 판의 액션 시퀀스 (액션 = 합10 최소 사각형 (r1,c1,r2,c2))
+  2) 이웃 = 'worst' 지점(작은 수를 낭비한 수)을 골라 다른 유효수로 갈아타 → 뒤를 재플레이
+  3) rollout 정책 = 적게 지우기(딱 맞는 짝 우선)
+  4) 수락 = Metropolis (온도 선형 냉각)
+  5) 병렬 = 같은 보드를 여러 인스턴스(rng만 다름) 돌려 최고 선택
+
+사용:
+    from algorithm.deploy import make_board, deploy
+    board = make_board(1234)
+    score, sequence = deploy(board, iters=2800, instances=10)
+
+    # 또는 CLI
+    python -m algorithm.deploy --seed 1234 --iters 2800 --instances 10
+"""
+from __future__ import annotations
+import math
+import random
+import argparse
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+
+import numpy as np
+
+ROWS, COLS = 9, 18
+TOTAL = ROWS * COLS
+
+
+# ────────────────────────── 보드 유틸 ──────────────────────────
+def make_board(seed: int) -> np.ndarray:
+    """시드로 9×18 보드 생성 (값 1-9)."""
+    return np.random.default_rng(seed).integers(1, 10, size=(ROWS, COLS), dtype=np.int8)
+
+
+def _prefix(grid: np.ndarray) -> np.ndarray:
+    """2D 누적합 (임의 사각형 합을 O(1)에)."""
+    P = np.zeros((ROWS + 1, COLS + 1), dtype=np.int32)
+    P[1:, 1:] = np.cumsum(np.cumsum(grid, axis=0), axis=1)
+    return P
+
+
+def valid_actions(grid: np.ndarray) -> list[tuple[int, int, int, int]]:
+    """합=10인 '최소 사각형' 유효 수 목록 (테두리 4변이 비지 않은 것)."""
+    P = _prefix(grid)
+
+    def area(r1, c1, r2, c2):
+        return int(P[r2 + 1, c2 + 1] - P[r1, c2 + 1] - P[r2 + 1, c1] + P[r1, c1])
+
+    res = []
+    for r1 in range(ROWS):
+        for r2 in range(r1, ROWS):
+            for c1 in range(COLS):
+                for c2 in range(c1, COLS):
+                    s = area(r1, c1, r2, c2)
+                    if s == 10:
+                        if area(r1, c1, r1, c2) == 0: continue   # 위 변
+                        if area(r1, c2, r2, c2) == 0: continue   # 오른 변
+                        if area(r2, c1, r2, c2) == 0: continue   # 아래 변
+                        if area(r1, c1, r2, c1) == 0: continue   # 왼 변
+                        res.append((r1, c1, r2, c2))
+                    elif s > 10:
+                        break   # c2 늘리면 합만 커짐 → 가지치기
+    return res
+
+
+def apply_move(grid: np.ndarray, mv) -> int:
+    """mv 사각형의 남은 사과 제거 (in-place). 제거 칸 수 반환."""
+    r1, c1, r2, c2 = mv
+    region = grid[r1:r2 + 1, c1:c2 + 1]
+    cleared = int(np.count_nonzero(region))
+    region[:] = 0
+    return cleared
+
+
+# ────────────────────────── rollout 정책 ──────────────────────────
+def _fewest_cells(grid: np.ndarray, actions):
+    """적게 지우는(딱 맞는 짝) 수 선택 — 검증된 최선의 rollout 정책."""
+    best, best_cells = actions[0], 1 << 30
+    for a in actions:
+        r1, c1, r2, c2 = a
+        cells = int(np.count_nonzero(grid[r1:r2 + 1, c1:c2 + 1]))
+        if cells < best_cells:
+            best_cells, best = cells, a
+    return best
+
+
+def _rollout(grid, first_action, rng, greedy_prob):
+    """first_action 두고 끝까지 플레이. (시퀀스, 총 제거) 반환. grid 소모됨."""
+    seq, total = [], 0
+    a = first_action
+    while a is not None:
+        total += apply_move(grid, a)
+        seq.append(a)
+        acts = valid_actions(grid)
+        if not acts:
+            break
+        a = acts[rng.randrange(len(acts))] if rng.random() >= greedy_prob else _fewest_cells(grid, acts)
+    return seq, total
+
+
+def _build_prefix(board, actions):
+    """각 시점 (보드 스냅샷, 누적 제거) 캐싱."""
+    grid = board.copy()
+    pgrid, pclear, acc = [grid.copy()], [0], 0
+    for a in actions:
+        acc += apply_move(grid, a)
+        pgrid.append(grid.copy())
+        pclear.append(acc)
+    return pgrid, pclear
+
+
+def _pick_worst(n, pc, rng):
+    """worst 이웃 지점: 딱 맞는 짝(2칸) 초과로 '낭비'한 수일수록 우선 수정."""
+    w = [(pc[i + 1] - pc[i] - 2) ** 2 + 0.1 for i in range(n)]
+    tot = sum(w)
+    r = rng.random() * tot
+    acc = 0.0
+    for i, wi in enumerate(w):
+        acc += wi
+        if acc >= r:
+            return i
+    return n - 1
+
+
+# ────────────────────────── 어닐링 ──────────────────────────
+def anneal_once(board, iters=2800, rng_seed=0, T0=3.0, greedy_prob=0.8):
+    """단일 어닐링 실행. (best_score, best_sequence) 반환."""
+    rng = random.Random(rng_seed)
+    acts = valid_actions(board)
+    if not acts:
+        return 0, []
+    cur_seq, cur_score = _rollout(board.copy(), _fewest_cells(board, acts), rng, 1.0)
+    best_seq, best_score = cur_seq, cur_score
+    pg, pc = _build_prefix(board, cur_seq)
+
+    for it in range(iters):
+        T = T0 * (1 - it / iters) + 1e-6
+        if len(cur_seq) < 2:
+            break
+        t = _pick_worst(len(cur_seq), pc, rng)
+        base_g = pg[t]
+        acts = valid_actions(base_g)
+        if len(acts) < 2:
+            continue
+        cur_a = cur_seq[t]
+        alt = [a for a in acts if a != cur_a]
+        if not alt:
+            continue
+        nf = alt[rng.randrange(len(alt))]
+        tail_seq, tail_total = _rollout(base_g.copy(), nf, rng, greedy_prob)
+        new_score = pc[t] + tail_total
+        delta = new_score - cur_score
+        if delta >= 0 or rng.random() < math.exp(delta / T):
+            cur_seq = cur_seq[:t] + tail_seq
+            cur_score = new_score
+            pg, pc = _build_prefix(board, cur_seq)
+            if cur_score > best_score:
+                best_seq, best_score = cur_seq, cur_score
+    return best_score, best_seq
+
+
+def _worker(arg):
+    board, iters, rng_seed, T0, gp = arg
+    return anneal_once(board, iters, rng_seed, T0, gp)
+
+
+def deploy(board, iters=2800, instances=10, T0=3.0, greedy_prob=0.8):
+    """배포 모델: instances개 어닐링(같은 보드, rng만 다름) 병렬 → 최고 선택.
+    (score, sequence) 반환. 병렬은 spawn 컨텍스트로 macOS fork+numpy 데드락 회피."""
+    args = [(board, iters, i, T0, greedy_prob) for i in range(instances)]
+    if instances == 1:
+        return _worker(args[0])
+    ctx = mp.get_context("spawn")
+    with ProcessPoolExecutor(max_workers=instances, mp_context=ctx) as ex:
+        results = list(ex.map(_worker, args))
+    return max(results, key=lambda r: r[0])
+
+
+# ────────────────────────── CLI ──────────────────────────
+def main():
+    p = argparse.ArgumentParser(description="사과게임 배포 모델 (worst-SA + 병렬 max)")
+    p.add_argument("--seed", type=int, default=1234, help="보드 시드")
+    p.add_argument("--iters", type=int, default=2800)
+    p.add_argument("--instances", type=int, default=10)
+    p.add_argument("--show-seq", action="store_true", help="수순 출력")
+    args = p.parse_args()
+
+    board = make_board(args.seed)
+    score, seq = deploy(board, iters=args.iters, instances=args.instances)
+    left = TOTAL - score
+    print(f"seed {args.seed}: {score}/{TOTAL}  ({len(seq)}수, 남은 {left}칸)  "
+          f"[합계 {int(board.sum())}, 이론상 최대 {162 if board.sum() % 10 == 0 else 161}]")
+    if args.show_seq:
+        for i, a in enumerate(seq, 1):
+            print(f"  {i:2d}. {tuple(int(x) for x in a)}")
+
+
+if __name__ == "__main__":
+    main()