feat: 添加石头剪刀布预测辅助系统
实现基于马尔可夫转移、频率统计和周期检测的融合预测算法，包含主程序、回测评估脚本、README 文档、MIT 许可证及 .gitignore 配置。
这个提交包含在：
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# Python 缓存
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__pycache__/
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*.py[cod]
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*$py.class
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*.so
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# 虚拟环境
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.venv/
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venv/
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env/
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# 对局历史（运行时自动生成，含个人数据，不建议提交）
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rps_history.json
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# 编辑器 / 系统文件
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.vscode/
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.idea/
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.Ds_Store
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# 临时调试文件
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_debug*.py
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_debug_*.txt
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_verify*.py
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MIT License
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Copyright (c) 2026 wangchuanli
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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# 猜拳预测辅助系统 (Rock-Paper-Scissors Predictor)
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一个基于序列预测的「石头剪刀布」辅助 AI。它通过分析对手的历史出手习惯，
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融合多种预测模型，给出下一局克制对手的推荐出手。
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> ⚠️ 说明：若对手**完全随机**出拳，任何算法胜率都趋近于 33.3%，无法被预测。
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> 本系统的价值在于识别**有习惯 / 有模式**的对手（如偏好某手、固定循环、受上一手影响）。
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## 原理
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系统同时使用三种互补的预测模型，并对它们融合投票：
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| 模型 | 思路 | 适用场景 |
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| --- | --- | --- |
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| 马尔可夫一阶转移 | 统计「上一手 → 这一手」的转移频率，预测下一手 | 对手受上一手影响 |
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| 频率统计 | 预测对手最常出的手 | 对手有固定偏好 |
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| 周期检测 | 检测对手是否按固定长度循环出拳 | 对手按循环模式 |
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融合策略：三模型**投票（少数服从多数）**；平票时按置信度加权选择。
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最终根据预测的对手出手，推荐能克制它的一手（`win_map`）。
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转移矩阵使用 **拉普拉斯平滑**（`np.ones((3,3))` 初始化），避免未见过的转移概率为 0。
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## 安装
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```bash
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pip install -r requirements.txt
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```
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## 使用
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```bash
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python rock-paper-scissors.py
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```
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按提示输入对手的出手：
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- `1` = 拳头
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- `2` = 剪刀
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- `3` = 布
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- `q` = 退出
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每局程序会输出：本局胜负、AI 预测的对手下一手、推荐的出手，以及累积统计。
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历史记录会持久化到 `rps_history.json`。
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## 评估 / 回测
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`evaluate.py` 用多种模拟对手（随机、偏好、循环、马尔可夫依赖）对算法做回测，
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报告胜率，验证算法在「有模式」的对手上显著优于随机基线（约 33.3% 的随机胜率）。
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```bash
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python evaluate.py
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```
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示例输出（seed=42, 各 2000 局）：
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| 对手类型 | 预测AI胜率 | 随机基线 | 说明 |
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| --- | --- | --- | --- |
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| 完全随机 | ~33.6% | ~33.3% | 无模式，算法不占优（符合预期） |
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| 偏好出布(60%) | ~66.8% | ~33.3% | 频率模型生效 |
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| 固定循环(拳头→剪刀→布) | ~99.8% | ~33.3% | 周期检测完美工作 |
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| 一阶马尔可夫依赖 | ~47.9% | ~33.3% | 转移矩阵学习生效 |
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> 结论：算法只在对手**存在可学习模式**时显著优于随机；面对完全随机的对手，
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> 胜率收敛到理论值，不会过拟合或作弊。
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## 文件结构
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```
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rock-paper-scissors.py 主程序（人机交互 + 预测核心）
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evaluate.py 回测/评估脚本
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rps_history.json 对局历史记录（自动生成）
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requirements.txt 依赖
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README.md 说明文档
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```
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## License
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MIT
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"""
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回测 / 评估脚本：用多种模拟对手验证猜拳预测算法的有效性。
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对比两组策略：
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- 预测 AI：根据历史推荐克制对手的出手
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- 随机基线：完全随机出手
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胜率定义：(胜局数) / (总对局数)。平局不计入胜率分子。
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理想情况下，面对「有模式」的对手，预测 AI 胜率应显著高于 33.3% 的随机基线。
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"""
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import random
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import importlib.util
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import numpy as np
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from collections import deque
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# 主文件名含连字符，无法直接 import，用 importlib 加载
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_spec = importlib.util.spec_from_file_location("rps_main", "rock-paper-scissors.py")
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_rps = importlib.util.module_from_spec(_spec)
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_spec.loader.exec_module(_rps)
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RPSAssistant = _rps.RPSAssistant
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win_map = _rps.win_map
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moves = {1: "拳头", 2: "剪刀", 3: "布"}
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# =============================
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# 模拟对手生成器
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# =============================
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def opponent_random(n):
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"""完全随机的对手（算法不应有优势）。"""
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return [random.choice([1, 2, 3]) for _ in range(n)]
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def opponent_bias(n, fav=3, p=0.6):
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"""有固定偏好的对手：以概率 p 出 fav，否则随机。"""
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return [fav if random.random() < p else random.choice([1, 2, 3]) for _ in range(n)]
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def opponent_cycle(n, pattern=(1, 2, 3)):
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"""按固定循环出拳的对手。"""
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return [pattern[i % len(pattern)] for i in range(n)]
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def opponent_markov(n, trans=None, start=1):
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"""一阶马尔可夫依赖的对手：下一手受上一手影响。"""
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if trans is None:
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# 行 i：上一手为 i 时，下一手的转移概率（拳头/剪刀/布）
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trans = {
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1: [0.6, 0.2, 0.2],
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2: [0.2, 0.6, 0.2],
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3: [0.2, 0.2, 0.6],
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}
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seq = [start]
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for _ in range(n - 1):
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prev = seq[-1]
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seq.append(int(np.random.choice([1, 2, 3], p=trans[prev])))
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return seq
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# =============================
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# 评估逻辑
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# =============================
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def simulate(enemy_seq, use_predictor=True):
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"""用给定对手序列跑一遍，返回 (胜局, 负局, 平局)。"""
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assistant = RPSAssistant()
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wins = losses = draws = 0
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my_move = random.choice([1, 2, 3]) # 首局随机
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for enemy in enemy_seq:
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if use_predictor:
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_, rec = assistant.record_opponent_move(enemy, my_move=my_move)
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my_move = rec
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else:
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my_move = random.choice([1, 2, 3])
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# 仍需推进 assistant 以统计，但 my_move 不参与预测
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assistant.record_opponent_move(enemy, my_move=my_move)
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# 判定本局
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if win_map[my_move] == enemy:
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wins += 1
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elif win_map[enemy] == my_move:
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losses += 1
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else:
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draws += 1
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return wins, losses, draws
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def report(name, enemy_seq):
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n = len(enemy_seq)
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w_pred, l_pred, d_pred = simulate(enemy_seq, use_predictor=True)
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w_rand, l_rand, d_rand = simulate(enemy_seq, use_predictor=False)
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pred_rate = w_pred / n * 100
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rand_rate = w_rand / n * 100
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print(f"对手类型: {name} (共 {n} 局)")
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print(f" 预测AI -> 胜 {w_pred} / 负 {l_pred} / 平 {d_pred} | 胜率 {pred_rate:.1f}%")
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print(f" 随机基线 -> 胜 {w_rand} / 负 {l_rand} / 平 {d_rand} | 胜率 {rand_rate:.1f}%")
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print(f" 提升 -> {pred_rate - rand_rate:+.1f} 个百分点\n")
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if __name__ == "__main__":
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N = 2000
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random.seed(42)
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np.random.seed(42)
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report("完全随机", opponent_random(N))
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report("偏好出布(60%)", opponent_bias(N, fav=3, p=0.6))
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report("固定循环(拳头->剪刀->布)", opponent_cycle(N))
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report("一阶马尔可夫依赖", opponent_markov(N))
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numpy>=1.21.0
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import random
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from collections import deque, Counter
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import numpy as np
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import json
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import os
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moves = {1: "拳头", 2: "剪刀", 3: "布"}
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win_map = {1: 2, 2: 3, 3: 1} # key克制value
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HISTORY_FILE = "rps_history.json"
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class RPSAssistant:
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def __init__(self, memory_size=15, cycle_maxlen=5):
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self.enemy_history = deque(maxlen=memory_size)
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self.recent_results = deque(maxlen=5)
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# 马尔可夫一阶转移矩阵，加1做拉普拉斯平滑，避免零概率
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self.transition_matrix = np.ones((3, 3))
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self.cycle_maxlen = cycle_maxlen
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self.W_count = 0 # 当前连胜
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self.total_wins = 0
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self.total_losses = 0
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self.history_records = []
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if os.path.exists(HISTORY_FILE):
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with open(HISTORY_FILE, "r") as f:
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self.history_records = json.load(f)
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# 初始化统计与转移矩阵
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for r in self.history_records:
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if r.get("本局结果") == "胜利":
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self.total_wins += 1
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elif r.get("本局结果") == "失败":
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self.total_losses += 1
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self._rebuild_transition_matrix()
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def _rebuild_transition_matrix(self):
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"""根据存档中的对手出手序列重建转移矩阵（用于从存档恢复学习状态）。"""
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self.transition_matrix = np.ones((3, 3))
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hist = [r["对手出手"] for r in self.history_records]
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self.enemy_history = deque(hist, maxlen=self.enemy_history.maxlen)
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for i in range(len(hist) - 1):
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prev, nxt = hist[i], hist[i + 1]
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self.transition_matrix[prev - 1][nxt - 1] += 1
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def save_history(self):
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for record in self.history_records:
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for k, v in record.items():
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if isinstance(v, np.integer):
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record[k] = int(v)
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with open(HISTORY_FILE, "w") as f:
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json.dump(self.history_records, f, ensure_ascii=False, indent=2)
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def markov_predict(self):
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"""基于上一手的一阶马尔可夫预测，返回 (预测手, 置信度)。
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置信度 = 最大转移概率（0~1），用于融合时衡量该预测的可信程度。
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置信度过低时返回 None，表示「无明显依赖」。
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"""
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if len(self.enemy_history) < 2:
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return None, 0.0
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last = self.enemy_history[-1]
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row = self.transition_matrix[last - 1]
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probs = row / row.sum()
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best = int(np.argmax(probs)) + 1
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confidence = float(probs.max())
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# 至少需要一定样本且明显偏向某一手才认为可信
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if confidence < 0.45:
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return None, confidence
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return best, confidence
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def frequency_predict(self):
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"""基于频率统计的预测，返回 (预测手, 置信度)。
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置信度 = 最高频手占比。当各手频率接近（如对手均衡出拳）时，
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置信度低，不应强行给出虚假偏好信号。
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"""
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if len(self.enemy_history) < 3:
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return None, 0.0
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count = Counter(self.enemy_history)
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total = sum(count.values())
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most, cnt = count.most_common(1)[0]
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confidence = cnt / total
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if confidence < 0.45:
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return None, confidence
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return most, confidence
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def cycle_predict(self):
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"""基于周期/循环模式的预测，返回 (预测手, 置信度) 或 (None, 0.0)。
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在末尾历史中寻找重复出现的固定长度片段，据其推断下一手。
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"""
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hist = list(self.enemy_history)
|
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max_L = min(self.cycle_maxlen, len(hist) // 2)
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for L in range(2, max_L + 1):
|
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pattern = hist[-L:]
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for start in range(0, len(hist) - 2 * L + 1):
|
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if hist[start:start + L] == pattern:
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return hist[start + L], 1.0
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return None, 0.0
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def predict_enemy(self):
|
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"""融合多种带置信度的预测。
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|
||||
策略：收集所有「有效（置信度达标）」的预测，按置信度加权随机选取；
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若没有任何有效预测，则退化为均匀随机（避免虚假信号）。
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"""
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candidates = []
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for pred_fn in (self.markov_predict, self.frequency_predict, self.cycle_predict):
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move, conf = pred_fn()
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if move is not None:
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candidates.append((move, conf))
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if not candidates:
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return random.choice([1, 2, 3])
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moves_list, weights = zip(*candidates)
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||||
# 归一化权重
|
||||
total_w = sum(weights)
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norm_weights = [w / total_w for w in weights]
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return int(np.random.choice(moves_list, p=norm_weights))
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||||
def recommend_move(self, predicted_enemy):
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"""根据预测对手出手，推荐能克制它的一手。"""
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if self.W_count >= 1:
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move = [k for k, v in win_map.items() if v == predicted_enemy][0]
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else:
|
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lose_count = sum([1 for r in self.recent_results if r == "失败"])
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||||
if lose_count >= 2 and random.random() < 0.3:
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move = random.choice([1, 2, 3])
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||||
else:
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move = [k for k, v in win_map.items() if v == predicted_enemy][0]
|
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return move
|
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def record_opponent_move(self, enemy_move, my_move):
|
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# 基于「已知历史」预测对手的下一手（此时 enemy_history 尚未包含当前手）
|
||||
predicted_next = self.predict_enemy()
|
||||
recommended_move = self.recommend_move(predicted_next)
|
||||
|
||||
# 用「上一手 -> 当前手」更新转移矩阵，并写入历史
|
||||
if self.enemy_history:
|
||||
prev = self.enemy_history[-1]
|
||||
self.transition_matrix[prev - 1][enemy_move - 1] += 1
|
||||
self.enemy_history.append(enemy_move)
|
||||
|
||||
# 判断本局胜负
|
||||
if win_map[my_move] == enemy_move:
|
||||
result = "胜利"
|
||||
self.W_count += 1
|
||||
self.total_wins += 1
|
||||
elif win_map[enemy_move] == my_move:
|
||||
result = "失败"
|
||||
self.W_count = 0
|
||||
self.total_losses += 1
|
||||
else:
|
||||
result = "平局"
|
||||
self.W_count = 0
|
||||
|
||||
self.recent_results.append(result)
|
||||
|
||||
# 保存本局记录
|
||||
record = {
|
||||
"局数": len(self.history_records) + 1,
|
||||
"对手出手": int(enemy_move),
|
||||
"AI预测对手下一手": int(predicted_next),
|
||||
"推荐出手": int(recommended_move),
|
||||
"我的出手": int(my_move),
|
||||
"本局结果": result
|
||||
}
|
||||
self.history_records.append(record)
|
||||
self.save_history()
|
||||
|
||||
# 输出格式化信息
|
||||
print(f"\n局数：{record['局数']}")
|
||||
print(f"你输入的对手实际出手：{moves[enemy_move]}")
|
||||
print(f"本局胜负结果：{result}")
|
||||
print(f"AI预测对手下一手可能出：{moves[predicted_next]}")
|
||||
print(f"AI推荐你出手：{moves[recommended_move]}")
|
||||
print(f"历史记录：总局数={len(self.history_records)}, 累积胜局={self.total_wins}, 累积负局={self.total_losses}, 当前连胜={self.W_count}\n")
|
||||
|
||||
return predicted_next, recommended_move
|
||||
|
||||
|
||||
# =============================
|
||||
# 主程序
|
||||
# =============================
|
||||
if __name__ == "__main__":
|
||||
assistant = RPSAssistant()
|
||||
print("🎮 猜拳辅助AI启动")
|
||||
print("每局输入对手出手：1=拳头,2=剪刀,3=布, q退出")
|
||||
|
||||
# 首局推荐出手
|
||||
first_move = random.choice([1, 2, 3])
|
||||
print(f"首局推荐你出手：{moves[first_move]}")
|
||||
|
||||
while True:
|
||||
inp = input("输入对手出手(1/2/3, q退出): ")
|
||||
if inp.lower() == 'q':
|
||||
print("比赛结束")
|
||||
break
|
||||
try:
|
||||
enemy_move = int(inp)
|
||||
if enemy_move not in [1, 2, 3]:
|
||||
print("请输入1/2/3或q退出")
|
||||
continue
|
||||
assistant.record_opponent_move(enemy_move, my_move=first_move)
|
||||
# 后续局首局推荐更新为上一局记录的推荐出手
|
||||
first_move = assistant.history_records[-1]["推荐出手"]
|
||||
except Exception as e:
|
||||
print("输入有误，请重新输入:", e)
|
||||
在新工单中引用
屏蔽一个用户