majiang_algorithm - 高性能麻将胡牌算法（Java）

项目说明

Majiang Algorithm

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> High-performance 麻将 winning-hand detection & AI discard algorithm based on lookup tables, supporting multiple wildcard tiles (jokers). One algorithm, three implementations — Java / Go / C++ — kept beha by shared cross-language tests.

中文文档

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Features

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Repository Layout

Both implementations load the same table files under data/ and are kept beha in sync by tests:
the algorithm unit tests are ported 1:1 between JUnit, go test and C++ tests, and
data/parity_cases.txt holds 2000+ fixed-seed deals whose
hu/ting/AI results are replayed and asserted by every language
(java/.../ParityFixtureTest.java, go/parity_test.go and cpp/test/parity_test.cpp).

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Quick Start

Ma

Win Detection / Waiting Hand

AI Discard

Go

Run the Go test suite (from go/, replays the same cases as the Ja):

C++

Table Generation

To regenerate the lookup tables with either implementation, run from the output directory:

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Interactive Web Platform & Algorithm Playground

> 🌐 Live Demo Online: 👉 (now powered by the Go build)

An interactive web platform and algorithm laboratory modeled after Tencent 麻将:

1. 4-Player 麻将 Battle (1 Human vs 3 AI / 4 AI Spectator):
- Green felt table, 3D tiles, and full tile set (Wan, Tong, Tiao, Winds, Dragons).
- Wildcard (界面 / laizi) support: Random flip indicator, specified wildcard, or clean hand.
- Draw, Discard, Chow (Chi), Pong (Peng), Kong (Gang), and Winning Hand (Hu).
2. Real-time Ready-Hand (Ting) Detection:
- Shows winning tiles and remaining count in the 游戏 whenever you are in Ting.
- Hover over hand tiles during discard to preview winning targets if discarded.
3. 💡 AI Recommendation:
- One-click best discard recommendation using AIUtil.outAI with expected score.
- 4-AI auto-play spectator mode with speeds (1x ~ 10x).
4. 🧪 Algorithm Playground:
- Test any hand (1-14 tiles) with wildcards.
- Measures isHu, isTing, and outAI execution times in microseconds (µs).

Start Web Server

The same web platform also ships as the Go build (single static binary, frontend embedded) — this is what the live demo runs as a systemd service (deploy/gq/):

Visit in your browser: 👉

CLI 4-AI Simulation Benchmark

Algorithm Documentation

| Document | Content |
|----------|---------|
| Win Detection Algorithm | Wildcard encoding, table structure, full win-detection & waiting-hand flow |
| AI Algorithm | Hand scoring model, discard / pong / kong decision logic |

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Related Projects

| Project | Description |
|---------|-------------|
| texas_algorithm | Texas Hold'em algorithm |
| teenpatti_algorithm | Teen Patti algorithm |

技术栈

开发语言：Java

项目类型：高性能麻将胡牌算法（Java）

Star 数：481

功能特点

这是一套高性能麻将胡牌算法（Java），代码结构清晰，适合开发者学习游戏的客户端渲染、服务端逻辑、场景管理与资源加载。项目涵盖了游戏的核心玩法、界面交互、地图与角色系统等模块，对理解游戏开发的完整流程具有较高的参考价值。

从技术实现角度看，游戏项目的难点在于：游戏循环与状态管理、资源加载与渲染优化、客户端与服务端的协议同步、以及多平台适配。本项目在这些方面提供了可运行的实现，开发者可以在此基础上进行二次开发或学习研究。

部署说明

1. 下载源码压缩包，解压到本地开发环境。

2. 根据项目类型准备对应的运行环境（Node.js / Go / C# / Unity / Godot 等）。

3. 按照 README 中的步骤配置依赖、数据库和配置文件。

4. 先启动服务端，再运行客户端，即可进入游戏进行测试。

注意：本项目仅供学习研究使用，请勿用于商业或非法用途。

学习价值

对于想进入游戏开发领域的开发者来说，这套源码是一个很好的入门参考。通过阅读代码，可以学习到游戏的核心玩法实现、场景与资源管理、网络协议设计、以及渲染优化等关键技术点。

同时，项目中的代码组织方式和模块划分，也展示了游戏项目在工程化方面的实践经验，值得开发者深入研究和借鉴。理解一个完整游戏项目的架构，对后续开发其他类型游戏也很有帮助。

免责声明

本资源来源于开源社区，仅供学习研究和技术交流使用，请勿用于任何商业或违法违规用途。使用本项目产生的一切后果由使用者自行承担。若涉及第三方权益，请联系删除。