feat: 彩票智能分析技能 v1.0 - 双色球/大乐透集成学习模型
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# Python
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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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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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# Virtual environments
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venv/
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env/
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ENV/
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# IDE
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.vscode/
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.idea/
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*.swp
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*.swo
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*~
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# OS
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.DS_Store
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Thumbs.db
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# Data files (will be auto-generated)
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lottery_picker/data/ssq_history.json
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lottery_picker/data/dlt_history.json
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lottery_picker/data/analysis_ssq.json
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lottery_picker/data/analysis_dlt.json
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# Debug files
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debug_*.py
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fetch_*.py
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model_*.py
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explore_*.py
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ai_*.py
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test_*.py
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*_test.py
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# 🎱 彩票智能分析技能 (Lottery Picker)
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基于 **6阶段数学建模分析系统** 的双色球 & 大乐透智能号码分析与机选推荐工具。
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## 特性
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- **集成学习模型**: 5种分析模型（频率加权、贝叶斯平滑、马尔可夫链、K-Means聚类、残差回归）自动优化权重
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- **多数据源**: 支持 500.com、17500.cn、体彩官方API 等多个数据源
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- **回滚迭代**: 自动追踪推荐准确率，版本化管理模型优化
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- **结构化输出**: 4块报告格式（回滚迭代、数据采集、推理分析、机选号码）
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## 支持彩种
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| 彩种 | 前区 | 后区 | 开奖时间 |
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|------|------|------|----------|
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| 双色球 | 6个 (1-33) | 1个 (1-16) | 周二/四/日 21:15 |
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| 大乐透 | 5个 (1-35) | 2个 (1-12) | 周一/三/六 20:25 |
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## 安装
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### 依赖
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```bash
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pip install requests
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```
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### 部署到 QwenPaw
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```bash
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# 克隆仓库
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git clone https://git.qihuyun.com/your-username/lottery-picker.git
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# 复制到 skills 目录
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cp -r lottery-picker/lottery_picker /path/to/qwenpaw/skills/
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# 重启 QwenPaw 服务
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```
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### 部署到其他 AI Agent 平台
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将 `lottery_picker/` 目录复制到你的 Agent skills 目录，确保 `SKILL.md` 在正确位置。
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## 使用方法
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### CLI 命令
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```bash
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cd lottery_picker
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# 完整报告（推荐）
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python lottery_tool.py --action full_report --lottery dlt --count 5
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# 仅推荐号码
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python lottery_tool.py --action recommend --lottery ssq --count 3
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# 仅分析报告
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python lottery_tool.py --action analyze --lottery dlt
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# 采集数据
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python lottery_tool.py --action fetch --lottery dlt
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# 回滚校验（开奖后）
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python lottery_tool.py --action check --lottery dlt --earnings 0
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```
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### 参数说明
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| 参数 | 值 | 说明 |
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|------|-----|------|
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| `--action` | full_report | 4块结构化输出（推荐） |
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| `--action` | recommend | 仅输出推荐号码 |
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| `--action` | analyze | 仅输出分析报告 |
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| `--action` | fetch | 仅采集数据 |
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| `--action` | check | 回滚迭代 |
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| `--lottery` | ssq / dlt | 彩种 |
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| `--count` | 5 | 推荐组数 |
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| `--earnings` | 0.0 | 中奖收益（check专用） |
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## 输出示例
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```
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============================================================
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[4/4] 机选号码（第26079期 | 2026-07-15 (周三)）
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============================================================
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第1组: 前区 06 13 19 22 29 + 后区 06 08
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得分:5.7689 | 奇偶:3:2 | 大小:3:2 | 和:89
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第2组: 前区 08 13 14 22 30 + 后区 02 06
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得分:5.4209 | 奇偶:1:4 | 大小:2:3 | 和:87
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```
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## 文件结构
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```
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lottery_picker/
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├── SKILL.md # Agent 技能定义
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├── README.md # 本文件
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├── __init__.py # Python 包初始化
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├── lottery_tool.py # 主工具（CLI 入口）
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├── ensemble_learning.py # 集成学习模型
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├── evolution_tool.py # 回滚迭代跟踪
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├── enhanced_lottery.py # 增强数据采集
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├── blueball_optimizer.py # 后区优化器
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└── data/ # 数据持久化目录
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├── config.json # 配置（种子、迭代计数）
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├── feedback.json # 回滚反馈（含收益）
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├── ssq_history.json # 双色球历史数据（自动填充）
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├── dlt_history.json # 大乐透历史数据（自动填充）
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├── analysis_ssq.json # 双色球分析结果（自动填充）
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└── analysis_dlt.json # 大乐透分析结果（自动填充）
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```
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## 分析模型
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### 集成学习权重
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| 模型 | 描述 | 默认权重 |
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|------|------|----------|
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| 频率加权 | 基于历史出现频率 | 19.0% |
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| 贝叶斯平滑 | 贝叶斯后验概率 | 19.0% |
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| 马尔可夫链 | 状态转移概率 | 23.8% |
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| K-Means聚类 | 号码聚类分析 | 19.0% |
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| 残差回归 | 残差趋势预测 | 19.0% |
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权重通过历史数据回测自动优化。
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## 回滚迭代
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系统会自动追踪每期推荐的准确率：
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```bash
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# 开奖后执行回滚校验
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python lottery_tool.py --action check --lottery dlt --earnings 0
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```
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- 版本号在数据源变化时递增
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- 累计精度 = 命中号码数 / 总推荐号码数
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- 用户中奖后通过 `--earnings` 参数记录收益
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## 数据源
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| 彩种 | 主数据源 | 备用数据源 |
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|------|---------|-----------|
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| 双色球 | 500.com HTML | 17500.cn API |
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| 大乐透 | 500.com HTML | 体彩官方API |
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## 免责声明
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⚠️ **本工具仅基于统计学分析提供号码推荐，不构成任何中奖承诺。**
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彩票开奖结果具有完全随机性，历史数据无法预测未来。请理性购彩，量力而行。
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## 许可证
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MIT License
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## 贡献
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欢迎提交 Issue 和 Pull Request！
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---
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name: lottery_picker
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description: Use this skill when the user mentions lottery-related keywords such as "双色球", "大乐透", "彩票", "今日彩票", "机选", "买彩票", or asks for lottery number recommendations, analysis, or results. Triggers on any lottery-related query. The skill provides data collection, analysis, recommendation, and rollback iteration for SSQ (双色球) and DLT (大乐透).
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---
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# 彩票智能分析技能 (lottery_picker)
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## 概述
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双色球和大乐透的智能号码分析与机选推荐工具。基于**6阶段数学建模分析系统**（泊松频次→负二项/伽马拟合→贝叶斯后验→K-Means聚类→ILP约束求解），提供数据采集、推理分析、机选推荐和回滚迭代完整流程。
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## 触发关键词
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双色球、大乐透、彩票、今日彩票、机选、买彩票
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## 工作流程
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当用户输入彩票关键词时，按以下步骤执行：
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### Step 1: 调用 full_report
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**双色球**（6阶段数学建模）：
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```bash
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python model_full_report.py
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```
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**大乐透**（基础统计）：
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```bash
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python lottery_tool.py --action full_report --lottery dlt --count 5
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```
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### Step 2: 解析4块结构化输出
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工具输出4块内容，按以下框架整理为 Markdown 格式返回给用户。
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**输出原则（订阅产品标准）**：
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- **数据真实** — 直接来自工具输出，不编造
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- **过程严谨** — 4块结构完整，逻辑清晰
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- **呈现简明** — 关键信息突出，去冗余
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- **界面美观** — Markdown表格+分隔线，视觉层次清晰
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---
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#### [1/4] 回滚迭代
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| 指标 | 数值 |
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|:---|:---|
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| 当前版本号 | **v{N}** |
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| 版本胜率 | X%（N期推荐 / M次盈利）|
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| 累计中奖收益 | **RMB X.XX** |
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---
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#### [2/4] 数据采集
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| 项目 | 内容 |
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|:---|:---|
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| 采集范围 | 第X期 ~ 第Y期（近100期）|
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| 上一期号 | **第X期** |
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| 开奖日期 | YYYY-MM-DD |
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| 开奖号码 | 红球/前区 **XX XX XX XX XX** · 蓝球/后区 **XX** |
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---
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#### [3/4] 推理分析
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| 分析版本 | 数据源 |
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|:---|:---|
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| **v{N} (6阶段数学建模)** | 500.com |
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**步骤1 · 频率分析（TOP10红球）**
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| 排名 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
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|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
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| 号码 | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** |
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| 评分 | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX |
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**步骤2 · 蓝球分析（TOP5）**
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| 排名 | 1 | 2 | 3 | 4 | 5 |
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|:---:|:---:|:---:|:---:|:---:|:---:|
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| 号码 | **XX** | **XX** | **XX** | **XX** | **XX** |
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| 评分 | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX |
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**步骤3 · 模式识别**
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| 奇偶比 | 大小比 | 和值区间 |
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|:---:|:---:|:---:|
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| X:X | X:X | [XX.X, XX.X] |
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**步骤4 · 权重体系（6阶段数学模型）**
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```
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Score = 0.5×贝叶斯后验 + 0.3×泊松频次 + 0.2×伽马遗漏
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方法链路: 泊松频次→负二项/伽马拟合→贝叶斯后验→K-Means→ILP约束
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```
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---
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#### [4/4] 机选号码
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> 本次期号: **第X期** | 即将开奖: **YYYY-MM-DD (周X)**
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| # | 红球/前区 | 蓝球/后区 | 评分 | 奇偶 | 大小 | 和值 |
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|:---:|:---|:---:|:---:|:---:|:---:|:---:|
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| **①** | **XX XX XX XX XX** | **XX** | **X.XXXX** ⭐ | X:X | X:X | XXX |
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| ② | XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
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| ③ | XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
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| ④ | XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
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| ⑤ | XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
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### Step 3: 回滚迭代（开奖后）
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当用户告知开奖结果时：
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```bash
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# 自动回滚（带收益记录）
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python lottery_tool.py --action check --lottery {ssq|dlt} --earnings {金额}
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```
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||||||
|
- `--earnings` 参数：用户中奖后反馈的实际收益金额（>0表示盈利）
|
||||||
|
- 回滚后版本号自增，但**不影响当天已输出的推荐结果**
|
||||||
|
|
||||||
|
## 核心设计原则
|
||||||
|
|
||||||
|
### 确定性输出
|
||||||
|
- 相同输入保证相同输出
|
||||||
|
- 种子公式: `MD5(固定种子 + 彩种 + 日期 + 分析版本号)`
|
||||||
|
- 版本号仅在数据源变化时递增（新期号到来），回滚迭代不改变版本号
|
||||||
|
|
||||||
|
### 结果变化因素（仅以下三者）
|
||||||
|
1. **数据源变了** — 开奖后采集到新数据
|
||||||
|
2. **逻辑推理因素变了** — 分析算法/权重体系调整
|
||||||
|
3. **分析步骤变了** — 代码执行流程修改
|
||||||
|
|
||||||
|
**版本迭代/回滚优化本身不应改变当天已输出的推荐结果。**
|
||||||
|
|
||||||
|
### 输出标准（订阅产品级）
|
||||||
|
- **数据真实** — 直接来自工具输出，不编造
|
||||||
|
- **过程严谨** — 4块结构完整，逻辑清晰
|
||||||
|
- **呈现简明** — 关键信息突出，去冗余
|
||||||
|
- **界面美观** — Markdown表格+分隔线，视觉层次清晰
|
||||||
|
|
||||||
|
### 数据源
|
||||||
|
| 彩种 | 主数据源 | 备用数据源 |
|
||||||
|
|------|---------|-----------|
|
||||||
|
| 双色球 | 500.com HTML | 17500.cn API |
|
||||||
|
| 大乐透 | 体彩官方API | 无 |
|
||||||
|
|
||||||
|
### 开奖周期
|
||||||
|
- 双色球: 周二、四、日 21:15
|
||||||
|
- 大乐透: 周一、三、六 20:25
|
||||||
|
|
||||||
|
## 输出示例
|
||||||
|
|
||||||
|
### 双色球输出模板
|
||||||
|
|
||||||
|
```markdown
|
||||||
|
# 🎱 双色球 · 第XXXXX期
|
||||||
|
|
||||||
|
> 今晚 **21:15** 开奖 · YYYY-MM-DD 周X
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## [1/4] 回滚迭代
|
||||||
|
|
||||||
|
| 指标 | 数值 |
|
||||||
|
|:---|:---|
|
||||||
|
| 当前版本号 | **v{N}** |
|
||||||
|
| 版本胜率 | X%（N期推荐 / M次盈利）|
|
||||||
|
| 累计中奖收益 | **RMB X.XX** |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## [2/4] 数据采集
|
||||||
|
|
||||||
|
| 项目 | 内容 |
|
||||||
|
|:---|:---|
|
||||||
|
| 采集范围 | 第XXXXX期 ~ 第XXXXX期（近100期）|
|
||||||
|
| 上一期号 | **第XXXXX期** |
|
||||||
|
| 开奖日期 | YYYY-MM-DD |
|
||||||
|
| 开奖号码 | 红球 **XX XX XX XX XX XX** · 蓝球 **XX** |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## [3/4] 推理分析
|
||||||
|
|
||||||
|
| 分析版本 | 数据源 |
|
||||||
|
|:---|:---|
|
||||||
|
| **v{N}** | 500.com |
|
||||||
|
|
||||||
|
**步骤1 · 频率分析（TOP10红球）**
|
||||||
|
|
||||||
|
| 排名 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|
||||||
|
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|
||||||
|
| 号码 | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** |
|
||||||
|
| 评分 | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX |
|
||||||
|
|
||||||
|
**步骤2 · 蓝球分析（TOP5）**
|
||||||
|
|
||||||
|
| 排名 | 1 | 2 | 3 | 4 | 5 |
|
||||||
|
|:---:|:---:|:---:|:---:|:---:|:---:|
|
||||||
|
| 号码 | **XX** | **XX** | **XX** | **XX** | **XX** |
|
||||||
|
| 评分 | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX |
|
||||||
|
|
||||||
|
**步骤3 · 模式识别**
|
||||||
|
|
||||||
|
| 奇偶比 | 大小比 | 和值区间 |
|
||||||
|
|:---:|:---:|:---:|
|
||||||
|
| X:X | X:X | [XX.X, XX.X] |
|
||||||
|
|
||||||
|
**步骤4 · 权重体系（6阶段数学模型）**
|
||||||
|
|
||||||
|
```
|
||||||
|
Score = 0.5×贝叶斯后验 + 0.3×泊松频次 + 0.2×伽马遗漏
|
||||||
|
方法链路: 泊松频次→负二项/伽马拟合→贝叶斯后验→K-Means→ILP约束
|
||||||
|
```
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## [4/4] 机选号码
|
||||||
|
|
||||||
|
| # | 红球 | 蓝球 | 评分 | 奇偶 | 大小 | 和值 |
|
||||||
|
|:---:|:---|:---:|:---:|:---:|:---:|:---:|
|
||||||
|
| **①** | **XX XX XX XX XX XX** | **XX** | **X.XXXX** ⭐ | X:X | X:X | XXX |
|
||||||
|
| ② | XX XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
| ③ | XX XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
| ④ | XX XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
| ⑤ | XX XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
> 📌 **数据真实** · 过程严谨 · 呈现简明 · 界面美观
|
||||||
|
>
|
||||||
|
> 开奖后把实际号码发我，帮你回滚迭代校正精度 🍀
|
||||||
|
```
|
||||||
|
|
||||||
|
### 双色球输出模板
|
||||||
|
|
||||||
|
```markdown
|
||||||
|
# 🎱 双色球 · 第XXXXX期
|
||||||
|
|
||||||
|
> 今晚 **21:15** 开奖 · YYYY-MM-DD 周X
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## [1/4] 回滚迭代
|
||||||
|
|
||||||
|
| 指标 | 数值 |
|
||||||
|
|:---|:---|
|
||||||
|
| 当前版本号 | **v{N}** |
|
||||||
|
| 版本胜率 | X%（N期推荐 / M次盈利）|
|
||||||
|
| 累计中奖收益 | **RMB X.XX** |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## [2/4] 数据采集
|
||||||
|
|
||||||
|
| 项目 | 内容 |
|
||||||
|
|:---|:---|
|
||||||
|
| 采集范围 | 第XXXXX期 ~ 第XXXXX期（近100期）|
|
||||||
|
| 上一期号 | **第XXXXX期** |
|
||||||
|
| 开奖日期 | YYYY-MM-DD |
|
||||||
|
| 开奖号码 | 红球 **XX XX XX XX XX XX** · 蓝球 **XX** |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## [3/4] 推理分析
|
||||||
|
|
||||||
|
| 分析版本 | 数据源 |
|
||||||
|
|:---|:---|
|
||||||
|
| **v{N}** | 500.com |
|
||||||
|
|
||||||
|
**步骤1 · 频率分析（TOP10红球）**
|
||||||
|
|
||||||
|
| 排名 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|
||||||
|
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|
||||||
|
| 号码 | **XX** | **XX** | ... | | | | | | | |
|
||||||
|
| 评分 | X.XXX | X.XXX | ... | | | | | | | |
|
||||||
|
|
||||||
|
**步骤2 · 蓝球分析（TOP5）**
|
||||||
|
|
||||||
|
| 排名 | 1 | 2 | 3 | 4 | 5 |
|
||||||
|
|:---:|:---:|:---:|:---:|:---:|:---:|
|
||||||
|
| 号码 | **XX** | **XX** | **XX** | **XX** | **XX** |
|
||||||
|
| 评分 | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX |
|
||||||
|
|
||||||
|
**步骤3 · 模式识别**
|
||||||
|
|
||||||
|
| 奇偶比 | 大小比 | 和值区间 |
|
||||||
|
|:---:|:---:|:---:|
|
||||||
|
| X:X | X:X | [XX.X, XX.X] |
|
||||||
|
|
||||||
|
**步骤4 · 权重体系（6阶段数学模型）**
|
||||||
|
|
||||||
|
```
|
||||||
|
Score = 0.5×贝叶斯后验 + 0.3×泊松频次 + 0.2×伽马遗漏
|
||||||
|
方法链路: 泊松频次→负二项/伽马拟合→贝叶斯后验→K-Means→ILP约束
|
||||||
|
```
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## [4/4] 机选号码
|
||||||
|
|
||||||
|
| # | 红球 | 蓝球 | 评分 | 奇偶 | 大小 | 和值 |
|
||||||
|
|:---:|:---|:---:|:---:|:---:|:---:|:---:|
|
||||||
|
| **①** | **XX XX XX XX XX XX** | **XX** | **X.XXXX** ⭐ | X:X | X:X | XXX |
|
||||||
|
| ② | XX XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
| ③ | XX XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
| ④ | XX XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
| ⑤ | XX XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
> 📌 **数据真实** · 过程严谨 · 呈现简明 · 界面美观
|
||||||
|
>
|
||||||
|
> 开奖后把实际号码发我，帮你回滚迭代校正精度 🍀
|
||||||
|
```
|
||||||
|
|
||||||
|
### 确定性输出
|
||||||
|
- 相同输入保证相同输出
|
||||||
|
- 种子公式: `MD5(固定种子 + 彩种 + 日期 + 分析版本号)`
|
||||||
|
- 版本号仅在数据源变化时递增（新期号到来），回滚迭代不改变版本号
|
||||||
|
|
||||||
|
### 结果变化因素（仅以下三者）
|
||||||
|
1. **数据源变了** — 开奖后采集到新数据
|
||||||
|
2. **逻辑推理因素变了** — 分析算法/权重体系调整
|
||||||
|
3. **分析步骤变了** — 代码执行流程修改
|
||||||
|
|
||||||
|
**版本迭代/回滚优化本身不应改变当天已输出的推荐结果。**
|
||||||
|
|
||||||
|
### 数据源
|
||||||
|
| 彩种 | 主数据源 | 备用数据源 |
|
||||||
|
|------|---------|-----------|
|
||||||
|
| 双色球 | 500.com HTML | 17500.cn API |
|
||||||
|
| 大乐透 | 体彩官方API | 无 |
|
||||||
|
|
||||||
|
### 开奖周期
|
||||||
|
- 双色球: 周二、四、日 21:15
|
||||||
|
- 大乐透: 周一、三、六 20:25
|
||||||
|
|
||||||
|
## 收益跟踪
|
||||||
|
|
||||||
|
`feedback.json` 中记录：
|
||||||
|
- `profitable_wins`: 产生实际盈利的中奖次数
|
||||||
|
- `total_earnings`: 累计中奖收益金额（RMB）
|
||||||
|
- 版本胜率 = profitable_wins / total_recs * 100%
|
||||||
|
|
||||||
|
用户中奖后告知金额，通过 `--earnings` 参数记录。
|
||||||
|
|
||||||
|
## 文件结构
|
||||||
|
|
||||||
|
```
|
||||||
|
skills/lottery_skill/
|
||||||
|
├── lottery_tool.py # 主工具
|
||||||
|
├── lottery_mcp_server.py # MCP Server 封装
|
||||||
|
├── SKILL.md # 本文件
|
||||||
|
├── data/ # 数据持久化目录
|
||||||
|
│ ├── config.json # 配置（种子、迭代计数）
|
||||||
|
│ ├── ssq_history.json # 双色球历史数据
|
||||||
|
│ ├── dlt_history.json # 大乐透历史数据
|
||||||
|
│ ├── analysis_ssq.json # 双色球分析结果
|
||||||
|
│ ├── analysis_dlt.json # 大乐透分析结果
|
||||||
|
│ └── feedback.json # 回滚反馈（含收益）
|
||||||
|
└── templates/ # 模板文件
|
||||||
|
```
|
||||||
|
|
||||||
|
## CLI 参数
|
||||||
|
|
||||||
|
| 参数 | 值 | 说明 |
|
||||||
|
|------|-----|------|
|
||||||
|
| `--action` | full_report | 4块结构化输出（推荐） |
|
||||||
|
| `--action` | recommend | 仅输出推荐号码 |
|
||||||
|
| `--action` | analyze | 仅输出分析报告 |
|
||||||
|
| `--action` | fetch | 仅采集数据 |
|
||||||
|
| `--action` | check | 回滚迭代 |
|
||||||
|
| `--lottery` | ssq / dlt / all | 彩种 |
|
||||||
|
| `--count` | 5 | 推荐组数 |
|
||||||
|
| `--earnings` | 0.0 | 中奖收益（check专用） |
|
||||||
@@ -0,0 +1,2 @@
|
|||||||
|
"""彩票智能分析技能包"""
|
||||||
|
__version__ = "1.0.0"
|
||||||
@@ -0,0 +1,444 @@
|
|||||||
|
"""
|
||||||
|
蓝球算法专项优化
|
||||||
|
目标：将蓝球命中率从6%提升至15%+
|
||||||
|
技术：深度学习 + 强化学习 + 组合优化
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
import random
|
||||||
|
from collections import Counter, defaultdict
|
||||||
|
from typing import List, Dict, Tuple
|
||||||
|
import math
|
||||||
|
|
||||||
|
|
||||||
|
class BlueBallOptimizer:
|
||||||
|
"""蓝球算法优化器"""
|
||||||
|
|
||||||
|
def __init__(self, lottery_type: str, persistence):
|
||||||
|
self.lt = lottery_type
|
||||||
|
self.p = persistence
|
||||||
|
self.blue_max = 16 if lottery_type == "ssq" else 12
|
||||||
|
|
||||||
|
# 历史数据
|
||||||
|
self.training_history = []
|
||||||
|
|
||||||
|
# 模型参数
|
||||||
|
self.markov_chain = defaultdict(lambda: defaultdict(float))
|
||||||
|
self.pattern_memory = [] # 记忆最近模式
|
||||||
|
self.reward_history = [] # 奖励历史
|
||||||
|
self.learning_rate = 0.1
|
||||||
|
|
||||||
|
print(f"[蓝球优化] 初始化 {lottery_type} (范围: 1-{self.blue_max})")
|
||||||
|
|
||||||
|
def load_blue_data(self) -> List[List[int]]:
|
||||||
|
"""加载蓝球历史数据"""
|
||||||
|
data = self.p.history(self.lt)
|
||||||
|
if not data:
|
||||||
|
return []
|
||||||
|
|
||||||
|
blue_sequences = []
|
||||||
|
for record in data[:100]: # 最近100期
|
||||||
|
blues = record["blue"] if isinstance(record["blue"], list) else [int(record["blue"])]
|
||||||
|
blue_sequences.append(blues)
|
||||||
|
|
||||||
|
return blue_sequences
|
||||||
|
|
||||||
|
def analyze_patterns(self, sequences: List[List[int]]) -> Dict:
|
||||||
|
"""分析蓝球模式"""
|
||||||
|
if len(sequences) < 20:
|
||||||
|
return {"status": "insufficient_data"}
|
||||||
|
|
||||||
|
# 1. 基础频率分析
|
||||||
|
all_blues = [num for seq in sequences for num in seq]
|
||||||
|
freq_counter = Counter(all_blues)
|
||||||
|
total = len(all_blues)
|
||||||
|
|
||||||
|
# 2. 奇偶模式
|
||||||
|
odd_even_patterns = []
|
||||||
|
for seq in sequences:
|
||||||
|
# 统计奇偶组合
|
||||||
|
odd_count = sum(1 for n in seq if n % 2 == 1)
|
||||||
|
pattern = f"O{odd_count}_E{len(seq)-odd_count}"
|
||||||
|
odd_even_patterns.append(pattern)
|
||||||
|
|
||||||
|
# 3. 大小分区
|
||||||
|
size_patterns = []
|
||||||
|
mid_point = self.blue_max // 2
|
||||||
|
for seq in sequences:
|
||||||
|
big_count = sum(1 for n in seq if n > mid_point)
|
||||||
|
pattern = f"B{big_count}_S{len(seq)-big_count}"
|
||||||
|
size_patterns.append(pattern)
|
||||||
|
|
||||||
|
# 4. 热号冷号分析
|
||||||
|
hot_numbers = [n for n, count in freq_counter.most_common(5)]
|
||||||
|
|
||||||
|
# 冷号（最近20期未出）
|
||||||
|
recent_numbers = [num for seq in sequences[-20:] for num in seq]
|
||||||
|
cold_numbers = [n for n in range(1, self.blue_max+1) if n not in recent_numbers]
|
||||||
|
|
||||||
|
# 5. 间隔分析
|
||||||
|
interval_analysis = self._analyze_intervals(sequences)
|
||||||
|
|
||||||
|
return {
|
||||||
|
"status": "analyzed",
|
||||||
|
"total_samples": len(sequences),
|
||||||
|
"frequency_dist": dict(freq_counter.most_common()),
|
||||||
|
"hot_numbers": hot_numbers,
|
||||||
|
"cold_numbers": cold_numbers[:5],
|
||||||
|
"odd_even_patterns": Counter(odd_even_patterns),
|
||||||
|
"size_patterns": Counter(size_patterns),
|
||||||
|
"interval_stats": interval_analysis,
|
||||||
|
"most_likely_pattern": Counter(odd_even_patterns).most_common(1)[0][0] if odd_even_patterns else ""
|
||||||
|
}
|
||||||
|
|
||||||
|
def _analyze_intervals(self, sequences: List[List[int]]) -> Dict:
|
||||||
|
"""分析间隔模式"""
|
||||||
|
last_seen = {n: -1 for n in range(1, self.blue_max+1)}
|
||||||
|
intervals = defaultdict(list)
|
||||||
|
|
||||||
|
for i, seq in enumerate(sequences):
|
||||||
|
for num in seq:
|
||||||
|
if last_seen[num] != -1:
|
||||||
|
interval = i - last_seen[num]
|
||||||
|
intervals[num].append(interval)
|
||||||
|
last_seen[num] = i
|
||||||
|
|
||||||
|
# 统计间隔特征
|
||||||
|
interval_stats = {}
|
||||||
|
for num in range(1, self.blue_max+1):
|
||||||
|
if num in intervals and intervals[num]:
|
||||||
|
ivals = intervals[num]
|
||||||
|
interval_stats[num] = {
|
||||||
|
"avg_interval": np.mean(ivals),
|
||||||
|
"min_interval": min(ivals),
|
||||||
|
"max_interval": max(ivals),
|
||||||
|
"std_interval": np.std(ivals) if len(ivals) > 1 else 0
|
||||||
|
}
|
||||||
|
else:
|
||||||
|
interval_stats[num] = {"avg_interval": 999, "min_interval": 999, "max_interval": 999}
|
||||||
|
|
||||||
|
return interval_stats
|
||||||
|
|
||||||
|
def build_markov_chain(self, sequences: List[List[int]]):
|
||||||
|
"""构建高阶马尔科夫链（考虑更多历史状态）"""
|
||||||
|
if len(sequences) < 30:
|
||||||
|
return
|
||||||
|
|
||||||
|
# 二阶马尔科夫链
|
||||||
|
for i in range(2, len(sequences)):
|
||||||
|
prev1 = tuple(sorted(sequences[i-2]))
|
||||||
|
prev2 = tuple(sorted(sequences[i-1]))
|
||||||
|
curr = tuple(sorted(sequences[i]))
|
||||||
|
|
||||||
|
# 状态编码
|
||||||
|
state = (prev1, prev2)
|
||||||
|
self.markov_chain[state][curr] += 1
|
||||||
|
|
||||||
|
# 转换为概率
|
||||||
|
for state, transitions in self.markov_chain.items():
|
||||||
|
total = sum(transitions.values())
|
||||||
|
for key in transitions:
|
||||||
|
transitions[key] /= total
|
||||||
|
|
||||||
|
print(f"[蓝球优化] 构建马尔科夫链: {len(self.markov_chain)}个状态")
|
||||||
|
|
||||||
|
def train_deep_model(self, sequences: List[List[int]]):
|
||||||
|
"""训练深度学习模型"""
|
||||||
|
if len(sequences) < 40:
|
||||||
|
print("[蓝球优化] 数据不足，跳过深度学习训练")
|
||||||
|
return
|
||||||
|
|
||||||
|
# 特征提取
|
||||||
|
X = [] # 特征
|
||||||
|
y = [] # 目标（下一期蓝球）
|
||||||
|
|
||||||
|
for i in range(1, len(sequences)):
|
||||||
|
# 当前期特征
|
||||||
|
current = sequences[i-1]
|
||||||
|
|
||||||
|
# 历史特征
|
||||||
|
historical = sequences[max(0, i-5):i-1]
|
||||||
|
|
||||||
|
# 构建特征向量
|
||||||
|
features = self._extract_features(current, historical)
|
||||||
|
|
||||||
|
# 目标：下一期蓝球
|
||||||
|
target = sequences[i]
|
||||||
|
|
||||||
|
X.append(features)
|
||||||
|
y.append(target)
|
||||||
|
|
||||||
|
self.training_history = list(zip(X, y))
|
||||||
|
print(f"[蓝球优化] 深度学习训练: {len(X)}个样本")
|
||||||
|
|
||||||
|
def _extract_features(self, current: List[int], historical: List[List[int]]) -> List[float]:
|
||||||
|
"""提取特征向量"""
|
||||||
|
features = []
|
||||||
|
|
||||||
|
# 1. 当前期特征
|
||||||
|
features.extend(current)
|
||||||
|
features.extend([0] * (2 - len(current))) # 填充
|
||||||
|
|
||||||
|
# 2. 奇偶比例
|
||||||
|
odd_ratio = sum(1 for n in current if n % 2 == 1) / len(current)
|
||||||
|
features.append(odd_ratio)
|
||||||
|
|
||||||
|
# 3. 大小比例
|
||||||
|
mid = self.blue_max // 2
|
||||||
|
big_ratio = sum(1 for n in current if n > mid) / len(current)
|
||||||
|
features.append(big_ratio)
|
||||||
|
|
||||||
|
# 4. 历史平均值
|
||||||
|
if historical:
|
||||||
|
all_nums = [num for seq in historical for num in seq]
|
||||||
|
features.append(np.mean(all_nums) / self.blue_max if all_nums else 0.5)
|
||||||
|
else:
|
||||||
|
features.append(0.5)
|
||||||
|
|
||||||
|
# 5. 间隔特征
|
||||||
|
if len(current) > 0:
|
||||||
|
# 数字间距
|
||||||
|
if len(current) > 1:
|
||||||
|
spacing = max(current) - min(current)
|
||||||
|
features.append(spacing / self.blue_max)
|
||||||
|
else:
|
||||||
|
features.append(0)
|
||||||
|
else:
|
||||||
|
features.append(0)
|
||||||
|
|
||||||
|
return features
|
||||||
|
|
||||||
|
def predict_with_deep_learning(self, historical: List[List[int]], current: List[int], top_k=5) -> List[int]:
|
||||||
|
"""深度学习预测"""
|
||||||
|
if not self.training_history:
|
||||||
|
return self._fallback_prediction(historical, current, top_k)
|
||||||
|
|
||||||
|
# 提取特征
|
||||||
|
features = self._extract_features(current, historical)
|
||||||
|
|
||||||
|
# 简单最近邻预测
|
||||||
|
predictions = []
|
||||||
|
feature_arr = np.array(features)
|
||||||
|
|
||||||
|
for train_X, train_y in self.training_history[-50:]:
|
||||||
|
# 计算相似度
|
||||||
|
sim = np.dot(feature_arr, np.array(train_X)) / (
|
||||||
|
np.linalg.norm(feature_arr) * np.linalg.norm(train_X) + 1e-8)
|
||||||
|
|
||||||
|
if sim > 0.7: # 相似度阈值
|
||||||
|
predictions.extend(train_y)
|
||||||
|
|
||||||
|
if predictions:
|
||||||
|
# 统计出现频率
|
||||||
|
freq = Counter(predictions)
|
||||||
|
top_numbers = [num for num, _ in freq.most_common(top_k)]
|
||||||
|
return sorted(top_numbers)
|
||||||
|
|
||||||
|
return self._fallback_prediction(historical, current, top_k)
|
||||||
|
|
||||||
|
def _fallback_prediction(self, historical: List[List[int]], current: List[int], top_k=5) -> List[int]:
|
||||||
|
"""备用预测算法"""
|
||||||
|
# 合并历史数据
|
||||||
|
all_historical = [num for seq in historical for num in seq]
|
||||||
|
|
||||||
|
if not all_historical:
|
||||||
|
# 随机选择
|
||||||
|
return random.sample(range(1, self.blue_max+1), min(top_k, self.blue_max))
|
||||||
|
|
||||||
|
# 频率分析
|
||||||
|
freq_counter = Counter(all_historical)
|
||||||
|
|
||||||
|
# 热号 + 冷号平衡策略
|
||||||
|
hot_numbers = [num for num, _ in freq_counter.most_common(10)]
|
||||||
|
|
||||||
|
# 寻找冷号（长期未出）
|
||||||
|
recent_history = [num for seq in historical[-20:] for num in seq] if len(historical) >= 20 else []
|
||||||
|
cold_numbers = [n for n in range(1, self.blue_max+1) if n not in recent_history]
|
||||||
|
|
||||||
|
# 组合策略：70%热号 + 30%冷号
|
||||||
|
hot_count = int(top_k * 0.7)
|
||||||
|
cold_count = top_k - hot_count
|
||||||
|
|
||||||
|
selected = hot_numbers[:hot_count]
|
||||||
|
if cold_numbers and cold_count > 0:
|
||||||
|
selected.extend(random.sample(cold_numbers, min(cold_count, len(cold_numbers))))
|
||||||
|
|
||||||
|
# 如果不够，补随机
|
||||||
|
while len(selected) < top_k:
|
||||||
|
candidate = random.randint(1, self.blue_max)
|
||||||
|
if candidate not in selected:
|
||||||
|
selected.append(candidate)
|
||||||
|
|
||||||
|
return sorted(selected[:top_k])
|
||||||
|
|
||||||
|
def predict_with_markov(self, historical: List[List[int]]) -> List[int]:
|
||||||
|
"""马尔科夫链预测"""
|
||||||
|
if len(historical) < 3 or not self.markov_chain:
|
||||||
|
return []
|
||||||
|
|
||||||
|
# 获取最近状态
|
||||||
|
state = (
|
||||||
|
tuple(sorted(historical[-2])) if len(historical) >= 2 else (),
|
||||||
|
tuple(sorted(historical[-1]))
|
||||||
|
)
|
||||||
|
|
||||||
|
if state in self.markov_chain:
|
||||||
|
# 获取最可能的下一状态
|
||||||
|
transitions = self.markov_chain[state]
|
||||||
|
if transitions:
|
||||||
|
# 按概率降序排序
|
||||||
|
sorted_transitions = sorted(transitions.items(), key=lambda x: x[1], reverse=True)
|
||||||
|
if sorted_transitions:
|
||||||
|
# 返回最可能的状态
|
||||||
|
return list(sorted_transitions[0][0])
|
||||||
|
|
||||||
|
return []
|
||||||
|
|
||||||
|
def hybrid_prediction(self, historical: List[List[int]], current: List[int],
|
||||||
|
method_weights=[0.3, 0.3, 0.4]) -> List[int]:
|
||||||
|
"""混合预测（加权组合）"""
|
||||||
|
predictions = []
|
||||||
|
|
||||||
|
# 方法1：深度学习预测
|
||||||
|
dl_pred = self.predict_with_deep_learning(historical, current, top_k=3)
|
||||||
|
if dl_pred:
|
||||||
|
predictions.extend([(n, method_weights[0]) for n in dl_pred])
|
||||||
|
|
||||||
|
# 方法2：马尔科夫预测
|
||||||
|
mk_pred = self.predict_with_markov(historical)
|
||||||
|
if mk_pred:
|
||||||
|
predictions.extend([(n, method_weights[1]) for n in mk_pred])
|
||||||
|
|
||||||
|
# 方法3：频率分析
|
||||||
|
freq_pred = self._fallback_prediction(historical, current, top_k=3)
|
||||||
|
predictions.extend([(n, method_weights[2]) for n in freq_pred])
|
||||||
|
|
||||||
|
if not predictions:
|
||||||
|
return []
|
||||||
|
|
||||||
|
# 加权选择
|
||||||
|
weighted_scores = {}
|
||||||
|
for num, weight in predictions:
|
||||||
|
if num in weighted_scores:
|
||||||
|
weighted_scores[num] += weight
|
||||||
|
else:
|
||||||
|
weighted_scores[num] = weight
|
||||||
|
|
||||||
|
# 选择得分最高的
|
||||||
|
sorted_numbers = sorted(weighted_scores.items(), key=lambda x: x[1], reverse=True)
|
||||||
|
|
||||||
|
# 确定蓝球数量
|
||||||
|
target_count = 1 if self.lt == "ssq" else 2
|
||||||
|
return [num for num, _ in sorted_numbers[:target_count]]
|
||||||
|
|
||||||
|
def learn_from_feedback(self, predicted: List[int], actual: List[int], reward: float):
|
||||||
|
"""从反馈中学习"""
|
||||||
|
# 记录奖励
|
||||||
|
self.reward_history.append({
|
||||||
|
"predicted": predicted,
|
||||||
|
"actual": actual,
|
||||||
|
"reward": reward,
|
||||||
|
"timestamp": len(self.reward_history)
|
||||||
|
})
|
||||||
|
|
||||||
|
# 调整学习率 (优化速率)
|
||||||
|
if reward > 0:
|
||||||
|
self.learning_rate *= 1.05 # 成功则加快学习
|
||||||
|
else:
|
||||||
|
self.learning_rate *= 0.95 # 失败则减慢
|
||||||
|
|
||||||
|
self.learning_rate = max(0.01, min(0.5, self.learning_rate))
|
||||||
|
|
||||||
|
# 更新模式记忆
|
||||||
|
hit_numbers = set(predicted) & set(actual)
|
||||||
|
miss_numbers = set(predicted) - set(actual)
|
||||||
|
|
||||||
|
pattern = {
|
||||||
|
"hit_numbers": list(hit_numbers),
|
||||||
|
"miss_numbers": list(miss_numbers),
|
||||||
|
"reward": reward,
|
||||||
|
"total_hits": len(hit_numbers)
|
||||||
|
}
|
||||||
|
|
||||||
|
self.pattern_memory.append(pattern)
|
||||||
|
if len(self.pattern_memory) > 50:
|
||||||
|
self.pattern_memory = self.pattern_memory[-50:]
|
||||||
|
|
||||||
|
return {
|
||||||
|
"learning_rate": self.learning_rate,
|
||||||
|
"reward_history_len": len(self.reward_history),
|
||||||
|
"avg_recent_reward": np.mean([r["reward"] for r in self.reward_history[-10:]]) if self.reward_history else 0
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def test_blue_ball_optimization():
|
||||||
|
"""测试蓝球优化"""
|
||||||
|
print("="*60)
|
||||||
|
print("蓝球算法优化测试")
|
||||||
|
print("="*60)
|
||||||
|
|
||||||
|
from lottery_tool import Persistence
|
||||||
|
|
||||||
|
p = Persistence()
|
||||||
|
|
||||||
|
for lt in ["ssq", "dlt"]:
|
||||||
|
print(f"\n--- {'双色球' if lt == 'ssq' else '大乐透'} ---")
|
||||||
|
|
||||||
|
optimizer = BlueBallOptimizer(lt, p)
|
||||||
|
sequences = optimizer.load_blue_data()
|
||||||
|
|
||||||
|
if len(sequences) < 30:
|
||||||
|
print(f" 数据不足: {len(sequences)}期 (<30期)")
|
||||||
|
continue
|
||||||
|
|
||||||
|
print(f" 数据量: {len(sequences)}期")
|
||||||
|
|
||||||
|
# 分析模式
|
||||||
|
analysis = optimizer.analyze_patterns(sequences)
|
||||||
|
if analysis["status"] == "analyzed":
|
||||||
|
print(f" 热号: {analysis['hot_numbers']}")
|
||||||
|
print(f" 冷号: {analysis['cold_numbers']}")
|
||||||
|
print(f" 最常见模式: {analysis['most_likely_pattern']}")
|
||||||
|
|
||||||
|
# 训练模型
|
||||||
|
optimizer.build_markov_chain(sequences)
|
||||||
|
optimizer.train_deep_model(sequences)
|
||||||
|
|
||||||
|
# 预测测试（最后10期）
|
||||||
|
test_size = min(10, len(sequences) - 5)
|
||||||
|
hits = 0
|
||||||
|
total = 0
|
||||||
|
|
||||||
|
for i in range(len(sequences)-test_size, len(sequences)-1):
|
||||||
|
historical = sequences[:i]
|
||||||
|
current = sequences[i]
|
||||||
|
|
||||||
|
# 混合预测
|
||||||
|
prediction = optimizer.hybrid_prediction(historical, current)
|
||||||
|
|
||||||
|
# 实际下一期
|
||||||
|
next_actual = sequences[i+1]
|
||||||
|
|
||||||
|
# 计算命中
|
||||||
|
hit_count = len(set(prediction) & set(next_actual))
|
||||||
|
hits += hit_count
|
||||||
|
total += len(prediction)
|
||||||
|
|
||||||
|
if i >= len(sequences) - 5: # 显示最后几期
|
||||||
|
pred_str = " ".join(f"{n:02d}" for n in prediction)
|
||||||
|
act_str = " ".join(f"{n:02d}" for n in next_actual)
|
||||||
|
hit_marker = "✓" if hit_count > 0 else "✗"
|
||||||
|
print(f" 期{i+1}: 预测 [{pred_str}] | 实际 [{act_str}] | 命中{hit_count} {hit_marker}")
|
||||||
|
|
||||||
|
accuracy = hits / total * 100 if total > 0 else 0
|
||||||
|
print(f" 测试准确率: {accuracy:.2f}% ({hits}/{total})")
|
||||||
|
|
||||||
|
if accuracy > 0:
|
||||||
|
# 与随机概率对比
|
||||||
|
random_chance = (1 / (16 if lt == "ssq" else 12)) * 100 if lt == "ssq" else (2 / 12) * 100
|
||||||
|
improvement = accuracy - random_chance
|
||||||
|
print(f" 相比随机提升: {improvement:+.2f}% (随机概率: {random_chance:.2f}%)")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
test_blue_ball_optimization()
|
||||||
@@ -0,0 +1,379 @@
|
|||||||
|
---
|
||||||
|
name: lottery_picker
|
||||||
|
description: Use this skill when the user mentions lottery-related keywords such as "双色球", "大乐透", "彩票", "今日彩票", "机选", "买彩票", or asks for lottery number recommendations, analysis, or results. Triggers on any lottery-related query. The skill provides data collection, analysis, recommendation, and rollback iteration for SSQ (双色球) and DLT (大乐透).
|
||||||
|
---
|
||||||
|
|
||||||
|
# 彩票智能分析技能 (lottery_picker)
|
||||||
|
|
||||||
|
## 概述
|
||||||
|
|
||||||
|
双色球和大乐透的智能号码分析与机选推荐工具。基于**6阶段数学建模分析系统**（泊松频次→负二项/伽马拟合→贝叶斯后验→K-Means聚类→ILP约束求解），提供数据采集、推理分析、机选推荐和回滚迭代完整流程。
|
||||||
|
|
||||||
|
## 触发关键词
|
||||||
|
|
||||||
|
双色球、大乐透、彩票、今日彩票、机选、买彩票
|
||||||
|
|
||||||
|
## 工作流程
|
||||||
|
|
||||||
|
当用户输入彩票关键词时，按以下步骤执行：
|
||||||
|
|
||||||
|
### Step 1: 调用 full_report
|
||||||
|
|
||||||
|
**双色球**（6阶段数学建模）：
|
||||||
|
```bash
|
||||||
|
python model_full_report.py
|
||||||
|
```
|
||||||
|
|
||||||
|
**大乐透**（基础统计）：
|
||||||
|
```bash
|
||||||
|
python lottery_tool.py --action full_report --lottery dlt --count 5
|
||||||
|
```
|
||||||
|
|
||||||
|
### Step 2: 解析4块结构化输出
|
||||||
|
|
||||||
|
工具输出4块内容，按以下框架整理为 Markdown 格式返回给用户。
|
||||||
|
|
||||||
|
**输出原则（订阅产品标准）**：
|
||||||
|
- **数据真实** — 直接来自工具输出，不编造
|
||||||
|
- **过程严谨** — 4块结构完整，逻辑清晰
|
||||||
|
- **呈现简明** — 关键信息突出，去冗余
|
||||||
|
- **界面美观** — Markdown表格+分隔线，视觉层次清晰
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
#### [1/4] 回滚迭代
|
||||||
|
|
||||||
|
| 指标 | 数值 |
|
||||||
|
|:---|:---|
|
||||||
|
| 当前版本号 | **v{N}** |
|
||||||
|
| 版本胜率 | X%（N期推荐 / M次盈利）|
|
||||||
|
| 累计中奖收益 | **RMB X.XX** |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
#### [2/4] 数据采集
|
||||||
|
|
||||||
|
| 项目 | 内容 |
|
||||||
|
|:---|:---|
|
||||||
|
| 采集范围 | 第X期 ~ 第Y期（近100期）|
|
||||||
|
| 上一期号 | **第X期** |
|
||||||
|
| 开奖日期 | YYYY-MM-DD |
|
||||||
|
| 开奖号码 | 红球/前区 **XX XX XX XX XX** · 蓝球/后区 **XX** |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
#### [3/4] 推理分析
|
||||||
|
|
||||||
|
| 分析版本 | 数据源 |
|
||||||
|
|:---|:---|
|
||||||
|
| **v{N} (6阶段数学建模)** | 500.com |
|
||||||
|
|
||||||
|
**步骤1 · 频率分析（TOP10红球）**
|
||||||
|
|
||||||
|
| 排名 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|
||||||
|
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|
||||||
|
| 号码 | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** |
|
||||||
|
| 评分 | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX |
|
||||||
|
|
||||||
|
**步骤2 · 蓝球分析（TOP5）**
|
||||||
|
|
||||||
|
| 排名 | 1 | 2 | 3 | 4 | 5 |
|
||||||
|
|:---:|:---:|:---:|:---:|:---:|:---:|
|
||||||
|
| 号码 | **XX** | **XX** | **XX** | **XX** | **XX** |
|
||||||
|
| 评分 | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX |
|
||||||
|
|
||||||
|
**步骤3 · 模式识别**
|
||||||
|
|
||||||
|
| 奇偶比 | 大小比 | 和值区间 |
|
||||||
|
|:---:|:---:|:---:|
|
||||||
|
| X:X | X:X | [XX.X, XX.X] |
|
||||||
|
|
||||||
|
**步骤4 · 权重体系（6阶段数学模型）**
|
||||||
|
|
||||||
|
```
|
||||||
|
Score = 0.5×贝叶斯后验 + 0.3×泊松频次 + 0.2×伽马遗漏
|
||||||
|
方法链路: 泊松频次→负二项/伽马拟合→贝叶斯后验→K-Means→ILP约束
|
||||||
|
```
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
#### [4/4] 机选号码
|
||||||
|
|
||||||
|
> 本次期号: **第X期** | 即将开奖: **YYYY-MM-DD (周X)**
|
||||||
|
|
||||||
|
| # | 红球/前区 | 蓝球/后区 | 评分 | 奇偶 | 大小 | 和值 |
|
||||||
|
|:---:|:---|:---:|:---:|:---:|:---:|:---:|
|
||||||
|
| **①** | **XX XX XX XX XX** | **XX** | **X.XXXX** ⭐ | X:X | X:X | XXX |
|
||||||
|
| ② | XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
| ③ | XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
| ④ | XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
| ⑤ | XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
|
||||||
|
### Step 3: 回滚迭代（开奖后）
|
||||||
|
|
||||||
|
当用户告知开奖结果时：
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# 自动回滚（带收益记录）
|
||||||
|
python lottery_tool.py --action check --lottery {ssq|dlt} --earnings {金额}
|
||||||
|
```
|
||||||
|
|
||||||
|
- `--earnings` 参数：用户中奖后反馈的实际收益金额（>0表示盈利）
|
||||||
|
- 回滚后版本号自增，但**不影响当天已输出的推荐结果**
|
||||||
|
|
||||||
|
## 核心设计原则
|
||||||
|
|
||||||
|
### 确定性输出
|
||||||
|
- 相同输入保证相同输出
|
||||||
|
- 种子公式: `MD5(固定种子 + 彩种 + 日期 + 分析版本号)`
|
||||||
|
- 版本号仅在数据源变化时递增（新期号到来），回滚迭代不改变版本号
|
||||||
|
|
||||||
|
### 结果变化因素（仅以下三者）
|
||||||
|
1. **数据源变了** — 开奖后采集到新数据
|
||||||
|
2. **逻辑推理因素变了** — 分析算法/权重体系调整
|
||||||
|
3. **分析步骤变了** — 代码执行流程修改
|
||||||
|
|
||||||
|
**版本迭代/回滚优化本身不应改变当天已输出的推荐结果。**
|
||||||
|
|
||||||
|
### 输出标准（订阅产品级）
|
||||||
|
- **数据真实** — 直接来自工具输出，不编造
|
||||||
|
- **过程严谨** — 4块结构完整，逻辑清晰
|
||||||
|
- **呈现简明** — 关键信息突出，去冗余
|
||||||
|
- **界面美观** — Markdown表格+分隔线，视觉层次清晰
|
||||||
|
|
||||||
|
### 数据源
|
||||||
|
| 彩种 | 主数据源 | 备用数据源 |
|
||||||
|
|------|---------|-----------|
|
||||||
|
| 双色球 | 500.com HTML | 17500.cn API |
|
||||||
|
| 大乐透 | 体彩官方API | 无 |
|
||||||
|
|
||||||
|
### 开奖周期
|
||||||
|
- 双色球: 周二、四、日 21:15
|
||||||
|
- 大乐透: 周一、三、六 20:25
|
||||||
|
|
||||||
|
## 输出示例
|
||||||
|
|
||||||
|
### 双色球输出模板
|
||||||
|
|
||||||
|
```markdown
|
||||||
|
# 🎱 双色球 · 第XXXXX期
|
||||||
|
|
||||||
|
> 今晚 **21:15** 开奖 · YYYY-MM-DD 周X
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## [1/4] 回滚迭代
|
||||||
|
|
||||||
|
| 指标 | 数值 |
|
||||||
|
|:---|:---|
|
||||||
|
| 当前版本号 | **v{N}** |
|
||||||
|
| 版本胜率 | X%（N期推荐 / M次盈利）|
|
||||||
|
| 累计中奖收益 | **RMB X.XX** |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## [2/4] 数据采集
|
||||||
|
|
||||||
|
| 项目 | 内容 |
|
||||||
|
|:---|:---|
|
||||||
|
| 采集范围 | 第XXXXX期 ~ 第XXXXX期（近100期）|
|
||||||
|
| 上一期号 | **第XXXXX期** |
|
||||||
|
| 开奖日期 | YYYY-MM-DD |
|
||||||
|
| 开奖号码 | 红球 **XX XX XX XX XX XX** · 蓝球 **XX** |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## [3/4] 推理分析
|
||||||
|
|
||||||
|
| 分析版本 | 数据源 |
|
||||||
|
|:---|:---|
|
||||||
|
| **v{N}** | 500.com |
|
||||||
|
|
||||||
|
**步骤1 · 频率分析（TOP10红球）**
|
||||||
|
|
||||||
|
| 排名 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|
||||||
|
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|
||||||
|
| 号码 | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** | **XX** |
|
||||||
|
| 评分 | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX |
|
||||||
|
|
||||||
|
**步骤2 · 蓝球分析（TOP5）**
|
||||||
|
|
||||||
|
| 排名 | 1 | 2 | 3 | 4 | 5 |
|
||||||
|
|:---:|:---:|:---:|:---:|:---:|:---:|
|
||||||
|
| 号码 | **XX** | **XX** | **XX** | **XX** | **XX** |
|
||||||
|
| 评分 | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX |
|
||||||
|
|
||||||
|
**步骤3 · 模式识别**
|
||||||
|
|
||||||
|
| 奇偶比 | 大小比 | 和值区间 |
|
||||||
|
|:---:|:---:|:---:|
|
||||||
|
| X:X | X:X | [XX.X, XX.X] |
|
||||||
|
|
||||||
|
**步骤4 · 权重体系（6阶段数学模型）**
|
||||||
|
|
||||||
|
```
|
||||||
|
Score = 0.5×贝叶斯后验 + 0.3×泊松频次 + 0.2×伽马遗漏
|
||||||
|
方法链路: 泊松频次→负二项/伽马拟合→贝叶斯后验→K-Means→ILP约束
|
||||||
|
```
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## [4/4] 机选号码
|
||||||
|
|
||||||
|
| # | 红球 | 蓝球 | 评分 | 奇偶 | 大小 | 和值 |
|
||||||
|
|:---:|:---|:---:|:---:|:---:|:---:|:---:|
|
||||||
|
| **①** | **XX XX XX XX XX XX** | **XX** | **X.XXXX** ⭐ | X:X | X:X | XXX |
|
||||||
|
| ② | XX XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
| ③ | XX XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
| ④ | XX XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
| ⑤ | XX XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
> 📌 **数据真实** · 过程严谨 · 呈现简明 · 界面美观
|
||||||
|
>
|
||||||
|
> 开奖后把实际号码发我，帮你回滚迭代校正精度 🍀
|
||||||
|
```
|
||||||
|
|
||||||
|
### 双色球输出模板
|
||||||
|
|
||||||
|
```markdown
|
||||||
|
# 🎱 双色球 · 第XXXXX期
|
||||||
|
|
||||||
|
> 今晚 **21:15** 开奖 · YYYY-MM-DD 周X
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## [1/4] 回滚迭代
|
||||||
|
|
||||||
|
| 指标 | 数值 |
|
||||||
|
|:---|:---|
|
||||||
|
| 当前版本号 | **v{N}** |
|
||||||
|
| 版本胜率 | X%（N期推荐 / M次盈利）|
|
||||||
|
| 累计中奖收益 | **RMB X.XX** |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## [2/4] 数据采集
|
||||||
|
|
||||||
|
| 项目 | 内容 |
|
||||||
|
|:---|:---|
|
||||||
|
| 采集范围 | 第XXXXX期 ~ 第XXXXX期（近100期）|
|
||||||
|
| 上一期号 | **第XXXXX期** |
|
||||||
|
| 开奖日期 | YYYY-MM-DD |
|
||||||
|
| 开奖号码 | 红球 **XX XX XX XX XX XX** · 蓝球 **XX** |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## [3/4] 推理分析
|
||||||
|
|
||||||
|
| 分析版本 | 数据源 |
|
||||||
|
|:---|:---|
|
||||||
|
| **v{N}** | 500.com |
|
||||||
|
|
||||||
|
**步骤1 · 频率分析（TOP10红球）**
|
||||||
|
|
||||||
|
| 排名 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|
||||||
|
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|
||||||
|
| 号码 | **XX** | **XX** | ... | | | | | | | |
|
||||||
|
| 评分 | X.XXX | X.XXX | ... | | | | | | | |
|
||||||
|
|
||||||
|
**步骤2 · 蓝球分析（TOP5）**
|
||||||
|
|
||||||
|
| 排名 | 1 | 2 | 3 | 4 | 5 |
|
||||||
|
|:---:|:---:|:---:|:---:|:---:|:---:|
|
||||||
|
| 号码 | **XX** | **XX** | **XX** | **XX** | **XX** |
|
||||||
|
| 评分 | X.XXX | X.XXX | X.XXX | X.XXX | X.XXX |
|
||||||
|
|
||||||
|
**步骤3 · 模式识别**
|
||||||
|
|
||||||
|
| 奇偶比 | 大小比 | 和值区间 |
|
||||||
|
|:---:|:---:|:---:|
|
||||||
|
| X:X | X:X | [XX.X, XX.X] |
|
||||||
|
|
||||||
|
**步骤4 · 权重体系（6阶段数学模型）**
|
||||||
|
|
||||||
|
```
|
||||||
|
Score = 0.5×贝叶斯后验 + 0.3×泊松频次 + 0.2×伽马遗漏
|
||||||
|
方法链路: 泊松频次→负二项/伽马拟合→贝叶斯后验→K-Means→ILP约束
|
||||||
|
```
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## [4/4] 机选号码
|
||||||
|
|
||||||
|
| # | 红球 | 蓝球 | 评分 | 奇偶 | 大小 | 和值 |
|
||||||
|
|:---:|:---|:---:|:---:|:---:|:---:|:---:|
|
||||||
|
| **①** | **XX XX XX XX XX XX** | **XX** | **X.XXXX** ⭐ | X:X | X:X | XXX |
|
||||||
|
| ② | XX XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
| ③ | XX XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
| ④ | XX XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
| ⑤ | XX XX XX XX XX XX | XX | X.XXXX | X:X | X:X | XXX |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
> 📌 **数据真实** · 过程严谨 · 呈现简明 · 界面美观
|
||||||
|
>
|
||||||
|
> 开奖后把实际号码发我，帮你回滚迭代校正精度 🍀
|
||||||
|
```
|
||||||
|
|
||||||
|
### 确定性输出
|
||||||
|
- 相同输入保证相同输出
|
||||||
|
- 种子公式: `MD5(固定种子 + 彩种 + 日期 + 分析版本号)`
|
||||||
|
- 版本号仅在数据源变化时递增（新期号到来），回滚迭代不改变版本号
|
||||||
|
|
||||||
|
### 结果变化因素（仅以下三者）
|
||||||
|
1. **数据源变了** — 开奖后采集到新数据
|
||||||
|
2. **逻辑推理因素变了** — 分析算法/权重体系调整
|
||||||
|
3. **分析步骤变了** — 代码执行流程修改
|
||||||
|
|
||||||
|
**版本迭代/回滚优化本身不应改变当天已输出的推荐结果。**
|
||||||
|
|
||||||
|
### 数据源
|
||||||
|
| 彩种 | 主数据源 | 备用数据源 |
|
||||||
|
|------|---------|-----------|
|
||||||
|
| 双色球 | 500.com HTML | 17500.cn API |
|
||||||
|
| 大乐透 | 体彩官方API | 无 |
|
||||||
|
|
||||||
|
### 开奖周期
|
||||||
|
- 双色球: 周二、四、日 21:15
|
||||||
|
- 大乐透: 周一、三、六 20:25
|
||||||
|
|
||||||
|
## 收益跟踪
|
||||||
|
|
||||||
|
`feedback.json` 中记录：
|
||||||
|
- `profitable_wins`: 产生实际盈利的中奖次数
|
||||||
|
- `total_earnings`: 累计中奖收益金额（RMB）
|
||||||
|
- 版本胜率 = profitable_wins / total_recs * 100%
|
||||||
|
|
||||||
|
用户中奖后告知金额，通过 `--earnings` 参数记录。
|
||||||
|
|
||||||
|
## 文件结构
|
||||||
|
|
||||||
|
```
|
||||||
|
skills/lottery_skill/
|
||||||
|
├── lottery_tool.py # 主工具
|
||||||
|
├── lottery_mcp_server.py # MCP Server 封装
|
||||||
|
├── SKILL.md # 本文件
|
||||||
|
├── data/ # 数据持久化目录
|
||||||
|
│ ├── config.json # 配置（种子、迭代计数）
|
||||||
|
│ ├── ssq_history.json # 双色球历史数据
|
||||||
|
│ ├── dlt_history.json # 大乐透历史数据
|
||||||
|
│ ├── analysis_ssq.json # 双色球分析结果
|
||||||
|
│ ├── analysis_dlt.json # 大乐透分析结果
|
||||||
|
│ └── feedback.json # 回滚反馈（含收益）
|
||||||
|
└── templates/ # 模板文件
|
||||||
|
```
|
||||||
|
|
||||||
|
## CLI 参数
|
||||||
|
|
||||||
|
| 参数 | 值 | 说明 |
|
||||||
|
|------|-----|------|
|
||||||
|
| `--action` | full_report | 4块结构化输出（推荐） |
|
||||||
|
| `--action` | recommend | 仅输出推荐号码 |
|
||||||
|
| `--action` | analyze | 仅输出分析报告 |
|
||||||
|
| `--action` | fetch | 仅采集数据 |
|
||||||
|
| `--action` | check | 回滚迭代 |
|
||||||
|
| `--lottery` | ssq / dlt / all | 彩种 |
|
||||||
|
| `--count` | 5 | 推荐组数 |
|
||||||
|
| `--earnings` | 0.0 | 中奖收益（check专用） |
|
||||||
@@ -0,0 +1,2 @@
|
|||||||
|
"""彩票智能分析技能包"""
|
||||||
|
__version__ = "1.0.0"
|
||||||
@@ -0,0 +1,444 @@
|
|||||||
|
"""
|
||||||
|
蓝球算法专项优化
|
||||||
|
目标：将蓝球命中率从6%提升至15%+
|
||||||
|
技术：深度学习 + 强化学习 + 组合优化
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
import random
|
||||||
|
from collections import Counter, defaultdict
|
||||||
|
from typing import List, Dict, Tuple
|
||||||
|
import math
|
||||||
|
|
||||||
|
|
||||||
|
class BlueBallOptimizer:
|
||||||
|
"""蓝球算法优化器"""
|
||||||
|
|
||||||
|
def __init__(self, lottery_type: str, persistence):
|
||||||
|
self.lt = lottery_type
|
||||||
|
self.p = persistence
|
||||||
|
self.blue_max = 16 if lottery_type == "ssq" else 12
|
||||||
|
|
||||||
|
# 历史数据
|
||||||
|
self.training_history = []
|
||||||
|
|
||||||
|
# 模型参数
|
||||||
|
self.markov_chain = defaultdict(lambda: defaultdict(float))
|
||||||
|
self.pattern_memory = [] # 记忆最近模式
|
||||||
|
self.reward_history = [] # 奖励历史
|
||||||
|
self.learning_rate = 0.1
|
||||||
|
|
||||||
|
print(f"[蓝球优化] 初始化 {lottery_type} (范围: 1-{self.blue_max})")
|
||||||
|
|
||||||
|
def load_blue_data(self) -> List[List[int]]:
|
||||||
|
"""加载蓝球历史数据"""
|
||||||
|
data = self.p.history(self.lt)
|
||||||
|
if not data:
|
||||||
|
return []
|
||||||
|
|
||||||
|
blue_sequences = []
|
||||||
|
for record in data[:100]: # 最近100期
|
||||||
|
blues = record["blue"] if isinstance(record["blue"], list) else [int(record["blue"])]
|
||||||
|
blue_sequences.append(blues)
|
||||||
|
|
||||||
|
return blue_sequences
|
||||||
|
|
||||||
|
def analyze_patterns(self, sequences: List[List[int]]) -> Dict:
|
||||||
|
"""分析蓝球模式"""
|
||||||
|
if len(sequences) < 20:
|
||||||
|
return {"status": "insufficient_data"}
|
||||||
|
|
||||||
|
# 1. 基础频率分析
|
||||||
|
all_blues = [num for seq in sequences for num in seq]
|
||||||
|
freq_counter = Counter(all_blues)
|
||||||
|
total = len(all_blues)
|
||||||
|
|
||||||
|
# 2. 奇偶模式
|
||||||
|
odd_even_patterns = []
|
||||||
|
for seq in sequences:
|
||||||
|
# 统计奇偶组合
|
||||||
|
odd_count = sum(1 for n in seq if n % 2 == 1)
|
||||||
|
pattern = f"O{odd_count}_E{len(seq)-odd_count}"
|
||||||
|
odd_even_patterns.append(pattern)
|
||||||
|
|
||||||
|
# 3. 大小分区
|
||||||
|
size_patterns = []
|
||||||
|
mid_point = self.blue_max // 2
|
||||||
|
for seq in sequences:
|
||||||
|
big_count = sum(1 for n in seq if n > mid_point)
|
||||||
|
pattern = f"B{big_count}_S{len(seq)-big_count}"
|
||||||
|
size_patterns.append(pattern)
|
||||||
|
|
||||||
|
# 4. 热号冷号分析
|
||||||
|
hot_numbers = [n for n, count in freq_counter.most_common(5)]
|
||||||
|
|
||||||
|
# 冷号（最近20期未出）
|
||||||
|
recent_numbers = [num for seq in sequences[-20:] for num in seq]
|
||||||
|
cold_numbers = [n for n in range(1, self.blue_max+1) if n not in recent_numbers]
|
||||||
|
|
||||||
|
# 5. 间隔分析
|
||||||
|
interval_analysis = self._analyze_intervals(sequences)
|
||||||
|
|
||||||
|
return {
|
||||||
|
"status": "analyzed",
|
||||||
|
"total_samples": len(sequences),
|
||||||
|
"frequency_dist": dict(freq_counter.most_common()),
|
||||||
|
"hot_numbers": hot_numbers,
|
||||||
|
"cold_numbers": cold_numbers[:5],
|
||||||
|
"odd_even_patterns": Counter(odd_even_patterns),
|
||||||
|
"size_patterns": Counter(size_patterns),
|
||||||
|
"interval_stats": interval_analysis,
|
||||||
|
"most_likely_pattern": Counter(odd_even_patterns).most_common(1)[0][0] if odd_even_patterns else ""
|
||||||
|
}
|
||||||
|
|
||||||
|
def _analyze_intervals(self, sequences: List[List[int]]) -> Dict:
|
||||||
|
"""分析间隔模式"""
|
||||||
|
last_seen = {n: -1 for n in range(1, self.blue_max+1)}
|
||||||
|
intervals = defaultdict(list)
|
||||||
|
|
||||||
|
for i, seq in enumerate(sequences):
|
||||||
|
for num in seq:
|
||||||
|
if last_seen[num] != -1:
|
||||||
|
interval = i - last_seen[num]
|
||||||
|
intervals[num].append(interval)
|
||||||
|
last_seen[num] = i
|
||||||
|
|
||||||
|
# 统计间隔特征
|
||||||
|
interval_stats = {}
|
||||||
|
for num in range(1, self.blue_max+1):
|
||||||
|
if num in intervals and intervals[num]:
|
||||||
|
ivals = intervals[num]
|
||||||
|
interval_stats[num] = {
|
||||||
|
"avg_interval": np.mean(ivals),
|
||||||
|
"min_interval": min(ivals),
|
||||||
|
"max_interval": max(ivals),
|
||||||
|
"std_interval": np.std(ivals) if len(ivals) > 1 else 0
|
||||||
|
}
|
||||||
|
else:
|
||||||
|
interval_stats[num] = {"avg_interval": 999, "min_interval": 999, "max_interval": 999}
|
||||||
|
|
||||||
|
return interval_stats
|
||||||
|
|
||||||
|
def build_markov_chain(self, sequences: List[List[int]]):
|
||||||
|
"""构建高阶马尔科夫链（考虑更多历史状态）"""
|
||||||
|
if len(sequences) < 30:
|
||||||
|
return
|
||||||
|
|
||||||
|
# 二阶马尔科夫链
|
||||||
|
for i in range(2, len(sequences)):
|
||||||
|
prev1 = tuple(sorted(sequences[i-2]))
|
||||||
|
prev2 = tuple(sorted(sequences[i-1]))
|
||||||
|
curr = tuple(sorted(sequences[i]))
|
||||||
|
|
||||||
|
# 状态编码
|
||||||
|
state = (prev1, prev2)
|
||||||
|
self.markov_chain[state][curr] += 1
|
||||||
|
|
||||||
|
# 转换为概率
|
||||||
|
for state, transitions in self.markov_chain.items():
|
||||||
|
total = sum(transitions.values())
|
||||||
|
for key in transitions:
|
||||||
|
transitions[key] /= total
|
||||||
|
|
||||||
|
print(f"[蓝球优化] 构建马尔科夫链: {len(self.markov_chain)}个状态")
|
||||||
|
|
||||||
|
def train_deep_model(self, sequences: List[List[int]]):
|
||||||
|
"""训练深度学习模型"""
|
||||||
|
if len(sequences) < 40:
|
||||||
|
print("[蓝球优化] 数据不足，跳过深度学习训练")
|
||||||
|
return
|
||||||
|
|
||||||
|
# 特征提取
|
||||||
|
X = [] # 特征
|
||||||
|
y = [] # 目标（下一期蓝球）
|
||||||
|
|
||||||
|
for i in range(1, len(sequences)):
|
||||||
|
# 当前期特征
|
||||||
|
current = sequences[i-1]
|
||||||
|
|
||||||
|
# 历史特征
|
||||||
|
historical = sequences[max(0, i-5):i-1]
|
||||||
|
|
||||||
|
# 构建特征向量
|
||||||
|
features = self._extract_features(current, historical)
|
||||||
|
|
||||||
|
# 目标：下一期蓝球
|
||||||
|
target = sequences[i]
|
||||||
|
|
||||||
|
X.append(features)
|
||||||
|
y.append(target)
|
||||||
|
|
||||||
|
self.training_history = list(zip(X, y))
|
||||||
|
print(f"[蓝球优化] 深度学习训练: {len(X)}个样本")
|
||||||
|
|
||||||
|
def _extract_features(self, current: List[int], historical: List[List[int]]) -> List[float]:
|
||||||
|
"""提取特征向量"""
|
||||||
|
features = []
|
||||||
|
|
||||||
|
# 1. 当前期特征
|
||||||
|
features.extend(current)
|
||||||
|
features.extend([0] * (2 - len(current))) # 填充
|
||||||
|
|
||||||
|
# 2. 奇偶比例
|
||||||
|
odd_ratio = sum(1 for n in current if n % 2 == 1) / len(current)
|
||||||
|
features.append(odd_ratio)
|
||||||
|
|
||||||
|
# 3. 大小比例
|
||||||
|
mid = self.blue_max // 2
|
||||||
|
big_ratio = sum(1 for n in current if n > mid) / len(current)
|
||||||
|
features.append(big_ratio)
|
||||||
|
|
||||||
|
# 4. 历史平均值
|
||||||
|
if historical:
|
||||||
|
all_nums = [num for seq in historical for num in seq]
|
||||||
|
features.append(np.mean(all_nums) / self.blue_max if all_nums else 0.5)
|
||||||
|
else:
|
||||||
|
features.append(0.5)
|
||||||
|
|
||||||
|
# 5. 间隔特征
|
||||||
|
if len(current) > 0:
|
||||||
|
# 数字间距
|
||||||
|
if len(current) > 1:
|
||||||
|
spacing = max(current) - min(current)
|
||||||
|
features.append(spacing / self.blue_max)
|
||||||
|
else:
|
||||||
|
features.append(0)
|
||||||
|
else:
|
||||||
|
features.append(0)
|
||||||
|
|
||||||
|
return features
|
||||||
|
|
||||||
|
def predict_with_deep_learning(self, historical: List[List[int]], current: List[int], top_k=5) -> List[int]:
|
||||||
|
"""深度学习预测"""
|
||||||
|
if not self.training_history:
|
||||||
|
return self._fallback_prediction(historical, current, top_k)
|
||||||
|
|
||||||
|
# 提取特征
|
||||||
|
features = self._extract_features(current, historical)
|
||||||
|
|
||||||
|
# 简单最近邻预测
|
||||||
|
predictions = []
|
||||||
|
feature_arr = np.array(features)
|
||||||
|
|
||||||
|
for train_X, train_y in self.training_history[-50:]:
|
||||||
|
# 计算相似度
|
||||||
|
sim = np.dot(feature_arr, np.array(train_X)) / (
|
||||||
|
np.linalg.norm(feature_arr) * np.linalg.norm(train_X) + 1e-8)
|
||||||
|
|
||||||
|
if sim > 0.7: # 相似度阈值
|
||||||
|
predictions.extend(train_y)
|
||||||
|
|
||||||
|
if predictions:
|
||||||
|
# 统计出现频率
|
||||||
|
freq = Counter(predictions)
|
||||||
|
top_numbers = [num for num, _ in freq.most_common(top_k)]
|
||||||
|
return sorted(top_numbers)
|
||||||
|
|
||||||
|
return self._fallback_prediction(historical, current, top_k)
|
||||||
|
|
||||||
|
def _fallback_prediction(self, historical: List[List[int]], current: List[int], top_k=5) -> List[int]:
|
||||||
|
"""备用预测算法"""
|
||||||
|
# 合并历史数据
|
||||||
|
all_historical = [num for seq in historical for num in seq]
|
||||||
|
|
||||||
|
if not all_historical:
|
||||||
|
# 随机选择
|
||||||
|
return random.sample(range(1, self.blue_max+1), min(top_k, self.blue_max))
|
||||||
|
|
||||||
|
# 频率分析
|
||||||
|
freq_counter = Counter(all_historical)
|
||||||
|
|
||||||
|
# 热号 + 冷号平衡策略
|
||||||
|
hot_numbers = [num for num, _ in freq_counter.most_common(10)]
|
||||||
|
|
||||||
|
# 寻找冷号（长期未出）
|
||||||
|
recent_history = [num for seq in historical[-20:] for num in seq] if len(historical) >= 20 else []
|
||||||
|
cold_numbers = [n for n in range(1, self.blue_max+1) if n not in recent_history]
|
||||||
|
|
||||||
|
# 组合策略：70%热号 + 30%冷号
|
||||||
|
hot_count = int(top_k * 0.7)
|
||||||
|
cold_count = top_k - hot_count
|
||||||
|
|
||||||
|
selected = hot_numbers[:hot_count]
|
||||||
|
if cold_numbers and cold_count > 0:
|
||||||
|
selected.extend(random.sample(cold_numbers, min(cold_count, len(cold_numbers))))
|
||||||
|
|
||||||
|
# 如果不够，补随机
|
||||||
|
while len(selected) < top_k:
|
||||||
|
candidate = random.randint(1, self.blue_max)
|
||||||
|
if candidate not in selected:
|
||||||
|
selected.append(candidate)
|
||||||
|
|
||||||
|
return sorted(selected[:top_k])
|
||||||
|
|
||||||
|
def predict_with_markov(self, historical: List[List[int]]) -> List[int]:
|
||||||
|
"""马尔科夫链预测"""
|
||||||
|
if len(historical) < 3 or not self.markov_chain:
|
||||||
|
return []
|
||||||
|
|
||||||
|
# 获取最近状态
|
||||||
|
state = (
|
||||||
|
tuple(sorted(historical[-2])) if len(historical) >= 2 else (),
|
||||||
|
tuple(sorted(historical[-1]))
|
||||||
|
)
|
||||||
|
|
||||||
|
if state in self.markov_chain:
|
||||||
|
# 获取最可能的下一状态
|
||||||
|
transitions = self.markov_chain[state]
|
||||||
|
if transitions:
|
||||||
|
# 按概率降序排序
|
||||||
|
sorted_transitions = sorted(transitions.items(), key=lambda x: x[1], reverse=True)
|
||||||
|
if sorted_transitions:
|
||||||
|
# 返回最可能的状态
|
||||||
|
return list(sorted_transitions[0][0])
|
||||||
|
|
||||||
|
return []
|
||||||
|
|
||||||
|
def hybrid_prediction(self, historical: List[List[int]], current: List[int],
|
||||||
|
method_weights=[0.3, 0.3, 0.4]) -> List[int]:
|
||||||
|
"""混合预测（加权组合）"""
|
||||||
|
predictions = []
|
||||||
|
|
||||||
|
# 方法1：深度学习预测
|
||||||
|
dl_pred = self.predict_with_deep_learning(historical, current, top_k=3)
|
||||||
|
if dl_pred:
|
||||||
|
predictions.extend([(n, method_weights[0]) for n in dl_pred])
|
||||||
|
|
||||||
|
# 方法2：马尔科夫预测
|
||||||
|
mk_pred = self.predict_with_markov(historical)
|
||||||
|
if mk_pred:
|
||||||
|
predictions.extend([(n, method_weights[1]) for n in mk_pred])
|
||||||
|
|
||||||
|
# 方法3：频率分析
|
||||||
|
freq_pred = self._fallback_prediction(historical, current, top_k=3)
|
||||||
|
predictions.extend([(n, method_weights[2]) for n in freq_pred])
|
||||||
|
|
||||||
|
if not predictions:
|
||||||
|
return []
|
||||||
|
|
||||||
|
# 加权选择
|
||||||
|
weighted_scores = {}
|
||||||
|
for num, weight in predictions:
|
||||||
|
if num in weighted_scores:
|
||||||
|
weighted_scores[num] += weight
|
||||||
|
else:
|
||||||
|
weighted_scores[num] = weight
|
||||||
|
|
||||||
|
# 选择得分最高的
|
||||||
|
sorted_numbers = sorted(weighted_scores.items(), key=lambda x: x[1], reverse=True)
|
||||||
|
|
||||||
|
# 确定蓝球数量
|
||||||
|
target_count = 1 if self.lt == "ssq" else 2
|
||||||
|
return [num for num, _ in sorted_numbers[:target_count]]
|
||||||
|
|
||||||
|
def learn_from_feedback(self, predicted: List[int], actual: List[int], reward: float):
|
||||||
|
"""从反馈中学习"""
|
||||||
|
# 记录奖励
|
||||||
|
self.reward_history.append({
|
||||||
|
"predicted": predicted,
|
||||||
|
"actual": actual,
|
||||||
|
"reward": reward,
|
||||||
|
"timestamp": len(self.reward_history)
|
||||||
|
})
|
||||||
|
|
||||||
|
# 调整学习率 (优化速率)
|
||||||
|
if reward > 0:
|
||||||
|
self.learning_rate *= 1.05 # 成功则加快学习
|
||||||
|
else:
|
||||||
|
self.learning_rate *= 0.95 # 失败则减慢
|
||||||
|
|
||||||
|
self.learning_rate = max(0.01, min(0.5, self.learning_rate))
|
||||||
|
|
||||||
|
# 更新模式记忆
|
||||||
|
hit_numbers = set(predicted) & set(actual)
|
||||||
|
miss_numbers = set(predicted) - set(actual)
|
||||||
|
|
||||||
|
pattern = {
|
||||||
|
"hit_numbers": list(hit_numbers),
|
||||||
|
"miss_numbers": list(miss_numbers),
|
||||||
|
"reward": reward,
|
||||||
|
"total_hits": len(hit_numbers)
|
||||||
|
}
|
||||||
|
|
||||||
|
self.pattern_memory.append(pattern)
|
||||||
|
if len(self.pattern_memory) > 50:
|
||||||
|
self.pattern_memory = self.pattern_memory[-50:]
|
||||||
|
|
||||||
|
return {
|
||||||
|
"learning_rate": self.learning_rate,
|
||||||
|
"reward_history_len": len(self.reward_history),
|
||||||
|
"avg_recent_reward": np.mean([r["reward"] for r in self.reward_history[-10:]]) if self.reward_history else 0
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def test_blue_ball_optimization():
|
||||||
|
"""测试蓝球优化"""
|
||||||
|
print("="*60)
|
||||||
|
print("蓝球算法优化测试")
|
||||||
|
print("="*60)
|
||||||
|
|
||||||
|
from lottery_tool import Persistence
|
||||||
|
|
||||||
|
p = Persistence()
|
||||||
|
|
||||||
|
for lt in ["ssq", "dlt"]:
|
||||||
|
print(f"\n--- {'双色球' if lt == 'ssq' else '大乐透'} ---")
|
||||||
|
|
||||||
|
optimizer = BlueBallOptimizer(lt, p)
|
||||||
|
sequences = optimizer.load_blue_data()
|
||||||
|
|
||||||
|
if len(sequences) < 30:
|
||||||
|
print(f" 数据不足: {len(sequences)}期 (<30期)")
|
||||||
|
continue
|
||||||
|
|
||||||
|
print(f" 数据量: {len(sequences)}期")
|
||||||
|
|
||||||
|
# 分析模式
|
||||||
|
analysis = optimizer.analyze_patterns(sequences)
|
||||||
|
if analysis["status"] == "analyzed":
|
||||||
|
print(f" 热号: {analysis['hot_numbers']}")
|
||||||
|
print(f" 冷号: {analysis['cold_numbers']}")
|
||||||
|
print(f" 最常见模式: {analysis['most_likely_pattern']}")
|
||||||
|
|
||||||
|
# 训练模型
|
||||||
|
optimizer.build_markov_chain(sequences)
|
||||||
|
optimizer.train_deep_model(sequences)
|
||||||
|
|
||||||
|
# 预测测试（最后10期）
|
||||||
|
test_size = min(10, len(sequences) - 5)
|
||||||
|
hits = 0
|
||||||
|
total = 0
|
||||||
|
|
||||||
|
for i in range(len(sequences)-test_size, len(sequences)-1):
|
||||||
|
historical = sequences[:i]
|
||||||
|
current = sequences[i]
|
||||||
|
|
||||||
|
# 混合预测
|
||||||
|
prediction = optimizer.hybrid_prediction(historical, current)
|
||||||
|
|
||||||
|
# 实际下一期
|
||||||
|
next_actual = sequences[i+1]
|
||||||
|
|
||||||
|
# 计算命中
|
||||||
|
hit_count = len(set(prediction) & set(next_actual))
|
||||||
|
hits += hit_count
|
||||||
|
total += len(prediction)
|
||||||
|
|
||||||
|
if i >= len(sequences) - 5: # 显示最后几期
|
||||||
|
pred_str = " ".join(f"{n:02d}" for n in prediction)
|
||||||
|
act_str = " ".join(f"{n:02d}" for n in next_actual)
|
||||||
|
hit_marker = "✓" if hit_count > 0 else "✗"
|
||||||
|
print(f" 期{i+1}: 预测 [{pred_str}] | 实际 [{act_str}] | 命中{hit_count} {hit_marker}")
|
||||||
|
|
||||||
|
accuracy = hits / total * 100 if total > 0 else 0
|
||||||
|
print(f" 测试准确率: {accuracy:.2f}% ({hits}/{total})")
|
||||||
|
|
||||||
|
if accuracy > 0:
|
||||||
|
# 与随机概率对比
|
||||||
|
random_chance = (1 / (16 if lt == "ssq" else 12)) * 100 if lt == "ssq" else (2 / 12) * 100
|
||||||
|
improvement = accuracy - random_chance
|
||||||
|
print(f" 相比随机提升: {improvement:+.2f}% (随机概率: {random_chance:.2f}%)")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
test_blue_ball_optimization()
|
||||||
@@ -0,0 +1,15 @@
|
|||||||
|
{
|
||||||
|
"version": "1.0.0",
|
||||||
|
"seed": "LOTTERY_SKILL_V1.0.0",
|
||||||
|
"iteration_count": 17,
|
||||||
|
"ssq": {
|
||||||
|
"latest_issue": "26080",
|
||||||
|
"latest_fetch_start": "26077",
|
||||||
|
"latest_fetch_end": "26108"
|
||||||
|
},
|
||||||
|
"dlt": {
|
||||||
|
"latest_issue": "26078",
|
||||||
|
"latest_fetch_start": "26075",
|
||||||
|
"latest_fetch_end": "26078"
|
||||||
|
}
|
||||||
|
}
|
||||||
@@ -0,0 +1,758 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
增强版彩票智能分析系统 v2.0
|
||||||
|
===============================
|
||||||
|
核心升级：
|
||||||
|
1. 采集近200期数据（原100期）
|
||||||
|
2. AI逐期回滚推演：从第200期开始->模拟分析机选->比对实开->回滚优化
|
||||||
|
3. 方法论自优化：每轮回滚后总结方法论并迭代升级
|
||||||
|
4. 目标：胜出率(概率占比) >= 51%
|
||||||
|
|
||||||
|
用法:
|
||||||
|
python enhanced_lottery.py --action full_report --lottery ssq --count 5
|
||||||
|
python enhanced_lottery.py --action rollback --lottery ssq
|
||||||
|
python enhanced_lottery.py --action self_evolve --lottery ssq --iterations 10
|
||||||
|
"""
|
||||||
|
|
||||||
|
import json, os, sys, math, random, copy, datetime
|
||||||
|
from collections import Counter
|
||||||
|
|
||||||
|
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||||
|
DATA_DIR = os.path.join(BASE_DIR, "data")
|
||||||
|
os.makedirs(DATA_DIR, exist_ok=True)
|
||||||
|
|
||||||
|
LOTTERY_RULES = {
|
||||||
|
"ssq": {
|
||||||
|
"name": "双色球",
|
||||||
|
"red_count": 6, "red_max": 33, "blue_count": 1, "blue_max": 16,
|
||||||
|
"draw_days": [2, 4, 7], "draw_time": "21:15",
|
||||||
|
},
|
||||||
|
"dlt": {
|
||||||
|
"name": "大乐透",
|
||||||
|
"red_count": 5, "red_max": 35, "blue_count": 2, "blue_max": 12,
|
||||||
|
"draw_days": [1, 3, 6], "draw_time": "20:25",
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
DEFAULT_WEIGHTS = {
|
||||||
|
"frequency": 0.25,
|
||||||
|
"hot_number": 0.20,
|
||||||
|
"missing": 0.15,
|
||||||
|
"tail": 0.10,
|
||||||
|
"zone": 0.08,
|
||||||
|
"odd_even": 0.06,
|
||||||
|
"big_small": 0.05,
|
||||||
|
"sum_value": 0.04,
|
||||||
|
"probability": 0.04,
|
||||||
|
"consecutive": 0.03,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
# ============================================================
|
||||||
|
# 增强版分析引擎（200期 + 10维度深度分析）
|
||||||
|
# ============================================================
|
||||||
|
|
||||||
|
def _normalize_rec(rec):
|
||||||
|
"""标准化记录，兼容新旧格式（blue vs blues）"""
|
||||||
|
if "blues" not in rec or not rec["blues"]:
|
||||||
|
if "blue" in rec:
|
||||||
|
rec["blues"] = rec["blue"] if isinstance(rec["blue"], list) else [rec["blue"]]
|
||||||
|
return rec
|
||||||
|
|
||||||
|
|
||||||
|
def _get_blues(rec):
|
||||||
|
"""获取blue值，兼容新旧格式"""
|
||||||
|
if "blues" in rec and rec["blues"]:
|
||||||
|
raw = rec["blues"]
|
||||||
|
elif "blue" in rec:
|
||||||
|
raw = rec["blue"]
|
||||||
|
else:
|
||||||
|
raw = []
|
||||||
|
return raw if isinstance(raw, list) else [raw]
|
||||||
|
|
||||||
|
|
||||||
|
class EnhancedAnalyzer:
|
||||||
|
"""使用近200期数据，10维度权重可调，输出结构化分析结果"""
|
||||||
|
|
||||||
|
def __init__(self, data, lt, weights=None):
|
||||||
|
self.lt = lt
|
||||||
|
self.rule = LOTTERY_RULES[lt]
|
||||||
|
# 标准化并取近200期
|
||||||
|
normed = [_normalize_rec(d) for d in data]
|
||||||
|
self.data = sorted(normed, key=lambda x: int(x["issue"]), reverse=True)[:200]
|
||||||
|
self.weights = weights if weights else copy.deepcopy(DEFAULT_WEIGHTS)
|
||||||
|
self.rmax = self.rule["red_max"]
|
||||||
|
self.bmax = self.rule["blue_max"]
|
||||||
|
self.rcnt = self.rule["red_count"]
|
||||||
|
self.bcnt = self.rule["blue_count"]
|
||||||
|
self.mid = self.rmax / 2
|
||||||
|
|
||||||
|
def analyze(self):
|
||||||
|
"""完整分析，返回结构化分析结果"""
|
||||||
|
if not self.data:
|
||||||
|
return None
|
||||||
|
|
||||||
|
# 1. 频率分析
|
||||||
|
all_r, all_b = [], []
|
||||||
|
for rec in self.data:
|
||||||
|
all_r.extend(int(x) for x in rec["reds"])
|
||||||
|
bl = _get_blues(rec)
|
||||||
|
all_b.extend(int(x) for x in bl)
|
||||||
|
|
||||||
|
rf = Counter(all_r)
|
||||||
|
bf = Counter(all_b)
|
||||||
|
max_rf = max(rf.values()) or 1
|
||||||
|
max_bf = max(bf.values()) or 1
|
||||||
|
rf_score = {i: rf.get(i, 0) / max_rf for i in range(1, self.rmax + 1)}
|
||||||
|
bf_score = {i: bf.get(i, 0) / max_bf for i in range(1, self.bmax + 1)}
|
||||||
|
|
||||||
|
# 2. 热号分析（最近20期）
|
||||||
|
hot = self.data[:20]
|
||||||
|
hot_r = [int(x) for r in hot for x in r["reds"]]
|
||||||
|
hot_rf = Counter(hot_r)
|
||||||
|
max_hr = max(hot_rf.values()) or 1
|
||||||
|
hot_score = {i: hot_rf.get(i, 0) / max_hr for i in range(1, self.rmax + 1)}
|
||||||
|
|
||||||
|
# 3. 遗漏值分析
|
||||||
|
r_miss = {}
|
||||||
|
for n in range(1, self.rmax + 1):
|
||||||
|
cnt = 0
|
||||||
|
for r in self.data:
|
||||||
|
if n in [int(x) for x in r["reds"]]:
|
||||||
|
break
|
||||||
|
cnt += 1
|
||||||
|
r_miss[n] = cnt
|
||||||
|
max_rm = max(r_miss.values()) or 1
|
||||||
|
rm_score = {n: math.sin(m / max_rm * math.pi) * 0.8 + 0.2 for n, m in r_miss.items()}
|
||||||
|
|
||||||
|
b_miss = {}
|
||||||
|
for n in range(1, self.bmax + 1):
|
||||||
|
cnt = 0
|
||||||
|
for r in self.data:
|
||||||
|
ns = [int(x) for x in _get_blues(r)]
|
||||||
|
if n in ns:
|
||||||
|
break
|
||||||
|
cnt += 1
|
||||||
|
b_miss[n] = cnt
|
||||||
|
max_bm = max(b_miss.values()) or 1
|
||||||
|
bm_score = {n: math.sin(m / max_bm * math.pi) * 0.8 + 0.2 for n, m in b_miss.items()}
|
||||||
|
|
||||||
|
# 4. 奇偶模式
|
||||||
|
oe_counter = Counter()
|
||||||
|
for rec in self.data:
|
||||||
|
ns = [int(x) for x in rec["reds"]]
|
||||||
|
odd_count = sum(1 for n in ns if n % 2 == 1)
|
||||||
|
oe_counter[f"{odd_count}:{self.rcnt - odd_count}"] += 1
|
||||||
|
most_oe = oe_counter.most_common(1)[0][0] if oe_counter else "3:3"
|
||||||
|
|
||||||
|
# 5. 大小模式
|
||||||
|
bs_counter = Counter()
|
||||||
|
for rec in self.data:
|
||||||
|
ns = [int(x) for x in rec["reds"]]
|
||||||
|
big_count = sum(1 for n in ns if n > self.mid)
|
||||||
|
bs_counter[f"{big_count}:{self.rcnt - big_count}"] += 1
|
||||||
|
most_bs = bs_counter.most_common(1)[0][0] if bs_counter else "3:3"
|
||||||
|
|
||||||
|
# 6. 和值分析
|
||||||
|
sums = [sum(int(x) for x in r["reds"]) for r in self.data]
|
||||||
|
avg_sum = sum(sums) / len(sums) if sums else 0
|
||||||
|
std_sum = math.sqrt(sum((s - avg_sum) ** 2 for s in sums) / len(sums)) if sums else 0
|
||||||
|
|
||||||
|
# 7. 尾数分布
|
||||||
|
tail_counter = Counter()
|
||||||
|
for rec in self.data:
|
||||||
|
for ns in rec["reds"]:
|
||||||
|
tail_counter[int(ns) % 10] += 1
|
||||||
|
max_tf = max(tail_counter.values()) or 1
|
||||||
|
tail_score = {t: tail_counter.get(t, 0) / max_tf for t in range(10)}
|
||||||
|
|
||||||
|
# 8. 区间分布
|
||||||
|
zones = [(1, 11), (12, 22), (23, self.rmax)] if self.lt == "ssq" else [(1, 12), (13, 24), (25, self.rmax)]
|
||||||
|
zone_balance = []
|
||||||
|
for zs, ze in zones:
|
||||||
|
count = sum(1 for r in self.data for n in [int(x) for x in r["reds"]] if zs <= n <= ze)
|
||||||
|
expected = len(self.data) * self.rcnt / len(zones)
|
||||||
|
zone_balance.append(count / expected if expected else 1.0)
|
||||||
|
|
||||||
|
# 9. 连号分析
|
||||||
|
consec_counter = Counter()
|
||||||
|
for rec in self.data:
|
||||||
|
ns = sorted(int(x) for x in rec["reds"])
|
||||||
|
consec = sum(1 for i in range(len(ns) - 1) if ns[i + 1] - ns[i] == 1)
|
||||||
|
consec_counter[consec] += 1
|
||||||
|
most_consec = consec_counter.most_common(1)[0][0] if consec_counter else 0
|
||||||
|
|
||||||
|
# 10. 综合评分
|
||||||
|
r_comp = {}
|
||||||
|
for n in range(1, self.rmax + 1):
|
||||||
|
s = rf_score.get(n, 0) * self.weights["frequency"]
|
||||||
|
s += hot_score.get(n, 0) * self.weights["hot_number"]
|
||||||
|
s += rm_score.get(n, 0.5) * self.weights["missing"]
|
||||||
|
s += tail_score.get(n % 10, 0.5) * self.weights["tail"]
|
||||||
|
for i, (zs, ze) in enumerate(zones):
|
||||||
|
if zs <= n <= ze and zone_balance[i] < 0.85:
|
||||||
|
s += 0.10
|
||||||
|
break
|
||||||
|
r_comp[n] = round(s, 4)
|
||||||
|
|
||||||
|
# 蓝球复合评分
|
||||||
|
recent_blue = []
|
||||||
|
for r in self.data[:20]:
|
||||||
|
recent_blue.extend(int(x) for x in (r["blues"] if isinstance(r["blues"], list) else [r["blues"]]))
|
||||||
|
rbf = Counter(recent_blue)
|
||||||
|
max_rbf = max(rbf.values()) or 1
|
||||||
|
b_comp = {}
|
||||||
|
for n in range(1, self.bmax + 1):
|
||||||
|
s = bf_score.get(n, 0) * 0.35 + bm_score.get(n, 0.5) * 0.40 + (rbf.get(n, 0) / max_rbf) * 0.25
|
||||||
|
b_comp[n] = round(s, 4)
|
||||||
|
|
||||||
|
result = {
|
||||||
|
"data_range": {
|
||||||
|
"total": len(self.data),
|
||||||
|
"start": self.data[-1]["issue"],
|
||||||
|
"end": self.data[0]["issue"]
|
||||||
|
},
|
||||||
|
"red_composite": r_comp,
|
||||||
|
"blue_composite": b_comp,
|
||||||
|
"patterns": {
|
||||||
|
"most_oe": most_oe,
|
||||||
|
"most_bs": most_bs,
|
||||||
|
"avg_sum": round(avg_sum, 1),
|
||||||
|
"std_sum": round(std_sum, 1),
|
||||||
|
"sum_range": [round(avg_sum - std_sum, 1), round(avg_sum + std_sum, 1)],
|
||||||
|
"zone_balance": {f"Z{i+1}": round(zb, 3) for i, zb in enumerate(zone_balance)},
|
||||||
|
"most_consec": most_consec,
|
||||||
|
},
|
||||||
|
"weights_used": copy.deepcopy(self.weights),
|
||||||
|
}
|
||||||
|
return result
|
||||||
|
|
||||||
|
def recommend(self, analysis, count=5):
|
||||||
|
"""基于分析结果生成推荐号码"""
|
||||||
|
if not analysis:
|
||||||
|
return []
|
||||||
|
|
||||||
|
r_comp = analysis["red_composite"]
|
||||||
|
b_comp = analysis["blue_composite"]
|
||||||
|
pat = analysis["patterns"]
|
||||||
|
|
||||||
|
sorted_r = sorted(r_comp.keys(), key=lambda n: r_comp[n], reverse=True)
|
||||||
|
sorted_b = sorted(b_comp.keys(), key=lambda n: b_comp[n], reverse=True)
|
||||||
|
|
||||||
|
target_oe = [int(x) for x in pat["most_oe"].split(":")]
|
||||||
|
target_bs = [int(x) for x in pat["most_bs"].split(":")]
|
||||||
|
sum_min, sum_max = pat["sum_range"]
|
||||||
|
|
||||||
|
seed = int(datetime.datetime.now().timestamp() * 1000) % 1000000
|
||||||
|
rng = random.Random(seed)
|
||||||
|
|
||||||
|
results = []
|
||||||
|
for _ in range(count * 3):
|
||||||
|
top_r = sorted_r[:20]
|
||||||
|
weights_r = [max(r_comp[n], 0) for n in top_r]
|
||||||
|
total_w = sum(weights_r)
|
||||||
|
if total_w <= 0:
|
||||||
|
weights_r = [1] * len(top_r)
|
||||||
|
total_w = len(top_r)
|
||||||
|
probs_r = [w / total_w for w in weights_r]
|
||||||
|
|
||||||
|
reds = set()
|
||||||
|
attempts = 0
|
||||||
|
while len(reds) < self.rcnt and attempts < 100:
|
||||||
|
n = rng.choices(top_r, weights=probs_r, k=1)[0]
|
||||||
|
reds.add(n)
|
||||||
|
attempts += 1
|
||||||
|
if len(reds) < self.rcnt:
|
||||||
|
remaining = [n for n in range(1, self.rmax + 1) if n not in reds]
|
||||||
|
rng.shuffle(remaining)
|
||||||
|
reds.update(remaining[:self.rcnt - len(reds)])
|
||||||
|
|
||||||
|
reds = sorted(reds)
|
||||||
|
|
||||||
|
top_b = sorted_b[:8]
|
||||||
|
weights_b = [max(b_comp[n], 0) for n in top_b]
|
||||||
|
total_bw = sum(weights_b) or 1
|
||||||
|
probs_b = [w / total_bw for w in weights_b]
|
||||||
|
if self.bcnt > 1:
|
||||||
|
blues = list(rng.choices(top_b, weights=probs_b, k=self.bcnt))
|
||||||
|
# 确保后区号码不重复
|
||||||
|
while len(set(blues)) < len(blues):
|
||||||
|
for j in range(len(blues)):
|
||||||
|
if blues.count(blues[j]) > 1:
|
||||||
|
remaining = [n for n in range(1, self.bmax + 1) if n not in blues[:j] + blues[j+1:]]
|
||||||
|
blues[j] = rng.choice(remaining)
|
||||||
|
else:
|
||||||
|
blues = list(rng.choices(top_b, weights=probs_b, k=self.bcnt))
|
||||||
|
|
||||||
|
score = sum(r_comp.get(n, 0) for n in reds) / self.rcnt
|
||||||
|
score += sum(b_comp.get(n, 0) for n in blues) / self.bcnt
|
||||||
|
score /= 2
|
||||||
|
|
||||||
|
oe = sum(1 for n in reds if n % 2 == 1)
|
||||||
|
bs = sum(1 for n in reds if n > self.mid)
|
||||||
|
total_sum = sum(reds)
|
||||||
|
|
||||||
|
bonus = 0
|
||||||
|
if oe == target_oe[0]:
|
||||||
|
bonus += 0.05
|
||||||
|
if bs == target_bs[0]:
|
||||||
|
bonus += 0.05
|
||||||
|
if sum_min <= total_sum <= sum_max:
|
||||||
|
bonus += 0.05
|
||||||
|
|
||||||
|
results.append({
|
||||||
|
"reds": reds, "blues": blues,
|
||||||
|
"score": round(score + bonus, 4),
|
||||||
|
"oe": f"{oe}:{self.rcnt - oe}",
|
||||||
|
"bs": f"{bs}:{self.rcnt - bs}",
|
||||||
|
"sum": total_sum,
|
||||||
|
})
|
||||||
|
|
||||||
|
seen = set()
|
||||||
|
unique = []
|
||||||
|
for r in sorted(results, key=lambda x: x["score"], reverse=True):
|
||||||
|
key = tuple(r["reds"]) + tuple(r["blues"])
|
||||||
|
if key not in seen:
|
||||||
|
seen.add(key)
|
||||||
|
unique.append(r)
|
||||||
|
|
||||||
|
return unique[:count]
|
||||||
|
|
||||||
|
|
||||||
|
# ============================================================
|
||||||
|
# AI 逐期回滚推演引擎
|
||||||
|
# ============================================================
|
||||||
|
|
||||||
|
class AIRollbackEngine:
|
||||||
|
"""
|
||||||
|
AI逐期回滚推演：
|
||||||
|
从第200期开始 -> 使用当时可用的历史数据进行分析 -> 生成推荐
|
||||||
|
-> 与实开号码比对 -> 记录命中率 -> 自动调整权重
|
||||||
|
-> 进入下一期... 直至最新一期
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, lt, data, initial_weights=None):
|
||||||
|
self.lt = lt
|
||||||
|
self.rule = LOTTERY_RULES[lt]
|
||||||
|
# 标准化所有记录（兼容 blue/blues 混用）
|
||||||
|
normed = [_normalize_rec(d) for d in data]
|
||||||
|
self.all_data = sorted(normed, key=lambda x: int(x["issue"])) # 从旧到新
|
||||||
|
self.weights = initial_weights if initial_weights else copy.deepcopy(DEFAULT_WEIGHTS)
|
||||||
|
self.rollback_history = []
|
||||||
|
self.methodology_log = []
|
||||||
|
|
||||||
|
def run(self):
|
||||||
|
"""执行完整回滚推演：从第200期开始逐期推演至最新一期"""
|
||||||
|
total_periods = len(self.all_data)
|
||||||
|
if total_periods < 200:
|
||||||
|
return {"error": f"数据不足200期，当前仅{total_periods}期", "available": total_periods}
|
||||||
|
|
||||||
|
start_idx = 199 # 第200期（0-based索引）
|
||||||
|
total_simulations = 0
|
||||||
|
total_red_hits = 0
|
||||||
|
total_blue_hits = 0
|
||||||
|
possible_red = 0
|
||||||
|
possible_blue = 0
|
||||||
|
methodology_snapshots = []
|
||||||
|
|
||||||
|
print(f"{'='*60}")
|
||||||
|
print(f" AI逐期回滚推演引擎 - {self.rule['name']}")
|
||||||
|
print(f" 数据总量: {total_periods}期 | 推演范围: 第200期 ~ 第{total_periods}期")
|
||||||
|
print(f" 推演总轮次: {total_periods - start_idx} 期")
|
||||||
|
print(f"{'='*60}")
|
||||||
|
|
||||||
|
for i in range(start_idx, total_periods):
|
||||||
|
current_issue = self.all_data[i]["issue"]
|
||||||
|
historical = self.all_data[:i]
|
||||||
|
|
||||||
|
actual = self.all_data[i]
|
||||||
|
actual_reds = [int(x) for x in actual["reds"]]
|
||||||
|
actual_blues = [int(x) for x in _get_blues(actual)]
|
||||||
|
|
||||||
|
analyzer = EnhancedAnalyzer(historical, self.lt, self.weights)
|
||||||
|
analysis = analyzer.analyze()
|
||||||
|
if not analysis:
|
||||||
|
continue
|
||||||
|
|
||||||
|
recommendations = analyzer.recommend(analysis, count=5)
|
||||||
|
if not recommendations:
|
||||||
|
continue
|
||||||
|
|
||||||
|
best_pick = recommendations[0]
|
||||||
|
pred_reds = best_pick["reds"]
|
||||||
|
pred_blues = best_pick["blues"]
|
||||||
|
|
||||||
|
red_hit = len(set(pred_reds) & set(actual_reds))
|
||||||
|
blue_hit = len(set(pred_blues) & set(actual_blues))
|
||||||
|
|
||||||
|
total_red_hits += red_hit
|
||||||
|
total_blue_hits += blue_hit
|
||||||
|
possible_red += self.rule["red_count"]
|
||||||
|
possible_blue += self.rule["blue_count"]
|
||||||
|
total_simulations += 1
|
||||||
|
|
||||||
|
self.rollback_history.append({
|
||||||
|
"issue": current_issue,
|
||||||
|
"iteration": total_simulations,
|
||||||
|
"pred_reds": pred_reds,
|
||||||
|
"pred_blues": pred_blues,
|
||||||
|
"actual_reds": actual_reds,
|
||||||
|
"actual_blues": actual_blues,
|
||||||
|
"red_hit": red_hit,
|
||||||
|
"blue_hit": blue_hit,
|
||||||
|
"score": best_pick["score"],
|
||||||
|
"weights_at_time": copy.deepcopy(self.weights),
|
||||||
|
})
|
||||||
|
|
||||||
|
self._optimize_weights(red_hit, blue_hit, pred_reds, actual_reds)
|
||||||
|
|
||||||
|
if total_simulations % 5 == 0:
|
||||||
|
red_acc = total_red_hits / possible_red * 100 if possible_red else 0
|
||||||
|
blue_acc = total_blue_hits / possible_blue * 100 if possible_blue else 0
|
||||||
|
methodology_snapshots.append({
|
||||||
|
"iteration": total_simulations,
|
||||||
|
"issue": current_issue,
|
||||||
|
"weights": copy.deepcopy(self.weights),
|
||||||
|
"red_accuracy": round(red_acc, 2),
|
||||||
|
"blue_accuracy": round(blue_acc, 2),
|
||||||
|
})
|
||||||
|
|
||||||
|
if total_simulations % 10 == 0 or total_simulations == 1:
|
||||||
|
red_acc = total_red_hits / possible_red * 100 if possible_red else 0
|
||||||
|
blue_acc = total_blue_hits / possible_blue * 100 if possible_blue else 0
|
||||||
|
overall_acc = (total_red_hits + total_blue_hits) / (possible_red + possible_blue) * 100
|
||||||
|
print(f" [推演 {total_simulations:3d}] 期{current_issue} "
|
||||||
|
f"红{red_hit}/{self.rule['red_count']} 蓝{blue_hit}/{self.rule['blue_count']} "
|
||||||
|
f"| 累计 红{red_acc:.1f}% 蓝{blue_acc:.1f}% 综合{overall_acc:.1f}%")
|
||||||
|
|
||||||
|
# 最终统计
|
||||||
|
avg_red = total_red_hits / possible_red * 100 if possible_red else 0
|
||||||
|
avg_blue = total_blue_hits / possible_blue * 100 if possible_blue else 0
|
||||||
|
overall_hits = total_red_hits + total_blue_hits
|
||||||
|
overall_possible = possible_red + possible_blue
|
||||||
|
overall_acc = overall_hits / overall_possible * 100 if overall_possible else 0
|
||||||
|
win_probability = overall_acc
|
||||||
|
|
||||||
|
hit_dist = {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0}
|
||||||
|
for entry in self.rollback_history:
|
||||||
|
total_hit = entry["red_hit"] + entry["blue_hit"]
|
||||||
|
if total_hit in hit_dist:
|
||||||
|
hit_dist[total_hit] += 1
|
||||||
|
|
||||||
|
result = {
|
||||||
|
"lottery": self.lt,
|
||||||
|
"total_simulations": total_simulations,
|
||||||
|
"start_issue": self.all_data[start_idx]["issue"],
|
||||||
|
"end_issue": self.all_data[-1]["issue"],
|
||||||
|
"final_weights": self.weights,
|
||||||
|
"accuracy": {
|
||||||
|
"red_accuracy": round(avg_red, 2),
|
||||||
|
"blue_accuracy": round(avg_blue, 2),
|
||||||
|
"overall_accuracy": round(overall_acc, 2),
|
||||||
|
"win_probability": round(win_probability, 2),
|
||||||
|
},
|
||||||
|
"hits": {
|
||||||
|
"total_red_hits": total_red_hits,
|
||||||
|
"total_blue_hits": total_blue_hits,
|
||||||
|
"possible_red": possible_red,
|
||||||
|
"possible_blue": possible_blue,
|
||||||
|
},
|
||||||
|
"hit_distribution": hit_dist,
|
||||||
|
"methodology_snapshots": methodology_snapshots,
|
||||||
|
"weight_evolution": self._get_weight_evolution(),
|
||||||
|
}
|
||||||
|
return result
|
||||||
|
|
||||||
|
def _optimize_weights(self, red_hit, blue_hit, pred_reds, actual_reds):
|
||||||
|
"""根据推演结果动态调整10维度权重"""
|
||||||
|
iteration = len(self.rollback_history) + 1
|
||||||
|
lr = max(0.003, 0.01 - iteration * 0.0001)
|
||||||
|
|
||||||
|
hit_ratio = (red_hit + blue_hit) / (self.rule["red_count"] + self.rule["blue_count"])
|
||||||
|
|
||||||
|
if hit_ratio >= 0.5:
|
||||||
|
self.weights["frequency"] = min(0.35, self.weights["frequency"] * (1 + lr))
|
||||||
|
self.weights["hot_number"] = min(0.30, self.weights["hot_number"] * (1 + lr * 0.8))
|
||||||
|
else:
|
||||||
|
self.weights["missing"] = min(0.30, self.weights["missing"] * (1 + lr))
|
||||||
|
self.weights["probability"] = min(0.15, self.weights["probability"] * (1 + lr * 1.2))
|
||||||
|
self.weights["frequency"] = max(0.15, self.weights["frequency"] * (1 - lr * 0.5))
|
||||||
|
|
||||||
|
pred_oe = sum(1 for n in pred_reds if n % 2 == 1)
|
||||||
|
actual_oe = sum(1 for n in actual_reds if n % 2 == 1)
|
||||||
|
if pred_oe == actual_oe:
|
||||||
|
self.weights["odd_even"] = min(0.12, self.weights["odd_even"] * (1 + lr))
|
||||||
|
|
||||||
|
pred_sum = sum(pred_reds)
|
||||||
|
actual_sum = sum(actual_reds)
|
||||||
|
if abs(pred_sum - actual_sum) <= 15:
|
||||||
|
self.weights["sum_value"] = min(0.10, self.weights["sum_value"] * (1 + lr))
|
||||||
|
|
||||||
|
pred_consec = sum(1 for i in range(len(pred_reds) - 1) if pred_reds[i + 1] - pred_reds[i] == 1)
|
||||||
|
actual_consec = sum(1 for i in range(len(actual_reds) - 1) if actual_reds[i + 1] - actual_reds[i] == 1)
|
||||||
|
if pred_consec == actual_consec:
|
||||||
|
self.weights["consecutive"] = min(0.08, self.weights["consecutive"] * (1 + lr))
|
||||||
|
|
||||||
|
total = sum(self.weights.values())
|
||||||
|
if total > 0:
|
||||||
|
for k in self.weights:
|
||||||
|
self.weights[k] /= total
|
||||||
|
|
||||||
|
def _get_weight_evolution(self):
|
||||||
|
if not self.rollback_history:
|
||||||
|
return []
|
||||||
|
interval = max(1, len(self.rollback_history) // 20)
|
||||||
|
evolution = []
|
||||||
|
for i, entry in enumerate(self.rollback_history):
|
||||||
|
if i % interval == 0 or i == len(self.rollback_history) - 1:
|
||||||
|
evolution.append({
|
||||||
|
"iteration": entry["iteration"],
|
||||||
|
"issue": entry["issue"],
|
||||||
|
"weights": entry["weights_at_time"],
|
||||||
|
})
|
||||||
|
return evolution
|
||||||
|
|
||||||
|
|
||||||
|
# ============================================================
|
||||||
|
# 自进化系统（多轮迭代 + 方法论总结升级）
|
||||||
|
# ============================================================
|
||||||
|
|
||||||
|
class SelfEvolvingSystem:
|
||||||
|
"""多轮迭代运行，每轮完成后总结方法论并升级，目标胜出率 >= 51%"""
|
||||||
|
|
||||||
|
def __init__(self, lt, data):
|
||||||
|
self.lt = lt
|
||||||
|
self.data = data
|
||||||
|
self.rule = LOTTERY_RULES[lt]
|
||||||
|
self.best_weights = copy.deepcopy(DEFAULT_WEIGHTS)
|
||||||
|
self.best_accuracy = 0
|
||||||
|
self.iteration_results = []
|
||||||
|
self.methodology_log = []
|
||||||
|
|
||||||
|
def run_iterations(self, max_iterations=20):
|
||||||
|
print(f"\n{'='*60}")
|
||||||
|
print(f" 🔬 自进化系统启动 - {self.rule['name']}")
|
||||||
|
print(f" 目标：胜出率(概率占比) >= 51%")
|
||||||
|
print(f" 最大迭代轮次: {max_iterations}")
|
||||||
|
print(f"{'='*60}")
|
||||||
|
|
||||||
|
for iteration in range(1, max_iterations + 1):
|
||||||
|
print(f"\n{'─'*60}")
|
||||||
|
print(f" 第 {iteration}/{max_iterations} 轮迭代")
|
||||||
|
print(f"{'─'*60}")
|
||||||
|
|
||||||
|
if iteration == 1:
|
||||||
|
current_weights = copy.deepcopy(DEFAULT_WEIGHTS)
|
||||||
|
else:
|
||||||
|
current_weights = self._mutate_weights(self.best_weights, iteration)
|
||||||
|
|
||||||
|
engine = AIRollbackEngine(self.lt, self.data, current_weights)
|
||||||
|
result = engine.run()
|
||||||
|
|
||||||
|
if "error" in result:
|
||||||
|
print(f" ❌ 错误: {result['error']}")
|
||||||
|
break
|
||||||
|
|
||||||
|
accuracy = result["accuracy"]["win_probability"]
|
||||||
|
self.iteration_results.append({
|
||||||
|
"iteration": iteration,
|
||||||
|
"accuracy": accuracy,
|
||||||
|
"weights": result["final_weights"],
|
||||||
|
"simulations": result["total_simulations"],
|
||||||
|
})
|
||||||
|
|
||||||
|
self._summarize_methodology(iteration, result)
|
||||||
|
|
||||||
|
if accuracy > self.best_accuracy:
|
||||||
|
self.best_accuracy = accuracy
|
||||||
|
self.best_weights = result["final_weights"]
|
||||||
|
print(f" 🏆 新最佳胜出率: {accuracy:.2f}%")
|
||||||
|
else:
|
||||||
|
print(f" 本轮胜出率: {accuracy:.2f}% (最佳: {self.best_accuracy:.2f}%)")
|
||||||
|
|
||||||
|
if self.best_accuracy >= 51.0:
|
||||||
|
print(f"\n{'='*60}")
|
||||||
|
print(f" 🎯 目标达成！胜出率 {self.best_accuracy:.2f}% >= 51%")
|
||||||
|
print(f"{'='*60}")
|
||||||
|
break
|
||||||
|
|
||||||
|
return self._final_report()
|
||||||
|
|
||||||
|
def _mutate_weights(self, base_weights, iteration):
|
||||||
|
mutated = copy.deepcopy(base_weights)
|
||||||
|
mutation_rate = max(0.005, 0.03 - iteration * 0.001)
|
||||||
|
for k in mutated:
|
||||||
|
delta = random.uniform(-mutation_rate, mutation_rate)
|
||||||
|
mutated[k] = max(0.01, mutated[k] + delta)
|
||||||
|
total = sum(mutated.values())
|
||||||
|
for k in mutated:
|
||||||
|
mutated[k] /= total
|
||||||
|
return mutated
|
||||||
|
|
||||||
|
def _summarize_methodology(self, iteration, result):
|
||||||
|
acc = result["accuracy"]
|
||||||
|
weights = result["final_weights"]
|
||||||
|
dist = result.get("hit_distribution", {})
|
||||||
|
|
||||||
|
sorted_w = sorted(weights.items(), key=lambda x: x[1], reverse=True)
|
||||||
|
top_dims = sorted_w[:3]
|
||||||
|
weak_dims = sorted_w[-2:]
|
||||||
|
|
||||||
|
summary_lines = [
|
||||||
|
f"\n === 方法论总结 - 第{iteration}轮 ===",
|
||||||
|
f" 胜出率: {acc['win_probability']:.2f}% (红球{acc['red_accuracy']:.1f}% 蓝球{acc['blue_accuracy']:.1f}%)",
|
||||||
|
f" 最优三维度:",
|
||||||
|
]
|
||||||
|
for dim, w in top_dims:
|
||||||
|
summary_lines.append(f" - {dim}: {w:.4f}")
|
||||||
|
summary_lines.append(f" 最弱二维度:")
|
||||||
|
for dim, w in weak_dims:
|
||||||
|
summary_lines.append(f" - {dim}: {w:.4f}")
|
||||||
|
summary_lines.append(f" 命中分布:")
|
||||||
|
for level in sorted(dist.keys()):
|
||||||
|
if dist.get(level, 0) > 0:
|
||||||
|
pct = dist[level] / result["total_simulations"] * 100
|
||||||
|
summary_lines.append(f" - {level}个命中: {dist[level]}次 ({pct:.1f}%)")
|
||||||
|
|
||||||
|
summary = "\n".join(summary_lines)
|
||||||
|
print(summary)
|
||||||
|
self.methodology_log.append(summary)
|
||||||
|
|
||||||
|
mpath = os.path.join(DATA_DIR, f"methodology_{self.lt}_iter{iteration}.json")
|
||||||
|
with open(mpath, "w", encoding="utf-8") as f:
|
||||||
|
json.dump({
|
||||||
|
"iteration": iteration,
|
||||||
|
"accuracy": acc,
|
||||||
|
"weights": weights,
|
||||||
|
"hit_distribution": dist,
|
||||||
|
"snapshots": result.get("methodology_snapshots", []),
|
||||||
|
}, f, ensure_ascii=False, indent=2)
|
||||||
|
print(f" [方法论已保存] {mpath}")
|
||||||
|
|
||||||
|
def _final_report(self):
|
||||||
|
report = {
|
||||||
|
"lottery": self.lt,
|
||||||
|
"total_iterations": len(self.iteration_results),
|
||||||
|
"best_accuracy": self.best_accuracy,
|
||||||
|
"best_weights": self.best_weights,
|
||||||
|
"iteration_summary": self.iteration_results,
|
||||||
|
}
|
||||||
|
rpath = os.path.join(DATA_DIR, f"self_evolve_{self.lt}_final.json")
|
||||||
|
with open(rpath, "w", encoding="utf-8") as f:
|
||||||
|
json.dump(report, f, ensure_ascii=False, indent=2)
|
||||||
|
return report
|
||||||
|
|
||||||
|
|
||||||
|
# ============================================================
|
||||||
|
# CLI 主入口
|
||||||
|
# ============================================================
|
||||||
|
|
||||||
|
def load_history(lt):
|
||||||
|
path = os.path.join(DATA_DIR, f"{lt}_history.json")
|
||||||
|
if os.path.exists(path):
|
||||||
|
try:
|
||||||
|
with open(path, "r", encoding="utf-8") as f:
|
||||||
|
return json.load(f)
|
||||||
|
except:
|
||||||
|
pass
|
||||||
|
return []
|
||||||
|
|
||||||
|
|
||||||
|
def print_header():
|
||||||
|
print("\n" + "*" * 60)
|
||||||
|
print(" 双色球 & 大乐透 * 增强智能分析系统 v2.0")
|
||||||
|
print(" AI回滚推演 | 方法论自优化 | 200期深度分析")
|
||||||
|
print(" " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M"))
|
||||||
|
print("*" * 60)
|
||||||
|
|
||||||
|
|
||||||
|
def show_full_report(lt, count=5):
|
||||||
|
rule = LOTTERY_RULES[lt]
|
||||||
|
data = load_history(lt)
|
||||||
|
if not data:
|
||||||
|
print(f"\n ⚠️ {rule['name']}暂无数据，请先采集")
|
||||||
|
print(f" python3 lottery_tool.py --action fetch --lottery {lt}")
|
||||||
|
return
|
||||||
|
|
||||||
|
analyzer = EnhancedAnalyzer(data, lt)
|
||||||
|
analysis = analyzer.analyze()
|
||||||
|
if not analysis:
|
||||||
|
print(f"\n ⚠️ {rule['name']}分析失败")
|
||||||
|
return
|
||||||
|
|
||||||
|
print(f"\n{'─'*60}")
|
||||||
|
print(f" {rule['name']} - 增强分析报告 (200期)")
|
||||||
|
print(f"{'─'*60}")
|
||||||
|
dr = analysis["data_range"]
|
||||||
|
print(f" 数据范围: {dr['total']}期 ({dr['start']} ~ {dr['end']})")
|
||||||
|
w = analyzer.weights
|
||||||
|
print(f" 权重体系: freq={w['frequency']:.3f} hot={w['hot_number']:.3f} miss={w['missing']:.3f}")
|
||||||
|
|
||||||
|
pat = analysis["patterns"]
|
||||||
|
print(f"\n 模式分析:")
|
||||||
|
print(f" 奇偶比: {pat['most_oe']} | 大小比: {pat['most_bs']}")
|
||||||
|
print(f" 和值范围: {pat['sum_range'][0]} ~ {pat['sum_range'][1]} (均{pat['avg_sum']})")
|
||||||
|
print(f" 区间分布: {pat['zone_balance']}")
|
||||||
|
|
||||||
|
recs = analyzer.recommend(analysis, count=count)
|
||||||
|
print(f"\n 🎯 AI推荐号码 ({count}组):")
|
||||||
|
for i, rec in enumerate(recs, 1):
|
||||||
|
rs = " ".join(f"{n:02d}" for n in rec["reds"])
|
||||||
|
bs = " ".join(f"{n:02d}" for n in rec["blues"])
|
||||||
|
print(f" [{i}] 红球: {rs} 蓝球: {bs} (评分:{rec['score']:.3f} OE:{rec['oe']} BS:{rec['bs']})")
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
import argparse
|
||||||
|
parser = argparse.ArgumentParser(description="增强版彩票智能分析系统 v2.0")
|
||||||
|
parser.add_argument("--action",
|
||||||
|
choices=["report", "rollback", "self_evolve"],
|
||||||
|
default="report",
|
||||||
|
help="report: 200期分析推荐 | rollback: 逐期回滚推演 | self_evolve: 自进化")
|
||||||
|
parser.add_argument("--lottery", choices=["ssq", "dlt", "all"], default="ssq")
|
||||||
|
parser.add_argument("--count", type=int, default=5, help="推荐组数")
|
||||||
|
parser.add_argument("--iterations", type=int, default=10, help="自进化迭代轮次")
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
print_header()
|
||||||
|
lotteries = ["ssq", "dlt"] if args.lottery == "all" else [args.lottery]
|
||||||
|
|
||||||
|
if args.action == "report":
|
||||||
|
for lt in lotteries:
|
||||||
|
show_full_report(lt, args.count)
|
||||||
|
return
|
||||||
|
|
||||||
|
if args.action == "rollback":
|
||||||
|
for lt in lotteries:
|
||||||
|
data = load_history(lt)
|
||||||
|
if len(data) < 200:
|
||||||
|
print(f"\n ⚠️ {LOTTERY_RULES[lt]['name']}数据不足200期 (当前{len(data)}期)")
|
||||||
|
print(f" 请先采集: python3 lottery_tool.py --action fetch --lottery {lt}")
|
||||||
|
continue
|
||||||
|
engine = AIRollbackEngine(lt, data)
|
||||||
|
result = engine.run()
|
||||||
|
if "error" not in result:
|
||||||
|
acc = result["accuracy"]
|
||||||
|
print(f"\n === 回滚推演完成 ===")
|
||||||
|
print(f" 综合胜出率: {acc['win_probability']:.2f}%")
|
||||||
|
print(f" 红球准确率: {acc['red_accuracy']:.1f}% | 蓝球: {acc['blue_accuracy']:.1f}%")
|
||||||
|
|
||||||
|
if args.action == "self_evolve":
|
||||||
|
for lt in lotteries:
|
||||||
|
data = load_history(lt)
|
||||||
|
if len(data) < 200:
|
||||||
|
print(f"\n ⚠️ {LOTTERY_RULES[lt]['name']}数据不足200期 (当前{len(data)}期)")
|
||||||
|
continue
|
||||||
|
system = SelfEvolvingSystem(lt, data)
|
||||||
|
final = system.run_iterations(max_iterations=args.iterations)
|
||||||
|
print(f"\n === 自进化最终结果 ===")
|
||||||
|
print(f" 最佳胜出率: {final['best_accuracy']:.2f}%")
|
||||||
|
print(f" 迭代总轮次: {final['total_iterations']}")
|
||||||
|
print(f" 最优权重:")
|
||||||
|
for k, v in sorted(final['best_weights'].items(), key=lambda x: x[1], reverse=True):
|
||||||
|
print(f" {k}: {v:.4f}")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
@@ -0,0 +1,725 @@
|
|||||||
|
#!/usr/bin/env python
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
大乐透/双色球 集成学习模块
|
||||||
|
方案3：多模型集成 + 权重优化训练
|
||||||
|
==================================
|
||||||
|
包含5种分析模型：
|
||||||
|
1. 频率加权模型 (Frequency Model)
|
||||||
|
2. 贝叶斯平滑模型 (Bayesian Smoothing Model)
|
||||||
|
3. 马尔可夫链模型 (Markov Chain Model)
|
||||||
|
4. K-Means聚类模型 (K-Means Clustering Model)
|
||||||
|
5. 残差回归模型 (Residual Regression Model)
|
||||||
|
|
||||||
|
通过历史数据回测学习各模型的最优权重
|
||||||
|
"""
|
||||||
|
|
||||||
|
import json, math, random, hashlib
|
||||||
|
from collections import Counter, defaultdict
|
||||||
|
|
||||||
|
# ==================== 基础工具函数 ====================
|
||||||
|
|
||||||
|
def sigmoid(x):
|
||||||
|
"""Sigmoid激活函数"""
|
||||||
|
return 1 / (1 + math.exp(-max(min(x, 500), -500)))
|
||||||
|
|
||||||
|
def normalize(scores):
|
||||||
|
"""归一化分数，使总和为1"""
|
||||||
|
total = sum(scores.values())
|
||||||
|
if total == 0:
|
||||||
|
return {k: 1.0/len(scores) for k in scores}
|
||||||
|
return {k: v/total for k, v in scores.items()}
|
||||||
|
|
||||||
|
def weighted_choice(weights, count=1):
|
||||||
|
"""从权重分布中无放回选择指定数量的元素"""
|
||||||
|
items = list(weights.keys())
|
||||||
|
if len(items) == 0:
|
||||||
|
return []
|
||||||
|
if count >= len(items):
|
||||||
|
return items[:]
|
||||||
|
result = []
|
||||||
|
remaining = items[:]
|
||||||
|
remaining_weights = weights.copy()
|
||||||
|
for _ in range(count):
|
||||||
|
total = sum(remaining_weights.values())
|
||||||
|
if total <= 0:
|
||||||
|
choice = random.choice(remaining)
|
||||||
|
else:
|
||||||
|
r = random.random() * total
|
||||||
|
cum = 0
|
||||||
|
for item in remaining:
|
||||||
|
cum += remaining_weights[item]
|
||||||
|
if r <= cum:
|
||||||
|
choice = item
|
||||||
|
break
|
||||||
|
result.append(choice)
|
||||||
|
remaining.remove(choice)
|
||||||
|
del remaining_weights[choice]
|
||||||
|
return result
|
||||||
|
|
||||||
|
# ==================== 模型1：频率加权模型 ====================
|
||||||
|
|
||||||
|
class FrequencyModel:
|
||||||
|
"""基于历史频率的加权模型"""
|
||||||
|
def __init__(self, lt, data, rule):
|
||||||
|
self.lt = lt
|
||||||
|
self.data = data
|
||||||
|
self.rule = rule
|
||||||
|
self.rcnt = rule["red_count"]
|
||||||
|
self.rmax = rule["red_max"]
|
||||||
|
self.bcnt = rule["blue_count"]
|
||||||
|
self.bmax = rule["blue_max"]
|
||||||
|
self.red_score = {}
|
||||||
|
self.blue_score = {}
|
||||||
|
self.analyze()
|
||||||
|
|
||||||
|
def analyze(self):
|
||||||
|
rule, rmax, bmax, rcnt, bcnt = self.rule, self.rule["red_max"], self.rule["blue_max"], self.rule["red_count"], self.rule["blue_count"]
|
||||||
|
all_r, all_b = [], []
|
||||||
|
for rec in self.data:
|
||||||
|
all_r.extend(int(x) for x in rec["reds"])
|
||||||
|
bl = rec["blue"]
|
||||||
|
# 修复：蓝球处理 - 支持整数和字符串两种格式
|
||||||
|
if isinstance(bl, str):
|
||||||
|
all_b.append(int(bl))
|
||||||
|
elif isinstance(bl, list):
|
||||||
|
all_b.extend(bl)
|
||||||
|
else:
|
||||||
|
all_b.append(bl)
|
||||||
|
|
||||||
|
# 频率统计
|
||||||
|
rf = Counter(all_r)
|
||||||
|
bf = Counter(all_b)
|
||||||
|
|
||||||
|
max_rf = max(rf.values()) if rf else 1
|
||||||
|
max_bf = max(bf.values()) if bf else 1
|
||||||
|
|
||||||
|
# 归一化
|
||||||
|
self.red_score = {i: rf.get(i, 0) / max_rf for i in range(1, rmax + 1)}
|
||||||
|
self.blue_score = {i: bf.get(i, 0) / max_bf for i in range(1, bmax + 1)}
|
||||||
|
|
||||||
|
# 平滑：避免0分
|
||||||
|
for i in range(1, rmax + 1):
|
||||||
|
if self.red_score[i] == 0:
|
||||||
|
self.red_score[i] = 0.01
|
||||||
|
for i in range(1, bmax + 1):
|
||||||
|
if self.blue_score[i] == 0:
|
||||||
|
self.blue_score[i] = 0.01
|
||||||
|
|
||||||
|
# 归一化到[0,1]
|
||||||
|
max_r = max(self.red_score.values())
|
||||||
|
max_b = max(self.blue_score.values())
|
||||||
|
if max_r > 0:
|
||||||
|
for i in self.red_score:
|
||||||
|
self.red_score[i] /= max_r
|
||||||
|
if max_b > 0:
|
||||||
|
for i in self.blue_score:
|
||||||
|
self.blue_score[i] /= max_b
|
||||||
|
|
||||||
|
|
||||||
|
# ==================== 模型2：贝叶斯平滑模型 ====================
|
||||||
|
|
||||||
|
class BayesianModel:
|
||||||
|
"""贝叶斯先验 + 似然平滑模型"""
|
||||||
|
def __init__(self, lt, data, rule):
|
||||||
|
self.lt = lt
|
||||||
|
self.data = data
|
||||||
|
self.rule = rule
|
||||||
|
self.rcnt = rule["red_count"]
|
||||||
|
self.rmax = rule["red_max"]
|
||||||
|
self.bcnt = rule["blue_count"]
|
||||||
|
self.bmax = rule["blue_max"]
|
||||||
|
self.prior = {i: 1.0 / rule["red_max"] for i in range(1, rule["red_max"] + 1)}
|
||||||
|
self.red_score = {}
|
||||||
|
self.blue_score = {}
|
||||||
|
self.analyze()
|
||||||
|
|
||||||
|
def analyze(self):
|
||||||
|
rule, rmax, bmax, rcnt, bcnt = self.rule, self.rule["red_max"], self.rule["blue_max"], self.rule["red_count"], self.rule["blue_count"]
|
||||||
|
all_r, all_b = [], []
|
||||||
|
for rec in self.data:
|
||||||
|
all_r.extend(int(x) for x in rec["reds"])
|
||||||
|
bl = rec["blue"]
|
||||||
|
# 修复：蓝球处理 - 支持整数和字符串两种格式
|
||||||
|
if isinstance(bl, str):
|
||||||
|
all_b.append(int(bl))
|
||||||
|
elif isinstance(bl, list):
|
||||||
|
all_b.extend(bl)
|
||||||
|
else:
|
||||||
|
all_b.append(bl)
|
||||||
|
|
||||||
|
# 似然
|
||||||
|
rf = Counter(all_r)
|
||||||
|
bf = Counter(all_b)
|
||||||
|
|
||||||
|
# 贝叶斯后验：后验 ∝ 先验 × 似然
|
||||||
|
# 使用伪计数：先验+1，似然+1，避免0
|
||||||
|
alpha = 1 # 先验伪计数
|
||||||
|
beta = 1 # 似然伪计数
|
||||||
|
total_alpha = rmax * alpha
|
||||||
|
total_beta = len(all_r) + beta * rmax
|
||||||
|
|
||||||
|
self.red_score = {}
|
||||||
|
for i in range(1, rmax + 1):
|
||||||
|
# 后验均值 = (α + count) / (α + β * rmax)
|
||||||
|
posterior = (alpha + rf.get(i, 0)) / (total_alpha + beta)
|
||||||
|
self.red_score[i] = posterior
|
||||||
|
|
||||||
|
total_blue = len(all_b) + beta * bmax
|
||||||
|
self.blue_score = {}
|
||||||
|
for i in range(1, bmax + 1):
|
||||||
|
posterior = (alpha + bf.get(i, 0)) / (total_alpha + beta)
|
||||||
|
self.blue_score[i] = posterior
|
||||||
|
|
||||||
|
# 归一化
|
||||||
|
max_r = max(self.red_score.values())
|
||||||
|
max_b = max(self.blue_score.values())
|
||||||
|
if max_r > 0:
|
||||||
|
for i in self.red_score:
|
||||||
|
self.red_score[i] /= max_r
|
||||||
|
if max_b > 0:
|
||||||
|
for i in self.blue_score:
|
||||||
|
self.blue_score[i] /= max_b
|
||||||
|
|
||||||
|
|
||||||
|
# ==================== 模型3：马尔可夫链模型 ====================
|
||||||
|
|
||||||
|
class MarkovModel:
|
||||||
|
"""基于马尔可夫链的状态转移模型"""
|
||||||
|
def __init__(self, lt, data, rule):
|
||||||
|
self.lt = lt
|
||||||
|
self.data = data
|
||||||
|
self.rule = rule
|
||||||
|
self.rcnt = rule["red_count"]
|
||||||
|
self.rmax = rule["red_max"]
|
||||||
|
self.bcnt = rule["blue_count"]
|
||||||
|
self.bmax = rule["blue_max"]
|
||||||
|
self.red_score = {}
|
||||||
|
self.blue_score = {}
|
||||||
|
self.analyze()
|
||||||
|
|
||||||
|
def analyze(self):
|
||||||
|
rule, rmax, bmax, rcnt, bcnt = self.rule, self.rule["red_max"], self.rule["blue_max"], self.rule["red_count"], self.rule["blue_count"]
|
||||||
|
|
||||||
|
# 构建状态转移矩阵（考虑最近5期作为状态）
|
||||||
|
# 这里简化为：计算每个号码在历史数据中的"惯性"
|
||||||
|
# 即：一个号码出现后，后续几期再次出现的概率
|
||||||
|
|
||||||
|
# 使用frozenset作为不可变集合作为字典键
|
||||||
|
transition = defaultdict(lambda: defaultdict(int))
|
||||||
|
for i in range(1, len(self.data)):
|
||||||
|
prev = frozenset(int(x) for x in self.data[i-1]["reds"])
|
||||||
|
curr = set(int(x) for x in self.data[i]["reds"])
|
||||||
|
for num in curr:
|
||||||
|
transition[prev][num] += 1
|
||||||
|
|
||||||
|
# 计算每个号码的"热度"（结合近期出现频率和转移概率）
|
||||||
|
recent_data = self.data[:20] if len(self.data) >= 20 else self.data
|
||||||
|
recent_reds = []
|
||||||
|
for rec in recent_data:
|
||||||
|
recent_reds.extend(int(x) for x in rec["reds"])
|
||||||
|
|
||||||
|
rf = Counter(recent_reds)
|
||||||
|
total_recent = len(recent_reds)
|
||||||
|
|
||||||
|
# 综合评分：近期频率 + 转移惯性
|
||||||
|
self.red_score = {}
|
||||||
|
for i in range(1, rmax + 1):
|
||||||
|
freq_score = rf.get(i, 0) / total_recent if total_recent > 0 else 0
|
||||||
|
# 惯性得分：如果上一期出现过，给予额外权重
|
||||||
|
last_reds = set(int(x) for x in self.data[0]["reds"]) if self.data else set()
|
||||||
|
inertia = 1.5 if i in last_reds else 1.0
|
||||||
|
self.red_score[i] = (freq_score * inertia + 0.01) # 加0.01平滑
|
||||||
|
|
||||||
|
# 蓝球简化：直接用近期频率
|
||||||
|
recent_blues = []
|
||||||
|
for rec in recent_data:
|
||||||
|
bl = rec["blue"]
|
||||||
|
recent_blues.append(int(bl) if isinstance(bl, str) else (bl[0] if isinstance(bl, list) else bl))
|
||||||
|
|
||||||
|
bf = Counter(recent_blues)
|
||||||
|
total_blue = len(recent_blues)
|
||||||
|
self.blue_score = {}
|
||||||
|
for i in range(1, bmax + 1):
|
||||||
|
self.blue_score[i] = (bf.get(i, 0) / total_blue * 0.8 + 1.0 / bmax * 0.2) if total_blue > 0 else (1.0 / bmax)
|
||||||
|
|
||||||
|
# 归一化
|
||||||
|
max_r = max(self.red_score.values()) if self.red_score else 1
|
||||||
|
max_b = max(self.blue_score.values()) if self.blue_score else 1
|
||||||
|
for i in self.red_score:
|
||||||
|
self.red_score[i] /= max_r
|
||||||
|
for i in self.blue_score:
|
||||||
|
self.blue_score[i] /= max_b
|
||||||
|
|
||||||
|
|
||||||
|
# ==================== 模型4：K-Means聚类模型 ====================
|
||||||
|
|
||||||
|
class KMeansModel:
|
||||||
|
"""基于历史聚类中心的推荐模型"""
|
||||||
|
def __init__(self, lt, data, rule):
|
||||||
|
self.lt = lt
|
||||||
|
self.data = data
|
||||||
|
self.rule = rule
|
||||||
|
self.rcnt = rule["red_count"]
|
||||||
|
self.rmax = rule["red_max"]
|
||||||
|
self.bcnt = rule["blue_count"]
|
||||||
|
self.bmax = rule["blue_max"]
|
||||||
|
self.red_score = {}
|
||||||
|
self.blue_score = {}
|
||||||
|
self.analyze()
|
||||||
|
|
||||||
|
def analyze(self):
|
||||||
|
rule, rmax, bmax, rcnt, bcnt = self.rule, self.rule["red_max"], self.rule["blue_max"], self.rule["red_count"], self.rule["blue_max"]
|
||||||
|
|
||||||
|
# 提取所有历史红球组合
|
||||||
|
clusters = []
|
||||||
|
for rec in self.data:
|
||||||
|
nums = sorted(int(x) for x in rec["reds"])
|
||||||
|
if len(nums) == rcnt:
|
||||||
|
clusters.append(nums)
|
||||||
|
|
||||||
|
if len(clusters) < 3:
|
||||||
|
# 数据不足，退化为频率模型
|
||||||
|
rf = Counter()
|
||||||
|
for nums in clusters:
|
||||||
|
for n in nums:
|
||||||
|
rf[n] += 1
|
||||||
|
max_f = max(rf.values()) if rf else 1
|
||||||
|
self.red_score = {i: rf.get(i, 0) / max_f for i in range(1, rmax + 1)}
|
||||||
|
return
|
||||||
|
|
||||||
|
# 简化版K-Means：使用质心法
|
||||||
|
# 计算每个位置的平均值（排序后）
|
||||||
|
sorted_clusters = [sorted(c) for c in clusters]
|
||||||
|
n_clusters = min(5, len(sorted_clusters))
|
||||||
|
|
||||||
|
# 计算每个位置的聚类中心
|
||||||
|
position_means = [[] for _ in range(rcnt)]
|
||||||
|
for cluster in sorted_clusters:
|
||||||
|
for pos in range(rcnt):
|
||||||
|
position_means[pos].append(cluster[pos])
|
||||||
|
|
||||||
|
# 计算每个号码的"接近中心"得分
|
||||||
|
position_avg = [sum(p) / len(p) for p in position_means if p]
|
||||||
|
|
||||||
|
self.red_score = {}
|
||||||
|
for i in range(1, rmax + 1):
|
||||||
|
# 计算该号码在所有位置上偏离平均值的程度
|
||||||
|
deviations = []
|
||||||
|
for pos, avg in enumerate(position_avg):
|
||||||
|
if len(sorted_clusters) > pos:
|
||||||
|
cluster_at_pos = [c[pos] for c in sorted_clusters if len(c) > pos]
|
||||||
|
if cluster_at_pos:
|
||||||
|
deviations.append(abs(i - avg))
|
||||||
|
avg_dev = sum(deviations) / len(deviations) if deviations else rmax / 2
|
||||||
|
# 得分：偏离越小得分越高（越接近典型组合）
|
||||||
|
score = max(0, 1 - avg_dev / (rmax * 0.5))
|
||||||
|
self.red_score[i] = max(score, 0.01) # 最小0.01
|
||||||
|
|
||||||
|
# 蓝球：直接用频率
|
||||||
|
all_blues = []
|
||||||
|
for rec in self.data:
|
||||||
|
bl = rec["blue"]
|
||||||
|
all_blues.append(int(bl) if isinstance(bl, str) else (bl[0] if isinstance(bl, list) else bl))
|
||||||
|
bf = Counter(all_blues)
|
||||||
|
max_bf = max(bf.values()) if bf else 1
|
||||||
|
self.blue_score = {i: bf.get(i, 0) / max_bf for i in range(1, bmax + 1)}
|
||||||
|
for i in range(1, bmax + 1):
|
||||||
|
if self.blue_score[i] == 0:
|
||||||
|
self.blue_score[i] = 0.01
|
||||||
|
|
||||||
|
# 归一化
|
||||||
|
max_r = max(self.red_score.values()) if self.red_score else 1
|
||||||
|
max_b = max(self.blue_score.values()) if self.blue_score else 1
|
||||||
|
for i in self.red_score:
|
||||||
|
self.red_score[i] /= max_r
|
||||||
|
for i in self.blue_score:
|
||||||
|
self.blue_score[i] /= max_b
|
||||||
|
|
||||||
|
|
||||||
|
# ==================== 模型5：残差回归模型 ====================
|
||||||
|
|
||||||
|
class ResidualModel:
|
||||||
|
"""残差分析 + 回归预测模型"""
|
||||||
|
def __init__(self, lt, data, rule):
|
||||||
|
self.lt = lt
|
||||||
|
self.data = data
|
||||||
|
self.rule = rule
|
||||||
|
self.rcnt = rule["red_count"]
|
||||||
|
self.rmax = rule["red_max"]
|
||||||
|
self.bcnt = rule["blue_count"]
|
||||||
|
self.bmax = rule["blue_max"]
|
||||||
|
self.red_score = {}
|
||||||
|
self.blue_score = {}
|
||||||
|
self.analyze()
|
||||||
|
|
||||||
|
def analyze(self):
|
||||||
|
rule, rmax, bmax, rcnt, bcnt = self.rule, self.rule["red_max"], self.rule["blue_max"], self.rule["red_count"], self.rule["blue_count"]
|
||||||
|
|
||||||
|
# 计算每期的和值、奇偶比、大小比作为特征
|
||||||
|
sums = []
|
||||||
|
for rec in self.data:
|
||||||
|
s = sum(int(x) for x in rec["reds"])
|
||||||
|
sums.append(s)
|
||||||
|
|
||||||
|
if len(sums) < 10:
|
||||||
|
# 数据不足，退化为均匀分布
|
||||||
|
self.red_score = {i: 1.0 / rmax for i in range(1, rmax + 1)}
|
||||||
|
self.blue_score = {i: 1.0 / bmax for i in range(1, bmax + 1)}
|
||||||
|
return
|
||||||
|
|
||||||
|
# 计算和值的移动平均（最近10期）
|
||||||
|
window = min(10, len(sums))
|
||||||
|
recent_sums = sums[-window:]
|
||||||
|
avg_sum = sum(recent_sums) / len(recent_sums)
|
||||||
|
std_sum = math.sqrt(sum((s - avg_sum) ** 2 for s in recent_sums) / len(recent_sums))
|
||||||
|
|
||||||
|
# 计算每个号码在"和值附近"组合中的出现频率
|
||||||
|
# 思路：哪些号码更常出现在和值接近均值的组合中
|
||||||
|
score_by_num = {i: 0.0 for i in range(1, rmax + 1)}
|
||||||
|
count_by_num = {i: 0 for i in range(1, rmax + 1)}
|
||||||
|
|
||||||
|
for rec in self.data:
|
||||||
|
nums = [int(x) for x in rec["reds"]]
|
||||||
|
s = sum(nums)
|
||||||
|
# 计算该组合和值与均值的距离
|
||||||
|
dist = abs(s - avg_sum)
|
||||||
|
if dist <= std_sum * 2: # 落在2个标准差内
|
||||||
|
for n in nums:
|
||||||
|
score_by_num[n] += 1 / (dist + 1) # 距离越近得分越高
|
||||||
|
count_by_num[n] += 1
|
||||||
|
|
||||||
|
# 归一化
|
||||||
|
max_score = max(score_by_num.values()) if max(score_by_num.values()) > 0 else 1
|
||||||
|
self.red_score = {}
|
||||||
|
for i in range(1, rmax + 1):
|
||||||
|
if count_by_num[i] > 0:
|
||||||
|
self.red_score[i] = (score_by_num[i] / count_by_num[i]) / max_score
|
||||||
|
else:
|
||||||
|
self.red_score[i] = 1.0 / rmax # 均匀分布
|
||||||
|
|
||||||
|
# 蓝球：和值相关性分析
|
||||||
|
blues_by_sum = defaultdict(list)
|
||||||
|
for rec in self.data:
|
||||||
|
bl = rec["blue"]
|
||||||
|
bl_val = int(bl) if isinstance(bl, str) else (bl[0] if isinstance(bl, list) else bl)
|
||||||
|
s = sum(int(x) for x in rec["reds"])
|
||||||
|
blues_by_sum[s].append(bl_val)
|
||||||
|
|
||||||
|
self.blue_score = {}
|
||||||
|
for i in range(1, bmax + 1):
|
||||||
|
# 计算该蓝球在"和值附近"出现的频率
|
||||||
|
total = 0
|
||||||
|
for s, blues in blues_by_sum.items():
|
||||||
|
dist = abs(s - avg_sum)
|
||||||
|
if dist <= std_sum * 2:
|
||||||
|
total += len([b for b in blues if b == i]) / (dist + 1)
|
||||||
|
self.blue_score[i] = max(total / 100, 1.0 / bmax)
|
||||||
|
|
||||||
|
# 归一化
|
||||||
|
max_r = max(self.red_score.values()) if self.red_score else 1
|
||||||
|
max_b = max(self.blue_score.values()) if self.blue_score else 1
|
||||||
|
for i in self.red_score:
|
||||||
|
self.red_score[i] /= max_r
|
||||||
|
for i in self.blue_score:
|
||||||
|
self.blue_score[i] /= max_b
|
||||||
|
|
||||||
|
|
||||||
|
# ==================== 集成学习引擎 ====================
|
||||||
|
|
||||||
|
class EnsembleLearner:
|
||||||
|
"""集成学习引擎：多模型加权 + 回测优化权重"""
|
||||||
|
|
||||||
|
def __init__(self, lt, data, rule):
|
||||||
|
self.lt = lt
|
||||||
|
self.data = data
|
||||||
|
self.rule = rule
|
||||||
|
self.rcnt = rule["red_count"]
|
||||||
|
self.rmax = rule["red_max"]
|
||||||
|
self.bcnt = rule["blue_count"]
|
||||||
|
self.bmax = rule["blue_max"]
|
||||||
|
|
||||||
|
# 初始化5个模型
|
||||||
|
self.models = {
|
||||||
|
'freq': FrequencyModel(lt, data, rule),
|
||||||
|
'bayes': BayesianModel(lt, data, rule),
|
||||||
|
'markov': MarkovModel(lt, data, rule),
|
||||||
|
'kmeans': KMeansModel(lt, data, rule),
|
||||||
|
'residual': ResidualModel(lt, data, rule)
|
||||||
|
}
|
||||||
|
|
||||||
|
# 初始权重（等权）
|
||||||
|
self.weights = {m: 1.0 / len(self.models) for m in self.models}
|
||||||
|
self.initial_weights = self.weights.copy()
|
||||||
|
|
||||||
|
# 回测结果
|
||||||
|
self.backtest_results = None
|
||||||
|
|
||||||
|
def get_combined_score(self):
|
||||||
|
"""获取集成后的综合评分"""
|
||||||
|
red_combined = {}
|
||||||
|
blue_combined = {}
|
||||||
|
|
||||||
|
for model_name, model in self.models.items():
|
||||||
|
w = self.weights[model_name]
|
||||||
|
for i in range(1, self.rmax + 1):
|
||||||
|
red_combined[i] = red_combined.get(i, 0) + w * model.red_score[i]
|
||||||
|
for i in range(1, self.bmax + 1):
|
||||||
|
blue_combined[i] = blue_combined.get(i, 0) + w * model.blue_score[i]
|
||||||
|
|
||||||
|
# 归一化
|
||||||
|
max_r = max(red_combined.values()) if red_combined else 1
|
||||||
|
max_b = max(blue_combined.values()) if blue_combined else 1
|
||||||
|
for i in red_combined:
|
||||||
|
red_combined[i] /= max_r
|
||||||
|
for i in blue_combined:
|
||||||
|
blue_combined[i] /= max_b
|
||||||
|
|
||||||
|
return red_combined, blue_combined
|
||||||
|
|
||||||
|
def _get_combined_for_weights(self, weights):
|
||||||
|
"""根据给定权重获取组合评分"""
|
||||||
|
red_c = {}
|
||||||
|
blue_c = {}
|
||||||
|
for model_name, model in self.models.items():
|
||||||
|
ww = weights[model_name]
|
||||||
|
for i in range(1, self.rmax + 1):
|
||||||
|
red_c[i] = red_c.get(i, 0) + ww * model.red_score[i]
|
||||||
|
for i in range(1, self.bmax + 1):
|
||||||
|
blue_c[i] = blue_c.get(i, 0) + ww * model.blue_score[i]
|
||||||
|
return red_c, blue_c
|
||||||
|
|
||||||
|
def _predict_from_scores(self, red_c, blue_c, rmax, bmax, rcnt, bcnt):
|
||||||
|
"""根据评分预测号码"""
|
||||||
|
sorted_r = sorted(red_c.items(), key=lambda x: x[1], reverse=True)
|
||||||
|
top_r = [n for n, s in sorted_r[:rmax]]
|
||||||
|
sorted_b = sorted(blue_c.items(), key=lambda x: x[1], reverse=True)
|
||||||
|
top_b = [n for n, s in sorted_b[:bmax]]
|
||||||
|
|
||||||
|
sel = []
|
||||||
|
cand = top_r[:]
|
||||||
|
weights_r = [red_c.get(n, 0.5) for n in cand]
|
||||||
|
while len(sel) < rcnt and cand:
|
||||||
|
tw = sum(weights_r)
|
||||||
|
if tw <= 0: pk = random.choice(cand)
|
||||||
|
else:
|
||||||
|
rv = random.random() * tw
|
||||||
|
cum = 0
|
||||||
|
for i, ww in enumerate(weights_r):
|
||||||
|
cum += ww
|
||||||
|
if rv <= cum:
|
||||||
|
pk = cand[i]
|
||||||
|
break
|
||||||
|
if pk not in sel: sel.append(pk)
|
||||||
|
idx = cand.index(pk); cand.pop(idx); weights_r.pop(idx)
|
||||||
|
sel.sort()
|
||||||
|
|
||||||
|
bls = []
|
||||||
|
cand_b = top_b[:]
|
||||||
|
weights_b = [blue_c.get(n, 0.5) for n in cand_b]
|
||||||
|
while len(bls) < bcnt and cand_b:
|
||||||
|
tw = sum(weights_b)
|
||||||
|
if tw <= 0: pk = random.choice(cand_b)
|
||||||
|
else:
|
||||||
|
rv = random.random() * tw
|
||||||
|
cum = 0
|
||||||
|
for i, ww in enumerate(weights_b):
|
||||||
|
cum += ww
|
||||||
|
if rv <= cum:
|
||||||
|
pk = cand_b[i]
|
||||||
|
break
|
||||||
|
if pk not in bls: bls.append(pk)
|
||||||
|
idx = cand_b.index(pk); cand_b.pop(idx); weights_b.pop(idx)
|
||||||
|
bls.sort()
|
||||||
|
|
||||||
|
return sel, bls
|
||||||
|
|
||||||
|
def backtest_optimize(self, test_periods=30):
|
||||||
|
"""
|
||||||
|
通过回测优化各模型权重
|
||||||
|
使用简单网格搜索 + 梯度上升混合方法
|
||||||
|
"""
|
||||||
|
rule = self.rule
|
||||||
|
rmax, bmax = self.rule["red_max"], self.rule["blue_max"]
|
||||||
|
|
||||||
|
print(f"\n{'='*60}")
|
||||||
|
print(f"🧠 集成学习权重优化 | 彩种: {rule['name']} | 数据期数: {len(self.data)}")
|
||||||
|
print(f"{'='*60}")
|
||||||
|
|
||||||
|
# 准备回测数据（按时间顺序）
|
||||||
|
sorted_data = sorted(self.data, key=lambda x: int(x["issue"]))
|
||||||
|
if len(sorted_data) < test_periods + 10:
|
||||||
|
test_periods = max(10, len(sorted_data) - 10)
|
||||||
|
|
||||||
|
# 评分函数：计算推荐与实际匹配的分数
|
||||||
|
def evaluate(weights, data_slice):
|
||||||
|
red_comp, blue_comp = self._get_combined_for_weights(weights)
|
||||||
|
pred_r, pred_b = self._predict_from_scores(red_comp, blue_comp, rmax, bmax, rule["red_count"], rule["blue_count"])
|
||||||
|
|
||||||
|
actual = data_slice
|
||||||
|
act_r = [int(x) for x in actual["reds"]]
|
||||||
|
act_b = actual["blue"] if isinstance(actual["blue"], list) else [int(actual["blue"])]
|
||||||
|
|
||||||
|
# 评分：命中红球数*2 + 命中蓝球数
|
||||||
|
hit_r = sum(1 for n in pred_r if n in act_r)
|
||||||
|
hit_b = sum(1 for n in pred_b if n in act_b if (isinstance(act_b, list) and n in act_b) or (isinstance(act_b, int) and n == act_b))
|
||||||
|
return hit_r * 2 + hit_b
|
||||||
|
|
||||||
|
# 简单网格搜索优化权重
|
||||||
|
best_weights = self.weights.copy()
|
||||||
|
best_score = float('-inf')
|
||||||
|
|
||||||
|
# 初始评估
|
||||||
|
current_score = 0
|
||||||
|
for i in range(test_periods):
|
||||||
|
if i + 10 < len(sorted_data):
|
||||||
|
slice_data = sorted_data[i + 10:i + 11] # 下一期作为测试
|
||||||
|
current_score += evaluate(self.weights, slice_data[0])
|
||||||
|
|
||||||
|
print(f" 初始等权总分: {current_score:.1f}")
|
||||||
|
|
||||||
|
# 简单梯度上升（每个模型权重微调）
|
||||||
|
improved = True
|
||||||
|
iterations = 0
|
||||||
|
max_iterations = 20
|
||||||
|
|
||||||
|
while improved and iterations < max_iterations:
|
||||||
|
improved = False
|
||||||
|
iterations += 1
|
||||||
|
|
||||||
|
for model_name in self.weights:
|
||||||
|
for delta in [-0.05, 0.05]: # 微调权重
|
||||||
|
test_w = self.weights.copy()
|
||||||
|
test_w[model_name] = max(0.01, min(0.9, test_w[model_name] + delta))
|
||||||
|
|
||||||
|
# 归一化
|
||||||
|
total = sum(test_w.values())
|
||||||
|
test_w = {k: v/total for k, v in test_w.items()}
|
||||||
|
|
||||||
|
score = 0
|
||||||
|
for i in range(test_periods):
|
||||||
|
if i + 10 < len(sorted_data):
|
||||||
|
slice_data = sorted_data[i + 10:i + 11]
|
||||||
|
score += evaluate(test_w, slice_data[0])
|
||||||
|
|
||||||
|
if score > best_score:
|
||||||
|
best_score = score
|
||||||
|
best_weights = test_w.copy()
|
||||||
|
improved = True
|
||||||
|
|
||||||
|
self.weights = best_weights
|
||||||
|
self.backtest_results = {
|
||||||
|
'iterations': iterations,
|
||||||
|
'best_score': best_score,
|
||||||
|
'test_periods': test_periods,
|
||||||
|
'model_weights': best_weights
|
||||||
|
}
|
||||||
|
|
||||||
|
# 输出各模型权重
|
||||||
|
print(f" 优化后权重:")
|
||||||
|
for m, w in sorted(self.weights.items(), key=lambda x: -x[1]):
|
||||||
|
model_names = {
|
||||||
|
'freq': '频率加权',
|
||||||
|
'bayes': '贝叶斯平滑',
|
||||||
|
'markov': '马尔可夫链',
|
||||||
|
'kmeans': 'K-Means聚类',
|
||||||
|
'residual': '残差回归'
|
||||||
|
}
|
||||||
|
print(f" {model_names.get(m, m):<12s}: {w:.4f}")
|
||||||
|
|
||||||
|
print(f" 最终集成总分: {best_score:.1f} (测试{test_periods}期)")
|
||||||
|
|
||||||
|
# 返回综合评分
|
||||||
|
return self.get_combined_score()
|
||||||
|
|
||||||
|
def get_recommendations(self, count=5):
|
||||||
|
"""基于集成评分生成推荐"""
|
||||||
|
red_comp, blue_comp = self.get_combined_score()
|
||||||
|
rmax, bmax = self.rule["red_max"], self.rule["blue_max"]
|
||||||
|
rcnt, bcnt = self.rule["red_count"], self.rule["blue_count"]
|
||||||
|
|
||||||
|
sorted_r = sorted(red_comp.items(), key=lambda x: x[1], reverse=True)
|
||||||
|
top_r = [n for n, s in sorted_r[:rmax]]
|
||||||
|
sorted_b = sorted(blue_comp.items(), key=lambda x: x[1], reverse=True)
|
||||||
|
top_b = [n for n, s in sorted_b[:bmax]]
|
||||||
|
|
||||||
|
seed = hashlib.md5(f"{self.lt}_ensemble".encode()).hexdigest()
|
||||||
|
res = []
|
||||||
|
for ci in range(count):
|
||||||
|
cs = hashlib.md5(f"ensemble_c{ci}_{seed}".encode()).hexdigest()
|
||||||
|
cr = random.Random(cs)
|
||||||
|
|
||||||
|
sel = []
|
||||||
|
cand = top_r[:]
|
||||||
|
w = [red_comp.get(n, 0.5) for n in cand]
|
||||||
|
while len(sel) < rcnt and cand:
|
||||||
|
tw = sum(w)
|
||||||
|
if tw <= 0: pk = cr.choice(cand)
|
||||||
|
else:
|
||||||
|
rv = cr.random() * tw
|
||||||
|
cum = 0
|
||||||
|
for i, ww in enumerate(w):
|
||||||
|
cum += ww
|
||||||
|
if rv <= cum:
|
||||||
|
pk = cand[i]
|
||||||
|
break
|
||||||
|
if pk not in sel: sel.append(pk)
|
||||||
|
idx = cand.index(pk); cand.pop(idx); w.pop(idx)
|
||||||
|
sel.sort()
|
||||||
|
|
||||||
|
bls = []
|
||||||
|
cand_b = top_b[:]
|
||||||
|
wb = [blue_comp.get(n, 0.5) for n in cand_b]
|
||||||
|
while len(bls) < bcnt and cand_b:
|
||||||
|
tw = sum(wb)
|
||||||
|
if tw <= 0: pk = cr.choice(cand_b)
|
||||||
|
else:
|
||||||
|
rv = cr.random() * tw
|
||||||
|
cum = 0
|
||||||
|
for i, ww in enumerate(wb):
|
||||||
|
cum += ww
|
||||||
|
if rv <= cum:
|
||||||
|
pk = cand_b[i]
|
||||||
|
break
|
||||||
|
if pk not in bls: bls.append(pk)
|
||||||
|
idx = cand_b.index(pk); cand_b.pop(idx); wb.pop(idx)
|
||||||
|
bls.sort()
|
||||||
|
|
||||||
|
score = round(sum(red_comp.get(n, 0) for n in sel) + sum(blue_comp.get(n, 0) for n in bls), 4)
|
||||||
|
mid = rmax / 2
|
||||||
|
res.append({
|
||||||
|
"reds": sel,
|
||||||
|
"blues": bls,
|
||||||
|
"score": score,
|
||||||
|
"odd_even": f"{sum(1 for n in sel if n % 2 == 1)}:{rcnt - sum(1 for n in sel if n % 2 == 1)}",
|
||||||
|
"big_small": f"{sum(1 for n in sel if n > mid)}:{rcnt - sum(1 for n in sel if n > mid)}",
|
||||||
|
"sum": sum(sel)
|
||||||
|
})
|
||||||
|
|
||||||
|
res.sort(key=lambda x: x["score"], reverse=True)
|
||||||
|
return res
|
||||||
|
|
||||||
|
|
||||||
|
# ==================== 主函数 ====================
|
||||||
|
|
||||||
|
def ensemble_recommendation(lt, data, rule, count=5):
|
||||||
|
"""集成学习推荐主接口"""
|
||||||
|
learner = EnsembleLearner(lt, data, rule)
|
||||||
|
red_comp, blue_comp = learner.backtest_optimize(test_periods=30)
|
||||||
|
recommendations = learner.get_recommendations(count=count)
|
||||||
|
|
||||||
|
return {
|
||||||
|
'weights': learner.weights,
|
||||||
|
'backtest': learner.backtest_results,
|
||||||
|
'recommendations': recommendations,
|
||||||
|
'combined_scores': {
|
||||||
|
'red': red_comp,
|
||||||
|
'blue': blue_comp
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
# 简单测试
|
||||||
|
print("集成学习模块加载成功！")
|
||||||
|
print("可用模型：频率加权、贝叶斯平滑、马尔可夫链、K-Means聚类、残差回归")
|
||||||
@@ -0,0 +1,285 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
回滚迭代跟踪工具 - 管理 AI 进化版本和回滚机制
|
||||||
|
"""
|
||||||
|
|
||||||
|
import json
|
||||||
|
import os
|
||||||
|
from datetime import datetime
|
||||||
|
from typing import Dict, List, Optional, Any
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
|
||||||
|
class EvolutionTracker:
|
||||||
|
"""回滚迭代跟踪器"""
|
||||||
|
|
||||||
|
def __init__(self, data_dir: str = "./evolution_tracking"):
|
||||||
|
self.data_dir = Path(data_dir)
|
||||||
|
self.data_dir.mkdir(parents=True, exist_ok=True)
|
||||||
|
self.version_file = self.data_dir / "versions.json"
|
||||||
|
self.performance_log = self.data_dir / "performance.json"
|
||||||
|
self.rollback_log = self.data_dir / "rollbacks.json"
|
||||||
|
|
||||||
|
def initialize(self):
|
||||||
|
"""初始化跟踪器"""
|
||||||
|
# 初始化版本记录
|
||||||
|
if not self.version_file.exists():
|
||||||
|
initial_version = {
|
||||||
|
"version": 0,
|
||||||
|
"created_at": datetime.now().isoformat(),
|
||||||
|
"performance": {
|
||||||
|
"win_rate": 0.0,
|
||||||
|
"total_bets": 0,
|
||||||
|
"total_winnings": 0.0
|
||||||
|
}
|
||||||
|
}
|
||||||
|
self.save_version(initial_version)
|
||||||
|
|
||||||
|
# 初始化性能日志
|
||||||
|
if not self.performance_log.exists():
|
||||||
|
self.save_performance({
|
||||||
|
"history": [],
|
||||||
|
"current": {
|
||||||
|
"total_bets": 0,
|
||||||
|
"win_rate": 0.0,
|
||||||
|
"earnings": 0.0,
|
||||||
|
"total_winnings": 0.0
|
||||||
|
}
|
||||||
|
})
|
||||||
|
|
||||||
|
# 初始化回滚日志
|
||||||
|
if not self.rollback_log.exists():
|
||||||
|
self.save_rollbacks({
|
||||||
|
"history": [],
|
||||||
|
"current_version": 0
|
||||||
|
})
|
||||||
|
|
||||||
|
def save_version(self, version_data: Dict):
|
||||||
|
"""保存版本信息"""
|
||||||
|
with open(self.version_file, 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(version_data, f, ensure_ascii=False, indent=2)
|
||||||
|
|
||||||
|
def load_version(self) -> Dict:
|
||||||
|
"""加载当前版本"""
|
||||||
|
if self.version_file.exists():
|
||||||
|
with open(self.version_file, 'r', encoding='utf-8') as f:
|
||||||
|
return json.load(f)
|
||||||
|
return {}
|
||||||
|
|
||||||
|
def record_performance(self, version: int, win_rate: float, earnings: float):
|
||||||
|
"""记录性能数据"""
|
||||||
|
performance = self.load_performance()
|
||||||
|
|
||||||
|
entry = {
|
||||||
|
"version": version,
|
||||||
|
"recorded_at": datetime.now().isoformat(),
|
||||||
|
"win_rate": win_rate,
|
||||||
|
"earnings": earnings,
|
||||||
|
"total_bets": performance["current"].get("total_bets", 0),
|
||||||
|
"total_winnings": performance["current"].get("total_winnings", 0.0)
|
||||||
|
}
|
||||||
|
|
||||||
|
performance["history"].append(entry)
|
||||||
|
|
||||||
|
# 更新当前性能
|
||||||
|
current = performance["current"]
|
||||||
|
current["total_bets"] += 1
|
||||||
|
current["win_rate"] = win_rate
|
||||||
|
current["earnings"] = earnings
|
||||||
|
current["total_winnings"] = current.get("total_winnings", 0.0) + earnings
|
||||||
|
|
||||||
|
self.save_performance(performance)
|
||||||
|
|
||||||
|
def load_performance(self) -> Dict:
|
||||||
|
"""加载性能日志"""
|
||||||
|
if self.performance_log.exists():
|
||||||
|
with open(self.performance_log, 'r', encoding='utf-8') as f:
|
||||||
|
return json.load(f)
|
||||||
|
return {"history": [], "current": {}}
|
||||||
|
|
||||||
|
def save_performance(self, performance: Dict):
|
||||||
|
"""保存性能日志"""
|
||||||
|
with open(self.performance_log, 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(performance, f, ensure_ascii=False, indent=2)
|
||||||
|
|
||||||
|
def record_rollback(self, from_version: int, to_version: int, reason: str):
|
||||||
|
"""记录回滚操作"""
|
||||||
|
rollbacks = self.load_rollbacks()
|
||||||
|
|
||||||
|
entry = {
|
||||||
|
"from_version": from_version,
|
||||||
|
"to_version": to_version,
|
||||||
|
"reason": reason,
|
||||||
|
"timestamp": datetime.now().isoformat()
|
||||||
|
}
|
||||||
|
|
||||||
|
rollbacks["history"].append(entry)
|
||||||
|
rollbacks["current_version"] = to_version
|
||||||
|
|
||||||
|
self.save_rollbacks(rollbacks)
|
||||||
|
|
||||||
|
def load_rollbacks(self) -> Dict:
|
||||||
|
"""加载回滚日志"""
|
||||||
|
if self.rollback_log.exists():
|
||||||
|
with open(self.rollback_log, 'r', encoding='utf-8') as f:
|
||||||
|
return json.load(f)
|
||||||
|
return {"history": [], "current_version": 0}
|
||||||
|
|
||||||
|
def save_rollbacks(self, rollbacks: Dict):
|
||||||
|
"""保存回滚日志"""
|
||||||
|
with open(self.rollback_log, 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(rollbacks, f, ensure_ascii=False, indent=2)
|
||||||
|
|
||||||
|
def get_version_history(self) -> List[Dict]:
|
||||||
|
"""获取版本历史"""
|
||||||
|
performance = self.load_performance()
|
||||||
|
return performance.get("history", [])
|
||||||
|
|
||||||
|
def get_best_version(self) -> Optional[Dict]:
|
||||||
|
"""获取性能最好的版本"""
|
||||||
|
history = self.get_version_history()
|
||||||
|
if not history:
|
||||||
|
return None
|
||||||
|
|
||||||
|
best = max(history, key=lambda x: x.get("win_rate", 0))
|
||||||
|
return best
|
||||||
|
|
||||||
|
def compare_versions(self, version1: int, version2: int) -> Dict:
|
||||||
|
"""比较两个版本的性能"""
|
||||||
|
history = self.get_version_history()
|
||||||
|
|
||||||
|
v1_data = next((h for h in history if h["version"] == version1), None)
|
||||||
|
v2_data = next((h for h in history if h["version"] == version2), None)
|
||||||
|
|
||||||
|
if not v1_data or not v2_data:
|
||||||
|
return {"error": "版本不存在"}
|
||||||
|
|
||||||
|
comparison = {
|
||||||
|
"version1": version1,
|
||||||
|
"version2": version2,
|
||||||
|
"v1_win_rate": v1_data.get("win_rate", 0),
|
||||||
|
"v2_win_rate": v2_data.get("win_rate", 0),
|
||||||
|
"v1_earnings": v1_data.get("earnings", 0.0),
|
||||||
|
"v2_earnings": v2_data.get("earnings", 0.0),
|
||||||
|
"better_version": version1 if v1_data.get("win_rate", 0) > v2_data.get("win_rate", 0) else version2
|
||||||
|
}
|
||||||
|
|
||||||
|
return comparison
|
||||||
|
|
||||||
|
def generate_report(self) -> str:
|
||||||
|
"""生成进化报告"""
|
||||||
|
performance = self.load_performance()
|
||||||
|
rollbacks = self.load_rollbacks()
|
||||||
|
|
||||||
|
report = [
|
||||||
|
"="*60,
|
||||||
|
"AI 进化跟踪报告",
|
||||||
|
"="*60,
|
||||||
|
f"当前版本：{rollbacks.get('current_version', 0)}",
|
||||||
|
f"回滚历史数：{len(rollbacks.get('history', []))}",
|
||||||
|
"\n当前性能数据：",
|
||||||
|
f" 胜率：{performance['current'].get('win_rate', 0):.2%}",
|
||||||
|
f" 累计投注：{performance['current'].get('total_bets', 0)}",
|
||||||
|
f" 累计收益：¥{performance['current'].get('total_winnings', 0.0):.2f}",
|
||||||
|
"\n版本历史：",
|
||||||
|
]
|
||||||
|
|
||||||
|
history = performance.get("history", [])
|
||||||
|
if history:
|
||||||
|
for i, entry in enumerate(history[-10:], 1): # 显示最后 10 个
|
||||||
|
report.extend([
|
||||||
|
f" [v{entry['version']}] 胜率：{entry['win_rate']:.2%}, "
|
||||||
|
f"收益：¥{entry['earnings']:.2f}, "
|
||||||
|
f"记录时间：{entry['recorded_at']}",
|
||||||
|
""
|
||||||
|
])
|
||||||
|
else:
|
||||||
|
report.append(" 暂无历史数据")
|
||||||
|
|
||||||
|
report.extend(["\n回滚历史：", ""])
|
||||||
|
|
||||||
|
if rollbacks.get("history"):
|
||||||
|
for i, entry in enumerate(rollbacks["history"]):
|
||||||
|
report.extend([
|
||||||
|
f" 回滚 #{i+1}: v{entry['from_version']} → v{entry['to_version']}, "
|
||||||
|
f"原因：{entry['reason']}, "
|
||||||
|
f"时间：{entry['timestamp']}",
|
||||||
|
""
|
||||||
|
])
|
||||||
|
else:
|
||||||
|
report.append(" 暂无回滚记录")
|
||||||
|
|
||||||
|
report.extend(["="*60, ""])
|
||||||
|
return "\n".join(report)
|
||||||
|
|
||||||
|
def optimize_weights(self, learning_rate: float = 0.1, epochs: int = 100) -> Dict:
|
||||||
|
"""优化模型权重 - 模拟进化过程"""
|
||||||
|
import random
|
||||||
|
|
||||||
|
# 初始化权重
|
||||||
|
weights = {
|
||||||
|
"red_hot": 0.3,
|
||||||
|
"red_cold": 0.2,
|
||||||
|
"consecutive": 0.15,
|
||||||
|
"sum_range": 0.15,
|
||||||
|
"parity": 0.1,
|
||||||
|
"entropy": 0.1
|
||||||
|
}
|
||||||
|
|
||||||
|
# 模拟进化过程
|
||||||
|
for epoch in range(epochs):
|
||||||
|
# 模拟数据输入
|
||||||
|
sample_data = [random.randint(1, 33) for _ in range(10)]
|
||||||
|
|
||||||
|
# 计算损失
|
||||||
|
loss = sum((w - random.random())**2 for w in weights.values())
|
||||||
|
|
||||||
|
# 梯度下降
|
||||||
|
for key in weights:
|
||||||
|
gradient = sum(random.random() for _ in range(10)) / len(sample_data)
|
||||||
|
weights[key] -= learning_rate * gradient
|
||||||
|
|
||||||
|
# 归一化权重
|
||||||
|
total = sum(weights.values())
|
||||||
|
weights = {k: round(v/total, 4) for k, v in weights.items()}
|
||||||
|
|
||||||
|
return {
|
||||||
|
"optimized_weights": weights,
|
||||||
|
"epochs_completed": epochs,
|
||||||
|
"final_loss": round(loss, 4)
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
"""主函数 - 测试回滚跟踪工具"""
|
||||||
|
tracker = EvolutionTracker()
|
||||||
|
tracker.initialize()
|
||||||
|
|
||||||
|
# 模拟一些版本记录
|
||||||
|
versions = [
|
||||||
|
(1, 0.05, 100.0),
|
||||||
|
(2, 0.12, 150.0),
|
||||||
|
(3, 0.08, 120.0),
|
||||||
|
(4, 0.15, 200.0),
|
||||||
|
(5, 0.18, 250.0)
|
||||||
|
]
|
||||||
|
|
||||||
|
for version, win_rate, earnings in versions:
|
||||||
|
tracker.record_performance(version, win_rate, earnings)
|
||||||
|
|
||||||
|
# 模拟回滚
|
||||||
|
tracker.record_rollback(5, 4, "性能下降")
|
||||||
|
|
||||||
|
# 生成报告
|
||||||
|
print(tracker.generate_report())
|
||||||
|
|
||||||
|
# 优化权重
|
||||||
|
optimized = tracker.optimize_weights()
|
||||||
|
print("\n优化结果：")
|
||||||
|
print(f" 完成轮数：{optimized['epochs_completed']}")
|
||||||
|
print(f" 最终损失：{optimized['final_loss']}")
|
||||||
|
print(f" 优化权重：{optimized['optimized_weights']}")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
@@ -0,0 +1,189 @@
|
|||||||
|
{
|
||||||
|
"iterations": [
|
||||||
|
{
|
||||||
|
"lt": "ssq",
|
||||||
|
"issue": "26063",
|
||||||
|
"date": "2026-06-04",
|
||||||
|
"pred_reds": [
|
||||||
|
11,
|
||||||
|
17,
|
||||||
|
21,
|
||||||
|
24,
|
||||||
|
28,
|
||||||
|
30
|
||||||
|
],
|
||||||
|
"pred_blues": [
|
||||||
|
2
|
||||||
|
],
|
||||||
|
"act_reds": [
|
||||||
|
2,
|
||||||
|
8,
|
||||||
|
25,
|
||||||
|
28,
|
||||||
|
30,
|
||||||
|
31
|
||||||
|
],
|
||||||
|
"act_blues": [
|
||||||
|
2
|
||||||
|
],
|
||||||
|
"hit_reds": [
|
||||||
|
28,
|
||||||
|
30
|
||||||
|
],
|
||||||
|
"hit_blues": [
|
||||||
|
2
|
||||||
|
],
|
||||||
|
"red_hits": 2,
|
||||||
|
"blue_hits": 1,
|
||||||
|
"total_hits": 3,
|
||||||
|
"earnings": 50.0
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"lt": "dlt",
|
||||||
|
"issue": "26064",
|
||||||
|
"date": "2026-06-10",
|
||||||
|
"pred_reds": [
|
||||||
|
1,
|
||||||
|
9,
|
||||||
|
14,
|
||||||
|
33,
|
||||||
|
34
|
||||||
|
],
|
||||||
|
"pred_blues": [
|
||||||
|
8,
|
||||||
|
10
|
||||||
|
],
|
||||||
|
"act_reds": [
|
||||||
|
3,
|
||||||
|
13,
|
||||||
|
15,
|
||||||
|
17,
|
||||||
|
21
|
||||||
|
],
|
||||||
|
"act_blues": [
|
||||||
|
2,
|
||||||
|
7
|
||||||
|
],
|
||||||
|
"hit_reds": [],
|
||||||
|
"hit_blues": [],
|
||||||
|
"red_hits": 0,
|
||||||
|
"blue_hits": 0,
|
||||||
|
"total_hits": 0,
|
||||||
|
"earnings": 0.0
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"lt": "dlt",
|
||||||
|
"issue": "26065",
|
||||||
|
"date": "2026-06-13",
|
||||||
|
"pred_reds": [
|
||||||
|
1,
|
||||||
|
9,
|
||||||
|
14,
|
||||||
|
33,
|
||||||
|
34
|
||||||
|
],
|
||||||
|
"pred_blues": [
|
||||||
|
8,
|
||||||
|
10
|
||||||
|
],
|
||||||
|
"act_reds": [
|
||||||
|
4,
|
||||||
|
11,
|
||||||
|
12,
|
||||||
|
13,
|
||||||
|
25
|
||||||
|
],
|
||||||
|
"act_blues": [
|
||||||
|
4,
|
||||||
|
8
|
||||||
|
],
|
||||||
|
"hit_reds": [],
|
||||||
|
"hit_blues": [
|
||||||
|
8
|
||||||
|
],
|
||||||
|
"red_hits": 0,
|
||||||
|
"blue_hits": 1,
|
||||||
|
"total_hits": 1,
|
||||||
|
"earnings": 0.0
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"lt": "dlt",
|
||||||
|
"issue": "26078",
|
||||||
|
"date": "2026-07-13",
|
||||||
|
"pred_reds": [
|
||||||
|
6,
|
||||||
|
13,
|
||||||
|
19,
|
||||||
|
22,
|
||||||
|
29
|
||||||
|
],
|
||||||
|
"pred_blues": [
|
||||||
|
6,
|
||||||
|
8
|
||||||
|
],
|
||||||
|
"act_reds": [
|
||||||
|
2,
|
||||||
|
13,
|
||||||
|
20,
|
||||||
|
25,
|
||||||
|
32
|
||||||
|
],
|
||||||
|
"act_blues": [
|
||||||
|
8,
|
||||||
|
11
|
||||||
|
],
|
||||||
|
"hit_reds": [
|
||||||
|
13
|
||||||
|
],
|
||||||
|
"hit_blues": [
|
||||||
|
8
|
||||||
|
],
|
||||||
|
"red_hits": 1,
|
||||||
|
"blue_hits": 1,
|
||||||
|
"total_hits": 2,
|
||||||
|
"earnings": 0.0
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"accuracy_history": [
|
||||||
|
{
|
||||||
|
"issue": "26063",
|
||||||
|
"lt": "ssq",
|
||||||
|
"hits": 3,
|
||||||
|
"avg_accuracy": 42.86
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"issue": "26064",
|
||||||
|
"lt": "dlt",
|
||||||
|
"hits": 0,
|
||||||
|
"avg_accuracy": 0.0
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"issue": "26065",
|
||||||
|
"lt": "dlt",
|
||||||
|
"hits": 1,
|
||||||
|
"avg_accuracy": 7.14
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"issue": "26078",
|
||||||
|
"lt": "dlt",
|
||||||
|
"hits": 2,
|
||||||
|
"avg_accuracy": 14.29
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"ssq": {
|
||||||
|
"total": 1,
|
||||||
|
"correct_reds": 2,
|
||||||
|
"correct_blues": 1,
|
||||||
|
"best": 3,
|
||||||
|
"profitable_wins": 1,
|
||||||
|
"total_earnings": 50.0
|
||||||
|
},
|
||||||
|
"dlt": {
|
||||||
|
"total": 3,
|
||||||
|
"correct_reds": 1,
|
||||||
|
"correct_blues": 2,
|
||||||
|
"best": 2,
|
||||||
|
"profitable_wins": 0,
|
||||||
|
"total_earnings": 0.0
|
||||||
|
}
|
||||||
|
}
|
||||||
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,758 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
增强版彩票智能分析系统 v2.0
|
||||||
|
===============================
|
||||||
|
核心升级：
|
||||||
|
1. 采集近200期数据（原100期）
|
||||||
|
2. AI逐期回滚推演：从第200期开始->模拟分析机选->比对实开->回滚优化
|
||||||
|
3. 方法论自优化：每轮回滚后总结方法论并迭代升级
|
||||||
|
4. 目标：胜出率(概率占比) >= 51%
|
||||||
|
|
||||||
|
用法:
|
||||||
|
python enhanced_lottery.py --action full_report --lottery ssq --count 5
|
||||||
|
python enhanced_lottery.py --action rollback --lottery ssq
|
||||||
|
python enhanced_lottery.py --action self_evolve --lottery ssq --iterations 10
|
||||||
|
"""
|
||||||
|
|
||||||
|
import json, os, sys, math, random, copy, datetime
|
||||||
|
from collections import Counter
|
||||||
|
|
||||||
|
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||||
|
DATA_DIR = os.path.join(BASE_DIR, "data")
|
||||||
|
os.makedirs(DATA_DIR, exist_ok=True)
|
||||||
|
|
||||||
|
LOTTERY_RULES = {
|
||||||
|
"ssq": {
|
||||||
|
"name": "双色球",
|
||||||
|
"red_count": 6, "red_max": 33, "blue_count": 1, "blue_max": 16,
|
||||||
|
"draw_days": [2, 4, 7], "draw_time": "21:15",
|
||||||
|
},
|
||||||
|
"dlt": {
|
||||||
|
"name": "大乐透",
|
||||||
|
"red_count": 5, "red_max": 35, "blue_count": 2, "blue_max": 12,
|
||||||
|
"draw_days": [1, 3, 6], "draw_time": "20:25",
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
DEFAULT_WEIGHTS = {
|
||||||
|
"frequency": 0.25,
|
||||||
|
"hot_number": 0.20,
|
||||||
|
"missing": 0.15,
|
||||||
|
"tail": 0.10,
|
||||||
|
"zone": 0.08,
|
||||||
|
"odd_even": 0.06,
|
||||||
|
"big_small": 0.05,
|
||||||
|
"sum_value": 0.04,
|
||||||
|
"probability": 0.04,
|
||||||
|
"consecutive": 0.03,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
# ============================================================
|
||||||
|
# 增强版分析引擎（200期 + 10维度深度分析）
|
||||||
|
# ============================================================
|
||||||
|
|
||||||
|
def _normalize_rec(rec):
|
||||||
|
"""标准化记录，兼容新旧格式（blue vs blues）"""
|
||||||
|
if "blues" not in rec or not rec["blues"]:
|
||||||
|
if "blue" in rec:
|
||||||
|
rec["blues"] = rec["blue"] if isinstance(rec["blue"], list) else [rec["blue"]]
|
||||||
|
return rec
|
||||||
|
|
||||||
|
|
||||||
|
def _get_blues(rec):
|
||||||
|
"""获取blue值，兼容新旧格式"""
|
||||||
|
if "blues" in rec and rec["blues"]:
|
||||||
|
raw = rec["blues"]
|
||||||
|
elif "blue" in rec:
|
||||||
|
raw = rec["blue"]
|
||||||
|
else:
|
||||||
|
raw = []
|
||||||
|
return raw if isinstance(raw, list) else [raw]
|
||||||
|
|
||||||
|
|
||||||
|
class EnhancedAnalyzer:
|
||||||
|
"""使用近200期数据，10维度权重可调，输出结构化分析结果"""
|
||||||
|
|
||||||
|
def __init__(self, data, lt, weights=None):
|
||||||
|
self.lt = lt
|
||||||
|
self.rule = LOTTERY_RULES[lt]
|
||||||
|
# 标准化并取近200期
|
||||||
|
normed = [_normalize_rec(d) for d in data]
|
||||||
|
self.data = sorted(normed, key=lambda x: int(x["issue"]), reverse=True)[:200]
|
||||||
|
self.weights = weights if weights else copy.deepcopy(DEFAULT_WEIGHTS)
|
||||||
|
self.rmax = self.rule["red_max"]
|
||||||
|
self.bmax = self.rule["blue_max"]
|
||||||
|
self.rcnt = self.rule["red_count"]
|
||||||
|
self.bcnt = self.rule["blue_count"]
|
||||||
|
self.mid = self.rmax / 2
|
||||||
|
|
||||||
|
def analyze(self):
|
||||||
|
"""完整分析，返回结构化分析结果"""
|
||||||
|
if not self.data:
|
||||||
|
return None
|
||||||
|
|
||||||
|
# 1. 频率分析
|
||||||
|
all_r, all_b = [], []
|
||||||
|
for rec in self.data:
|
||||||
|
all_r.extend(int(x) for x in rec["reds"])
|
||||||
|
bl = _get_blues(rec)
|
||||||
|
all_b.extend(int(x) for x in bl)
|
||||||
|
|
||||||
|
rf = Counter(all_r)
|
||||||
|
bf = Counter(all_b)
|
||||||
|
max_rf = max(rf.values()) or 1
|
||||||
|
max_bf = max(bf.values()) or 1
|
||||||
|
rf_score = {i: rf.get(i, 0) / max_rf for i in range(1, self.rmax + 1)}
|
||||||
|
bf_score = {i: bf.get(i, 0) / max_bf for i in range(1, self.bmax + 1)}
|
||||||
|
|
||||||
|
# 2. 热号分析（最近20期）
|
||||||
|
hot = self.data[:20]
|
||||||
|
hot_r = [int(x) for r in hot for x in r["reds"]]
|
||||||
|
hot_rf = Counter(hot_r)
|
||||||
|
max_hr = max(hot_rf.values()) or 1
|
||||||
|
hot_score = {i: hot_rf.get(i, 0) / max_hr for i in range(1, self.rmax + 1)}
|
||||||
|
|
||||||
|
# 3. 遗漏值分析
|
||||||
|
r_miss = {}
|
||||||
|
for n in range(1, self.rmax + 1):
|
||||||
|
cnt = 0
|
||||||
|
for r in self.data:
|
||||||
|
if n in [int(x) for x in r["reds"]]:
|
||||||
|
break
|
||||||
|
cnt += 1
|
||||||
|
r_miss[n] = cnt
|
||||||
|
max_rm = max(r_miss.values()) or 1
|
||||||
|
rm_score = {n: math.sin(m / max_rm * math.pi) * 0.8 + 0.2 for n, m in r_miss.items()}
|
||||||
|
|
||||||
|
b_miss = {}
|
||||||
|
for n in range(1, self.bmax + 1):
|
||||||
|
cnt = 0
|
||||||
|
for r in self.data:
|
||||||
|
ns = [int(x) for x in _get_blues(r)]
|
||||||
|
if n in ns:
|
||||||
|
break
|
||||||
|
cnt += 1
|
||||||
|
b_miss[n] = cnt
|
||||||
|
max_bm = max(b_miss.values()) or 1
|
||||||
|
bm_score = {n: math.sin(m / max_bm * math.pi) * 0.8 + 0.2 for n, m in b_miss.items()}
|
||||||
|
|
||||||
|
# 4. 奇偶模式
|
||||||
|
oe_counter = Counter()
|
||||||
|
for rec in self.data:
|
||||||
|
ns = [int(x) for x in rec["reds"]]
|
||||||
|
odd_count = sum(1 for n in ns if n % 2 == 1)
|
||||||
|
oe_counter[f"{odd_count}:{self.rcnt - odd_count}"] += 1
|
||||||
|
most_oe = oe_counter.most_common(1)[0][0] if oe_counter else "3:3"
|
||||||
|
|
||||||
|
# 5. 大小模式
|
||||||
|
bs_counter = Counter()
|
||||||
|
for rec in self.data:
|
||||||
|
ns = [int(x) for x in rec["reds"]]
|
||||||
|
big_count = sum(1 for n in ns if n > self.mid)
|
||||||
|
bs_counter[f"{big_count}:{self.rcnt - big_count}"] += 1
|
||||||
|
most_bs = bs_counter.most_common(1)[0][0] if bs_counter else "3:3"
|
||||||
|
|
||||||
|
# 6. 和值分析
|
||||||
|
sums = [sum(int(x) for x in r["reds"]) for r in self.data]
|
||||||
|
avg_sum = sum(sums) / len(sums) if sums else 0
|
||||||
|
std_sum = math.sqrt(sum((s - avg_sum) ** 2 for s in sums) / len(sums)) if sums else 0
|
||||||
|
|
||||||
|
# 7. 尾数分布
|
||||||
|
tail_counter = Counter()
|
||||||
|
for rec in self.data:
|
||||||
|
for ns in rec["reds"]:
|
||||||
|
tail_counter[int(ns) % 10] += 1
|
||||||
|
max_tf = max(tail_counter.values()) or 1
|
||||||
|
tail_score = {t: tail_counter.get(t, 0) / max_tf for t in range(10)}
|
||||||
|
|
||||||
|
# 8. 区间分布
|
||||||
|
zones = [(1, 11), (12, 22), (23, self.rmax)] if self.lt == "ssq" else [(1, 12), (13, 24), (25, self.rmax)]
|
||||||
|
zone_balance = []
|
||||||
|
for zs, ze in zones:
|
||||||
|
count = sum(1 for r in self.data for n in [int(x) for x in r["reds"]] if zs <= n <= ze)
|
||||||
|
expected = len(self.data) * self.rcnt / len(zones)
|
||||||
|
zone_balance.append(count / expected if expected else 1.0)
|
||||||
|
|
||||||
|
# 9. 连号分析
|
||||||
|
consec_counter = Counter()
|
||||||
|
for rec in self.data:
|
||||||
|
ns = sorted(int(x) for x in rec["reds"])
|
||||||
|
consec = sum(1 for i in range(len(ns) - 1) if ns[i + 1] - ns[i] == 1)
|
||||||
|
consec_counter[consec] += 1
|
||||||
|
most_consec = consec_counter.most_common(1)[0][0] if consec_counter else 0
|
||||||
|
|
||||||
|
# 10. 综合评分
|
||||||
|
r_comp = {}
|
||||||
|
for n in range(1, self.rmax + 1):
|
||||||
|
s = rf_score.get(n, 0) * self.weights["frequency"]
|
||||||
|
s += hot_score.get(n, 0) * self.weights["hot_number"]
|
||||||
|
s += rm_score.get(n, 0.5) * self.weights["missing"]
|
||||||
|
s += tail_score.get(n % 10, 0.5) * self.weights["tail"]
|
||||||
|
for i, (zs, ze) in enumerate(zones):
|
||||||
|
if zs <= n <= ze and zone_balance[i] < 0.85:
|
||||||
|
s += 0.10
|
||||||
|
break
|
||||||
|
r_comp[n] = round(s, 4)
|
||||||
|
|
||||||
|
# 蓝球复合评分
|
||||||
|
recent_blue = []
|
||||||
|
for r in self.data[:20]:
|
||||||
|
recent_blue.extend(int(x) for x in (r["blues"] if isinstance(r["blues"], list) else [r["blues"]]))
|
||||||
|
rbf = Counter(recent_blue)
|
||||||
|
max_rbf = max(rbf.values()) or 1
|
||||||
|
b_comp = {}
|
||||||
|
for n in range(1, self.bmax + 1):
|
||||||
|
s = bf_score.get(n, 0) * 0.35 + bm_score.get(n, 0.5) * 0.40 + (rbf.get(n, 0) / max_rbf) * 0.25
|
||||||
|
b_comp[n] = round(s, 4)
|
||||||
|
|
||||||
|
result = {
|
||||||
|
"data_range": {
|
||||||
|
"total": len(self.data),
|
||||||
|
"start": self.data[-1]["issue"],
|
||||||
|
"end": self.data[0]["issue"]
|
||||||
|
},
|
||||||
|
"red_composite": r_comp,
|
||||||
|
"blue_composite": b_comp,
|
||||||
|
"patterns": {
|
||||||
|
"most_oe": most_oe,
|
||||||
|
"most_bs": most_bs,
|
||||||
|
"avg_sum": round(avg_sum, 1),
|
||||||
|
"std_sum": round(std_sum, 1),
|
||||||
|
"sum_range": [round(avg_sum - std_sum, 1), round(avg_sum + std_sum, 1)],
|
||||||
|
"zone_balance": {f"Z{i+1}": round(zb, 3) for i, zb in enumerate(zone_balance)},
|
||||||
|
"most_consec": most_consec,
|
||||||
|
},
|
||||||
|
"weights_used": copy.deepcopy(self.weights),
|
||||||
|
}
|
||||||
|
return result
|
||||||
|
|
||||||
|
def recommend(self, analysis, count=5):
|
||||||
|
"""基于分析结果生成推荐号码"""
|
||||||
|
if not analysis:
|
||||||
|
return []
|
||||||
|
|
||||||
|
r_comp = analysis["red_composite"]
|
||||||
|
b_comp = analysis["blue_composite"]
|
||||||
|
pat = analysis["patterns"]
|
||||||
|
|
||||||
|
sorted_r = sorted(r_comp.keys(), key=lambda n: r_comp[n], reverse=True)
|
||||||
|
sorted_b = sorted(b_comp.keys(), key=lambda n: b_comp[n], reverse=True)
|
||||||
|
|
||||||
|
target_oe = [int(x) for x in pat["most_oe"].split(":")]
|
||||||
|
target_bs = [int(x) for x in pat["most_bs"].split(":")]
|
||||||
|
sum_min, sum_max = pat["sum_range"]
|
||||||
|
|
||||||
|
seed = int(datetime.datetime.now().timestamp() * 1000) % 1000000
|
||||||
|
rng = random.Random(seed)
|
||||||
|
|
||||||
|
results = []
|
||||||
|
for _ in range(count * 3):
|
||||||
|
top_r = sorted_r[:20]
|
||||||
|
weights_r = [max(r_comp[n], 0) for n in top_r]
|
||||||
|
total_w = sum(weights_r)
|
||||||
|
if total_w <= 0:
|
||||||
|
weights_r = [1] * len(top_r)
|
||||||
|
total_w = len(top_r)
|
||||||
|
probs_r = [w / total_w for w in weights_r]
|
||||||
|
|
||||||
|
reds = set()
|
||||||
|
attempts = 0
|
||||||
|
while len(reds) < self.rcnt and attempts < 100:
|
||||||
|
n = rng.choices(top_r, weights=probs_r, k=1)[0]
|
||||||
|
reds.add(n)
|
||||||
|
attempts += 1
|
||||||
|
if len(reds) < self.rcnt:
|
||||||
|
remaining = [n for n in range(1, self.rmax + 1) if n not in reds]
|
||||||
|
rng.shuffle(remaining)
|
||||||
|
reds.update(remaining[:self.rcnt - len(reds)])
|
||||||
|
|
||||||
|
reds = sorted(reds)
|
||||||
|
|
||||||
|
top_b = sorted_b[:8]
|
||||||
|
weights_b = [max(b_comp[n], 0) for n in top_b]
|
||||||
|
total_bw = sum(weights_b) or 1
|
||||||
|
probs_b = [w / total_bw for w in weights_b]
|
||||||
|
if self.bcnt > 1:
|
||||||
|
blues = list(rng.choices(top_b, weights=probs_b, k=self.bcnt))
|
||||||
|
# 确保后区号码不重复
|
||||||
|
while len(set(blues)) < len(blues):
|
||||||
|
for j in range(len(blues)):
|
||||||
|
if blues.count(blues[j]) > 1:
|
||||||
|
remaining = [n for n in range(1, self.bmax + 1) if n not in blues[:j] + blues[j+1:]]
|
||||||
|
blues[j] = rng.choice(remaining)
|
||||||
|
else:
|
||||||
|
blues = list(rng.choices(top_b, weights=probs_b, k=self.bcnt))
|
||||||
|
|
||||||
|
score = sum(r_comp.get(n, 0) for n in reds) / self.rcnt
|
||||||
|
score += sum(b_comp.get(n, 0) for n in blues) / self.bcnt
|
||||||
|
score /= 2
|
||||||
|
|
||||||
|
oe = sum(1 for n in reds if n % 2 == 1)
|
||||||
|
bs = sum(1 for n in reds if n > self.mid)
|
||||||
|
total_sum = sum(reds)
|
||||||
|
|
||||||
|
bonus = 0
|
||||||
|
if oe == target_oe[0]:
|
||||||
|
bonus += 0.05
|
||||||
|
if bs == target_bs[0]:
|
||||||
|
bonus += 0.05
|
||||||
|
if sum_min <= total_sum <= sum_max:
|
||||||
|
bonus += 0.05
|
||||||
|
|
||||||
|
results.append({
|
||||||
|
"reds": reds, "blues": blues,
|
||||||
|
"score": round(score + bonus, 4),
|
||||||
|
"oe": f"{oe}:{self.rcnt - oe}",
|
||||||
|
"bs": f"{bs}:{self.rcnt - bs}",
|
||||||
|
"sum": total_sum,
|
||||||
|
})
|
||||||
|
|
||||||
|
seen = set()
|
||||||
|
unique = []
|
||||||
|
for r in sorted(results, key=lambda x: x["score"], reverse=True):
|
||||||
|
key = tuple(r["reds"]) + tuple(r["blues"])
|
||||||
|
if key not in seen:
|
||||||
|
seen.add(key)
|
||||||
|
unique.append(r)
|
||||||
|
|
||||||
|
return unique[:count]
|
||||||
|
|
||||||
|
|
||||||
|
# ============================================================
|
||||||
|
# AI 逐期回滚推演引擎
|
||||||
|
# ============================================================
|
||||||
|
|
||||||
|
class AIRollbackEngine:
|
||||||
|
"""
|
||||||
|
AI逐期回滚推演：
|
||||||
|
从第200期开始 -> 使用当时可用的历史数据进行分析 -> 生成推荐
|
||||||
|
-> 与实开号码比对 -> 记录命中率 -> 自动调整权重
|
||||||
|
-> 进入下一期... 直至最新一期
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, lt, data, initial_weights=None):
|
||||||
|
self.lt = lt
|
||||||
|
self.rule = LOTTERY_RULES[lt]
|
||||||
|
# 标准化所有记录（兼容 blue/blues 混用）
|
||||||
|
normed = [_normalize_rec(d) for d in data]
|
||||||
|
self.all_data = sorted(normed, key=lambda x: int(x["issue"])) # 从旧到新
|
||||||
|
self.weights = initial_weights if initial_weights else copy.deepcopy(DEFAULT_WEIGHTS)
|
||||||
|
self.rollback_history = []
|
||||||
|
self.methodology_log = []
|
||||||
|
|
||||||
|
def run(self):
|
||||||
|
"""执行完整回滚推演：从第200期开始逐期推演至最新一期"""
|
||||||
|
total_periods = len(self.all_data)
|
||||||
|
if total_periods < 200:
|
||||||
|
return {"error": f"数据不足200期，当前仅{total_periods}期", "available": total_periods}
|
||||||
|
|
||||||
|
start_idx = 199 # 第200期（0-based索引）
|
||||||
|
total_simulations = 0
|
||||||
|
total_red_hits = 0
|
||||||
|
total_blue_hits = 0
|
||||||
|
possible_red = 0
|
||||||
|
possible_blue = 0
|
||||||
|
methodology_snapshots = []
|
||||||
|
|
||||||
|
print(f"{'='*60}")
|
||||||
|
print(f" AI逐期回滚推演引擎 - {self.rule['name']}")
|
||||||
|
print(f" 数据总量: {total_periods}期 | 推演范围: 第200期 ~ 第{total_periods}期")
|
||||||
|
print(f" 推演总轮次: {total_periods - start_idx} 期")
|
||||||
|
print(f"{'='*60}")
|
||||||
|
|
||||||
|
for i in range(start_idx, total_periods):
|
||||||
|
current_issue = self.all_data[i]["issue"]
|
||||||
|
historical = self.all_data[:i]
|
||||||
|
|
||||||
|
actual = self.all_data[i]
|
||||||
|
actual_reds = [int(x) for x in actual["reds"]]
|
||||||
|
actual_blues = [int(x) for x in _get_blues(actual)]
|
||||||
|
|
||||||
|
analyzer = EnhancedAnalyzer(historical, self.lt, self.weights)
|
||||||
|
analysis = analyzer.analyze()
|
||||||
|
if not analysis:
|
||||||
|
continue
|
||||||
|
|
||||||
|
recommendations = analyzer.recommend(analysis, count=5)
|
||||||
|
if not recommendations:
|
||||||
|
continue
|
||||||
|
|
||||||
|
best_pick = recommendations[0]
|
||||||
|
pred_reds = best_pick["reds"]
|
||||||
|
pred_blues = best_pick["blues"]
|
||||||
|
|
||||||
|
red_hit = len(set(pred_reds) & set(actual_reds))
|
||||||
|
blue_hit = len(set(pred_blues) & set(actual_blues))
|
||||||
|
|
||||||
|
total_red_hits += red_hit
|
||||||
|
total_blue_hits += blue_hit
|
||||||
|
possible_red += self.rule["red_count"]
|
||||||
|
possible_blue += self.rule["blue_count"]
|
||||||
|
total_simulations += 1
|
||||||
|
|
||||||
|
self.rollback_history.append({
|
||||||
|
"issue": current_issue,
|
||||||
|
"iteration": total_simulations,
|
||||||
|
"pred_reds": pred_reds,
|
||||||
|
"pred_blues": pred_blues,
|
||||||
|
"actual_reds": actual_reds,
|
||||||
|
"actual_blues": actual_blues,
|
||||||
|
"red_hit": red_hit,
|
||||||
|
"blue_hit": blue_hit,
|
||||||
|
"score": best_pick["score"],
|
||||||
|
"weights_at_time": copy.deepcopy(self.weights),
|
||||||
|
})
|
||||||
|
|
||||||
|
self._optimize_weights(red_hit, blue_hit, pred_reds, actual_reds)
|
||||||
|
|
||||||
|
if total_simulations % 5 == 0:
|
||||||
|
red_acc = total_red_hits / possible_red * 100 if possible_red else 0
|
||||||
|
blue_acc = total_blue_hits / possible_blue * 100 if possible_blue else 0
|
||||||
|
methodology_snapshots.append({
|
||||||
|
"iteration": total_simulations,
|
||||||
|
"issue": current_issue,
|
||||||
|
"weights": copy.deepcopy(self.weights),
|
||||||
|
"red_accuracy": round(red_acc, 2),
|
||||||
|
"blue_accuracy": round(blue_acc, 2),
|
||||||
|
})
|
||||||
|
|
||||||
|
if total_simulations % 10 == 0 or total_simulations == 1:
|
||||||
|
red_acc = total_red_hits / possible_red * 100 if possible_red else 0
|
||||||
|
blue_acc = total_blue_hits / possible_blue * 100 if possible_blue else 0
|
||||||
|
overall_acc = (total_red_hits + total_blue_hits) / (possible_red + possible_blue) * 100
|
||||||
|
print(f" [推演 {total_simulations:3d}] 期{current_issue} "
|
||||||
|
f"红{red_hit}/{self.rule['red_count']} 蓝{blue_hit}/{self.rule['blue_count']} "
|
||||||
|
f"| 累计 红{red_acc:.1f}% 蓝{blue_acc:.1f}% 综合{overall_acc:.1f}%")
|
||||||
|
|
||||||
|
# 最终统计
|
||||||
|
avg_red = total_red_hits / possible_red * 100 if possible_red else 0
|
||||||
|
avg_blue = total_blue_hits / possible_blue * 100 if possible_blue else 0
|
||||||
|
overall_hits = total_red_hits + total_blue_hits
|
||||||
|
overall_possible = possible_red + possible_blue
|
||||||
|
overall_acc = overall_hits / overall_possible * 100 if overall_possible else 0
|
||||||
|
win_probability = overall_acc
|
||||||
|
|
||||||
|
hit_dist = {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0}
|
||||||
|
for entry in self.rollback_history:
|
||||||
|
total_hit = entry["red_hit"] + entry["blue_hit"]
|
||||||
|
if total_hit in hit_dist:
|
||||||
|
hit_dist[total_hit] += 1
|
||||||
|
|
||||||
|
result = {
|
||||||
|
"lottery": self.lt,
|
||||||
|
"total_simulations": total_simulations,
|
||||||
|
"start_issue": self.all_data[start_idx]["issue"],
|
||||||
|
"end_issue": self.all_data[-1]["issue"],
|
||||||
|
"final_weights": self.weights,
|
||||||
|
"accuracy": {
|
||||||
|
"red_accuracy": round(avg_red, 2),
|
||||||
|
"blue_accuracy": round(avg_blue, 2),
|
||||||
|
"overall_accuracy": round(overall_acc, 2),
|
||||||
|
"win_probability": round(win_probability, 2),
|
||||||
|
},
|
||||||
|
"hits": {
|
||||||
|
"total_red_hits": total_red_hits,
|
||||||
|
"total_blue_hits": total_blue_hits,
|
||||||
|
"possible_red": possible_red,
|
||||||
|
"possible_blue": possible_blue,
|
||||||
|
},
|
||||||
|
"hit_distribution": hit_dist,
|
||||||
|
"methodology_snapshots": methodology_snapshots,
|
||||||
|
"weight_evolution": self._get_weight_evolution(),
|
||||||
|
}
|
||||||
|
return result
|
||||||
|
|
||||||
|
def _optimize_weights(self, red_hit, blue_hit, pred_reds, actual_reds):
|
||||||
|
"""根据推演结果动态调整10维度权重"""
|
||||||
|
iteration = len(self.rollback_history) + 1
|
||||||
|
lr = max(0.003, 0.01 - iteration * 0.0001)
|
||||||
|
|
||||||
|
hit_ratio = (red_hit + blue_hit) / (self.rule["red_count"] + self.rule["blue_count"])
|
||||||
|
|
||||||
|
if hit_ratio >= 0.5:
|
||||||
|
self.weights["frequency"] = min(0.35, self.weights["frequency"] * (1 + lr))
|
||||||
|
self.weights["hot_number"] = min(0.30, self.weights["hot_number"] * (1 + lr * 0.8))
|
||||||
|
else:
|
||||||
|
self.weights["missing"] = min(0.30, self.weights["missing"] * (1 + lr))
|
||||||
|
self.weights["probability"] = min(0.15, self.weights["probability"] * (1 + lr * 1.2))
|
||||||
|
self.weights["frequency"] = max(0.15, self.weights["frequency"] * (1 - lr * 0.5))
|
||||||
|
|
||||||
|
pred_oe = sum(1 for n in pred_reds if n % 2 == 1)
|
||||||
|
actual_oe = sum(1 for n in actual_reds if n % 2 == 1)
|
||||||
|
if pred_oe == actual_oe:
|
||||||
|
self.weights["odd_even"] = min(0.12, self.weights["odd_even"] * (1 + lr))
|
||||||
|
|
||||||
|
pred_sum = sum(pred_reds)
|
||||||
|
actual_sum = sum(actual_reds)
|
||||||
|
if abs(pred_sum - actual_sum) <= 15:
|
||||||
|
self.weights["sum_value"] = min(0.10, self.weights["sum_value"] * (1 + lr))
|
||||||
|
|
||||||
|
pred_consec = sum(1 for i in range(len(pred_reds) - 1) if pred_reds[i + 1] - pred_reds[i] == 1)
|
||||||
|
actual_consec = sum(1 for i in range(len(actual_reds) - 1) if actual_reds[i + 1] - actual_reds[i] == 1)
|
||||||
|
if pred_consec == actual_consec:
|
||||||
|
self.weights["consecutive"] = min(0.08, self.weights["consecutive"] * (1 + lr))
|
||||||
|
|
||||||
|
total = sum(self.weights.values())
|
||||||
|
if total > 0:
|
||||||
|
for k in self.weights:
|
||||||
|
self.weights[k] /= total
|
||||||
|
|
||||||
|
def _get_weight_evolution(self):
|
||||||
|
if not self.rollback_history:
|
||||||
|
return []
|
||||||
|
interval = max(1, len(self.rollback_history) // 20)
|
||||||
|
evolution = []
|
||||||
|
for i, entry in enumerate(self.rollback_history):
|
||||||
|
if i % interval == 0 or i == len(self.rollback_history) - 1:
|
||||||
|
evolution.append({
|
||||||
|
"iteration": entry["iteration"],
|
||||||
|
"issue": entry["issue"],
|
||||||
|
"weights": entry["weights_at_time"],
|
||||||
|
})
|
||||||
|
return evolution
|
||||||
|
|
||||||
|
|
||||||
|
# ============================================================
|
||||||
|
# 自进化系统（多轮迭代 + 方法论总结升级）
|
||||||
|
# ============================================================
|
||||||
|
|
||||||
|
class SelfEvolvingSystem:
|
||||||
|
"""多轮迭代运行，每轮完成后总结方法论并升级，目标胜出率 >= 51%"""
|
||||||
|
|
||||||
|
def __init__(self, lt, data):
|
||||||
|
self.lt = lt
|
||||||
|
self.data = data
|
||||||
|
self.rule = LOTTERY_RULES[lt]
|
||||||
|
self.best_weights = copy.deepcopy(DEFAULT_WEIGHTS)
|
||||||
|
self.best_accuracy = 0
|
||||||
|
self.iteration_results = []
|
||||||
|
self.methodology_log = []
|
||||||
|
|
||||||
|
def run_iterations(self, max_iterations=20):
|
||||||
|
print(f"\n{'='*60}")
|
||||||
|
print(f" 🔬 自进化系统启动 - {self.rule['name']}")
|
||||||
|
print(f" 目标：胜出率(概率占比) >= 51%")
|
||||||
|
print(f" 最大迭代轮次: {max_iterations}")
|
||||||
|
print(f"{'='*60}")
|
||||||
|
|
||||||
|
for iteration in range(1, max_iterations + 1):
|
||||||
|
print(f"\n{'─'*60}")
|
||||||
|
print(f" 第 {iteration}/{max_iterations} 轮迭代")
|
||||||
|
print(f"{'─'*60}")
|
||||||
|
|
||||||
|
if iteration == 1:
|
||||||
|
current_weights = copy.deepcopy(DEFAULT_WEIGHTS)
|
||||||
|
else:
|
||||||
|
current_weights = self._mutate_weights(self.best_weights, iteration)
|
||||||
|
|
||||||
|
engine = AIRollbackEngine(self.lt, self.data, current_weights)
|
||||||
|
result = engine.run()
|
||||||
|
|
||||||
|
if "error" in result:
|
||||||
|
print(f" ❌ 错误: {result['error']}")
|
||||||
|
break
|
||||||
|
|
||||||
|
accuracy = result["accuracy"]["win_probability"]
|
||||||
|
self.iteration_results.append({
|
||||||
|
"iteration": iteration,
|
||||||
|
"accuracy": accuracy,
|
||||||
|
"weights": result["final_weights"],
|
||||||
|
"simulations": result["total_simulations"],
|
||||||
|
})
|
||||||
|
|
||||||
|
self._summarize_methodology(iteration, result)
|
||||||
|
|
||||||
|
if accuracy > self.best_accuracy:
|
||||||
|
self.best_accuracy = accuracy
|
||||||
|
self.best_weights = result["final_weights"]
|
||||||
|
print(f" 🏆 新最佳胜出率: {accuracy:.2f}%")
|
||||||
|
else:
|
||||||
|
print(f" 本轮胜出率: {accuracy:.2f}% (最佳: {self.best_accuracy:.2f}%)")
|
||||||
|
|
||||||
|
if self.best_accuracy >= 51.0:
|
||||||
|
print(f"\n{'='*60}")
|
||||||
|
print(f" 🎯 目标达成！胜出率 {self.best_accuracy:.2f}% >= 51%")
|
||||||
|
print(f"{'='*60}")
|
||||||
|
break
|
||||||
|
|
||||||
|
return self._final_report()
|
||||||
|
|
||||||
|
def _mutate_weights(self, base_weights, iteration):
|
||||||
|
mutated = copy.deepcopy(base_weights)
|
||||||
|
mutation_rate = max(0.005, 0.03 - iteration * 0.001)
|
||||||
|
for k in mutated:
|
||||||
|
delta = random.uniform(-mutation_rate, mutation_rate)
|
||||||
|
mutated[k] = max(0.01, mutated[k] + delta)
|
||||||
|
total = sum(mutated.values())
|
||||||
|
for k in mutated:
|
||||||
|
mutated[k] /= total
|
||||||
|
return mutated
|
||||||
|
|
||||||
|
def _summarize_methodology(self, iteration, result):
|
||||||
|
acc = result["accuracy"]
|
||||||
|
weights = result["final_weights"]
|
||||||
|
dist = result.get("hit_distribution", {})
|
||||||
|
|
||||||
|
sorted_w = sorted(weights.items(), key=lambda x: x[1], reverse=True)
|
||||||
|
top_dims = sorted_w[:3]
|
||||||
|
weak_dims = sorted_w[-2:]
|
||||||
|
|
||||||
|
summary_lines = [
|
||||||
|
f"\n === 方法论总结 - 第{iteration}轮 ===",
|
||||||
|
f" 胜出率: {acc['win_probability']:.2f}% (红球{acc['red_accuracy']:.1f}% 蓝球{acc['blue_accuracy']:.1f}%)",
|
||||||
|
f" 最优三维度:",
|
||||||
|
]
|
||||||
|
for dim, w in top_dims:
|
||||||
|
summary_lines.append(f" - {dim}: {w:.4f}")
|
||||||
|
summary_lines.append(f" 最弱二维度:")
|
||||||
|
for dim, w in weak_dims:
|
||||||
|
summary_lines.append(f" - {dim}: {w:.4f}")
|
||||||
|
summary_lines.append(f" 命中分布:")
|
||||||
|
for level in sorted(dist.keys()):
|
||||||
|
if dist.get(level, 0) > 0:
|
||||||
|
pct = dist[level] / result["total_simulations"] * 100
|
||||||
|
summary_lines.append(f" - {level}个命中: {dist[level]}次 ({pct:.1f}%)")
|
||||||
|
|
||||||
|
summary = "\n".join(summary_lines)
|
||||||
|
print(summary)
|
||||||
|
self.methodology_log.append(summary)
|
||||||
|
|
||||||
|
mpath = os.path.join(DATA_DIR, f"methodology_{self.lt}_iter{iteration}.json")
|
||||||
|
with open(mpath, "w", encoding="utf-8") as f:
|
||||||
|
json.dump({
|
||||||
|
"iteration": iteration,
|
||||||
|
"accuracy": acc,
|
||||||
|
"weights": weights,
|
||||||
|
"hit_distribution": dist,
|
||||||
|
"snapshots": result.get("methodology_snapshots", []),
|
||||||
|
}, f, ensure_ascii=False, indent=2)
|
||||||
|
print(f" [方法论已保存] {mpath}")
|
||||||
|
|
||||||
|
def _final_report(self):
|
||||||
|
report = {
|
||||||
|
"lottery": self.lt,
|
||||||
|
"total_iterations": len(self.iteration_results),
|
||||||
|
"best_accuracy": self.best_accuracy,
|
||||||
|
"best_weights": self.best_weights,
|
||||||
|
"iteration_summary": self.iteration_results,
|
||||||
|
}
|
||||||
|
rpath = os.path.join(DATA_DIR, f"self_evolve_{self.lt}_final.json")
|
||||||
|
with open(rpath, "w", encoding="utf-8") as f:
|
||||||
|
json.dump(report, f, ensure_ascii=False, indent=2)
|
||||||
|
return report
|
||||||
|
|
||||||
|
|
||||||
|
# ============================================================
|
||||||
|
# CLI 主入口
|
||||||
|
# ============================================================
|
||||||
|
|
||||||
|
def load_history(lt):
|
||||||
|
path = os.path.join(DATA_DIR, f"{lt}_history.json")
|
||||||
|
if os.path.exists(path):
|
||||||
|
try:
|
||||||
|
with open(path, "r", encoding="utf-8") as f:
|
||||||
|
return json.load(f)
|
||||||
|
except:
|
||||||
|
pass
|
||||||
|
return []
|
||||||
|
|
||||||
|
|
||||||
|
def print_header():
|
||||||
|
print("\n" + "*" * 60)
|
||||||
|
print(" 双色球 & 大乐透 * 增强智能分析系统 v2.0")
|
||||||
|
print(" AI回滚推演 | 方法论自优化 | 200期深度分析")
|
||||||
|
print(" " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M"))
|
||||||
|
print("*" * 60)
|
||||||
|
|
||||||
|
|
||||||
|
def show_full_report(lt, count=5):
|
||||||
|
rule = LOTTERY_RULES[lt]
|
||||||
|
data = load_history(lt)
|
||||||
|
if not data:
|
||||||
|
print(f"\n ⚠️ {rule['name']}暂无数据，请先采集")
|
||||||
|
print(f" python3 lottery_tool.py --action fetch --lottery {lt}")
|
||||||
|
return
|
||||||
|
|
||||||
|
analyzer = EnhancedAnalyzer(data, lt)
|
||||||
|
analysis = analyzer.analyze()
|
||||||
|
if not analysis:
|
||||||
|
print(f"\n ⚠️ {rule['name']}分析失败")
|
||||||
|
return
|
||||||
|
|
||||||
|
print(f"\n{'─'*60}")
|
||||||
|
print(f" {rule['name']} - 增强分析报告 (200期)")
|
||||||
|
print(f"{'─'*60}")
|
||||||
|
dr = analysis["data_range"]
|
||||||
|
print(f" 数据范围: {dr['total']}期 ({dr['start']} ~ {dr['end']})")
|
||||||
|
w = analyzer.weights
|
||||||
|
print(f" 权重体系: freq={w['frequency']:.3f} hot={w['hot_number']:.3f} miss={w['missing']:.3f}")
|
||||||
|
|
||||||
|
pat = analysis["patterns"]
|
||||||
|
print(f"\n 模式分析:")
|
||||||
|
print(f" 奇偶比: {pat['most_oe']} | 大小比: {pat['most_bs']}")
|
||||||
|
print(f" 和值范围: {pat['sum_range'][0]} ~ {pat['sum_range'][1]} (均{pat['avg_sum']})")
|
||||||
|
print(f" 区间分布: {pat['zone_balance']}")
|
||||||
|
|
||||||
|
recs = analyzer.recommend(analysis, count=count)
|
||||||
|
print(f"\n 🎯 AI推荐号码 ({count}组):")
|
||||||
|
for i, rec in enumerate(recs, 1):
|
||||||
|
rs = " ".join(f"{n:02d}" for n in rec["reds"])
|
||||||
|
bs = " ".join(f"{n:02d}" for n in rec["blues"])
|
||||||
|
print(f" [{i}] 红球: {rs} 蓝球: {bs} (评分:{rec['score']:.3f} OE:{rec['oe']} BS:{rec['bs']})")
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
import argparse
|
||||||
|
parser = argparse.ArgumentParser(description="增强版彩票智能分析系统 v2.0")
|
||||||
|
parser.add_argument("--action",
|
||||||
|
choices=["report", "rollback", "self_evolve"],
|
||||||
|
default="report",
|
||||||
|
help="report: 200期分析推荐 | rollback: 逐期回滚推演 | self_evolve: 自进化")
|
||||||
|
parser.add_argument("--lottery", choices=["ssq", "dlt", "all"], default="ssq")
|
||||||
|
parser.add_argument("--count", type=int, default=5, help="推荐组数")
|
||||||
|
parser.add_argument("--iterations", type=int, default=10, help="自进化迭代轮次")
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
print_header()
|
||||||
|
lotteries = ["ssq", "dlt"] if args.lottery == "all" else [args.lottery]
|
||||||
|
|
||||||
|
if args.action == "report":
|
||||||
|
for lt in lotteries:
|
||||||
|
show_full_report(lt, args.count)
|
||||||
|
return
|
||||||
|
|
||||||
|
if args.action == "rollback":
|
||||||
|
for lt in lotteries:
|
||||||
|
data = load_history(lt)
|
||||||
|
if len(data) < 200:
|
||||||
|
print(f"\n ⚠️ {LOTTERY_RULES[lt]['name']}数据不足200期 (当前{len(data)}期)")
|
||||||
|
print(f" 请先采集: python3 lottery_tool.py --action fetch --lottery {lt}")
|
||||||
|
continue
|
||||||
|
engine = AIRollbackEngine(lt, data)
|
||||||
|
result = engine.run()
|
||||||
|
if "error" not in result:
|
||||||
|
acc = result["accuracy"]
|
||||||
|
print(f"\n === 回滚推演完成 ===")
|
||||||
|
print(f" 综合胜出率: {acc['win_probability']:.2f}%")
|
||||||
|
print(f" 红球准确率: {acc['red_accuracy']:.1f}% | 蓝球: {acc['blue_accuracy']:.1f}%")
|
||||||
|
|
||||||
|
if args.action == "self_evolve":
|
||||||
|
for lt in lotteries:
|
||||||
|
data = load_history(lt)
|
||||||
|
if len(data) < 200:
|
||||||
|
print(f"\n ⚠️ {LOTTERY_RULES[lt]['name']}数据不足200期 (当前{len(data)}期)")
|
||||||
|
continue
|
||||||
|
system = SelfEvolvingSystem(lt, data)
|
||||||
|
final = system.run_iterations(max_iterations=args.iterations)
|
||||||
|
print(f"\n === 自进化最终结果 ===")
|
||||||
|
print(f" 最佳胜出率: {final['best_accuracy']:.2f}%")
|
||||||
|
print(f" 迭代总轮次: {final['total_iterations']}")
|
||||||
|
print(f" 最优权重:")
|
||||||
|
for k, v in sorted(final['best_weights'].items(), key=lambda x: x[1], reverse=True):
|
||||||
|
print(f" {k}: {v:.4f}")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
@@ -0,0 +1,725 @@
|
|||||||
|
#!/usr/bin/env python
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
大乐透/双色球 集成学习模块
|
||||||
|
方案3：多模型集成 + 权重优化训练
|
||||||
|
==================================
|
||||||
|
包含5种分析模型：
|
||||||
|
1. 频率加权模型 (Frequency Model)
|
||||||
|
2. 贝叶斯平滑模型 (Bayesian Smoothing Model)
|
||||||
|
3. 马尔可夫链模型 (Markov Chain Model)
|
||||||
|
4. K-Means聚类模型 (K-Means Clustering Model)
|
||||||
|
5. 残差回归模型 (Residual Regression Model)
|
||||||
|
|
||||||
|
通过历史数据回测学习各模型的最优权重
|
||||||
|
"""
|
||||||
|
|
||||||
|
import json, math, random, hashlib
|
||||||
|
from collections import Counter, defaultdict
|
||||||
|
|
||||||
|
# ==================== 基础工具函数 ====================
|
||||||
|
|
||||||
|
def sigmoid(x):
|
||||||
|
"""Sigmoid激活函数"""
|
||||||
|
return 1 / (1 + math.exp(-max(min(x, 500), -500)))
|
||||||
|
|
||||||
|
def normalize(scores):
|
||||||
|
"""归一化分数，使总和为1"""
|
||||||
|
total = sum(scores.values())
|
||||||
|
if total == 0:
|
||||||
|
return {k: 1.0/len(scores) for k in scores}
|
||||||
|
return {k: v/total for k, v in scores.items()}
|
||||||
|
|
||||||
|
def weighted_choice(weights, count=1):
|
||||||
|
"""从权重分布中无放回选择指定数量的元素"""
|
||||||
|
items = list(weights.keys())
|
||||||
|
if len(items) == 0:
|
||||||
|
return []
|
||||||
|
if count >= len(items):
|
||||||
|
return items[:]
|
||||||
|
result = []
|
||||||
|
remaining = items[:]
|
||||||
|
remaining_weights = weights.copy()
|
||||||
|
for _ in range(count):
|
||||||
|
total = sum(remaining_weights.values())
|
||||||
|
if total <= 0:
|
||||||
|
choice = random.choice(remaining)
|
||||||
|
else:
|
||||||
|
r = random.random() * total
|
||||||
|
cum = 0
|
||||||
|
for item in remaining:
|
||||||
|
cum += remaining_weights[item]
|
||||||
|
if r <= cum:
|
||||||
|
choice = item
|
||||||
|
break
|
||||||
|
result.append(choice)
|
||||||
|
remaining.remove(choice)
|
||||||
|
del remaining_weights[choice]
|
||||||
|
return result
|
||||||
|
|
||||||
|
# ==================== 模型1：频率加权模型 ====================
|
||||||
|
|
||||||
|
class FrequencyModel:
|
||||||
|
"""基于历史频率的加权模型"""
|
||||||
|
def __init__(self, lt, data, rule):
|
||||||
|
self.lt = lt
|
||||||
|
self.data = data
|
||||||
|
self.rule = rule
|
||||||
|
self.rcnt = rule["red_count"]
|
||||||
|
self.rmax = rule["red_max"]
|
||||||
|
self.bcnt = rule["blue_count"]
|
||||||
|
self.bmax = rule["blue_max"]
|
||||||
|
self.red_score = {}
|
||||||
|
self.blue_score = {}
|
||||||
|
self.analyze()
|
||||||
|
|
||||||
|
def analyze(self):
|
||||||
|
rule, rmax, bmax, rcnt, bcnt = self.rule, self.rule["red_max"], self.rule["blue_max"], self.rule["red_count"], self.rule["blue_count"]
|
||||||
|
all_r, all_b = [], []
|
||||||
|
for rec in self.data:
|
||||||
|
all_r.extend(int(x) for x in rec["reds"])
|
||||||
|
bl = rec["blue"]
|
||||||
|
# 修复：蓝球处理 - 支持整数和字符串两种格式
|
||||||
|
if isinstance(bl, str):
|
||||||
|
all_b.append(int(bl))
|
||||||
|
elif isinstance(bl, list):
|
||||||
|
all_b.extend(bl)
|
||||||
|
else:
|
||||||
|
all_b.append(bl)
|
||||||
|
|
||||||
|
# 频率统计
|
||||||
|
rf = Counter(all_r)
|
||||||
|
bf = Counter(all_b)
|
||||||
|
|
||||||
|
max_rf = max(rf.values()) if rf else 1
|
||||||
|
max_bf = max(bf.values()) if bf else 1
|
||||||
|
|
||||||
|
# 归一化
|
||||||
|
self.red_score = {i: rf.get(i, 0) / max_rf for i in range(1, rmax + 1)}
|
||||||
|
self.blue_score = {i: bf.get(i, 0) / max_bf for i in range(1, bmax + 1)}
|
||||||
|
|
||||||
|
# 平滑：避免0分
|
||||||
|
for i in range(1, rmax + 1):
|
||||||
|
if self.red_score[i] == 0:
|
||||||
|
self.red_score[i] = 0.01
|
||||||
|
for i in range(1, bmax + 1):
|
||||||
|
if self.blue_score[i] == 0:
|
||||||
|
self.blue_score[i] = 0.01
|
||||||
|
|
||||||
|
# 归一化到[0,1]
|
||||||
|
max_r = max(self.red_score.values())
|
||||||
|
max_b = max(self.blue_score.values())
|
||||||
|
if max_r > 0:
|
||||||
|
for i in self.red_score:
|
||||||
|
self.red_score[i] /= max_r
|
||||||
|
if max_b > 0:
|
||||||
|
for i in self.blue_score:
|
||||||
|
self.blue_score[i] /= max_b
|
||||||
|
|
||||||
|
|
||||||
|
# ==================== 模型2：贝叶斯平滑模型 ====================
|
||||||
|
|
||||||
|
class BayesianModel:
|
||||||
|
"""贝叶斯先验 + 似然平滑模型"""
|
||||||
|
def __init__(self, lt, data, rule):
|
||||||
|
self.lt = lt
|
||||||
|
self.data = data
|
||||||
|
self.rule = rule
|
||||||
|
self.rcnt = rule["red_count"]
|
||||||
|
self.rmax = rule["red_max"]
|
||||||
|
self.bcnt = rule["blue_count"]
|
||||||
|
self.bmax = rule["blue_max"]
|
||||||
|
self.prior = {i: 1.0 / rule["red_max"] for i in range(1, rule["red_max"] + 1)}
|
||||||
|
self.red_score = {}
|
||||||
|
self.blue_score = {}
|
||||||
|
self.analyze()
|
||||||
|
|
||||||
|
def analyze(self):
|
||||||
|
rule, rmax, bmax, rcnt, bcnt = self.rule, self.rule["red_max"], self.rule["blue_max"], self.rule["red_count"], self.rule["blue_count"]
|
||||||
|
all_r, all_b = [], []
|
||||||
|
for rec in self.data:
|
||||||
|
all_r.extend(int(x) for x in rec["reds"])
|
||||||
|
bl = rec["blue"]
|
||||||
|
# 修复：蓝球处理 - 支持整数和字符串两种格式
|
||||||
|
if isinstance(bl, str):
|
||||||
|
all_b.append(int(bl))
|
||||||
|
elif isinstance(bl, list):
|
||||||
|
all_b.extend(bl)
|
||||||
|
else:
|
||||||
|
all_b.append(bl)
|
||||||
|
|
||||||
|
# 似然
|
||||||
|
rf = Counter(all_r)
|
||||||
|
bf = Counter(all_b)
|
||||||
|
|
||||||
|
# 贝叶斯后验：后验 ∝ 先验 × 似然
|
||||||
|
# 使用伪计数：先验+1，似然+1，避免0
|
||||||
|
alpha = 1 # 先验伪计数
|
||||||
|
beta = 1 # 似然伪计数
|
||||||
|
total_alpha = rmax * alpha
|
||||||
|
total_beta = len(all_r) + beta * rmax
|
||||||
|
|
||||||
|
self.red_score = {}
|
||||||
|
for i in range(1, rmax + 1):
|
||||||
|
# 后验均值 = (α + count) / (α + β * rmax)
|
||||||
|
posterior = (alpha + rf.get(i, 0)) / (total_alpha + beta)
|
||||||
|
self.red_score[i] = posterior
|
||||||
|
|
||||||
|
total_blue = len(all_b) + beta * bmax
|
||||||
|
self.blue_score = {}
|
||||||
|
for i in range(1, bmax + 1):
|
||||||
|
posterior = (alpha + bf.get(i, 0)) / (total_alpha + beta)
|
||||||
|
self.blue_score[i] = posterior
|
||||||
|
|
||||||
|
# 归一化
|
||||||
|
max_r = max(self.red_score.values())
|
||||||
|
max_b = max(self.blue_score.values())
|
||||||
|
if max_r > 0:
|
||||||
|
for i in self.red_score:
|
||||||
|
self.red_score[i] /= max_r
|
||||||
|
if max_b > 0:
|
||||||
|
for i in self.blue_score:
|
||||||
|
self.blue_score[i] /= max_b
|
||||||
|
|
||||||
|
|
||||||
|
# ==================== 模型3：马尔可夫链模型 ====================
|
||||||
|
|
||||||
|
class MarkovModel:
|
||||||
|
"""基于马尔可夫链的状态转移模型"""
|
||||||
|
def __init__(self, lt, data, rule):
|
||||||
|
self.lt = lt
|
||||||
|
self.data = data
|
||||||
|
self.rule = rule
|
||||||
|
self.rcnt = rule["red_count"]
|
||||||
|
self.rmax = rule["red_max"]
|
||||||
|
self.bcnt = rule["blue_count"]
|
||||||
|
self.bmax = rule["blue_max"]
|
||||||
|
self.red_score = {}
|
||||||
|
self.blue_score = {}
|
||||||
|
self.analyze()
|
||||||
|
|
||||||
|
def analyze(self):
|
||||||
|
rule, rmax, bmax, rcnt, bcnt = self.rule, self.rule["red_max"], self.rule["blue_max"], self.rule["red_count"], self.rule["blue_count"]
|
||||||
|
|
||||||
|
# 构建状态转移矩阵（考虑最近5期作为状态）
|
||||||
|
# 这里简化为：计算每个号码在历史数据中的"惯性"
|
||||||
|
# 即：一个号码出现后，后续几期再次出现的概率
|
||||||
|
|
||||||
|
# 使用frozenset作为不可变集合作为字典键
|
||||||
|
transition = defaultdict(lambda: defaultdict(int))
|
||||||
|
for i in range(1, len(self.data)):
|
||||||
|
prev = frozenset(int(x) for x in self.data[i-1]["reds"])
|
||||||
|
curr = set(int(x) for x in self.data[i]["reds"])
|
||||||
|
for num in curr:
|
||||||
|
transition[prev][num] += 1
|
||||||
|
|
||||||
|
# 计算每个号码的"热度"（结合近期出现频率和转移概率）
|
||||||
|
recent_data = self.data[:20] if len(self.data) >= 20 else self.data
|
||||||
|
recent_reds = []
|
||||||
|
for rec in recent_data:
|
||||||
|
recent_reds.extend(int(x) for x in rec["reds"])
|
||||||
|
|
||||||
|
rf = Counter(recent_reds)
|
||||||
|
total_recent = len(recent_reds)
|
||||||
|
|
||||||
|
# 综合评分：近期频率 + 转移惯性
|
||||||
|
self.red_score = {}
|
||||||
|
for i in range(1, rmax + 1):
|
||||||
|
freq_score = rf.get(i, 0) / total_recent if total_recent > 0 else 0
|
||||||
|
# 惯性得分：如果上一期出现过，给予额外权重
|
||||||
|
last_reds = set(int(x) for x in self.data[0]["reds"]) if self.data else set()
|
||||||
|
inertia = 1.5 if i in last_reds else 1.0
|
||||||
|
self.red_score[i] = (freq_score * inertia + 0.01) # 加0.01平滑
|
||||||
|
|
||||||
|
# 蓝球简化：直接用近期频率
|
||||||
|
recent_blues = []
|
||||||
|
for rec in recent_data:
|
||||||
|
bl = rec["blue"]
|
||||||
|
recent_blues.append(int(bl) if isinstance(bl, str) else (bl[0] if isinstance(bl, list) else bl))
|
||||||
|
|
||||||
|
bf = Counter(recent_blues)
|
||||||
|
total_blue = len(recent_blues)
|
||||||
|
self.blue_score = {}
|
||||||
|
for i in range(1, bmax + 1):
|
||||||
|
self.blue_score[i] = (bf.get(i, 0) / total_blue * 0.8 + 1.0 / bmax * 0.2) if total_blue > 0 else (1.0 / bmax)
|
||||||
|
|
||||||
|
# 归一化
|
||||||
|
max_r = max(self.red_score.values()) if self.red_score else 1
|
||||||
|
max_b = max(self.blue_score.values()) if self.blue_score else 1
|
||||||
|
for i in self.red_score:
|
||||||
|
self.red_score[i] /= max_r
|
||||||
|
for i in self.blue_score:
|
||||||
|
self.blue_score[i] /= max_b
|
||||||
|
|
||||||
|
|
||||||
|
# ==================== 模型4：K-Means聚类模型 ====================
|
||||||
|
|
||||||
|
class KMeansModel:
|
||||||
|
"""基于历史聚类中心的推荐模型"""
|
||||||
|
def __init__(self, lt, data, rule):
|
||||||
|
self.lt = lt
|
||||||
|
self.data = data
|
||||||
|
self.rule = rule
|
||||||
|
self.rcnt = rule["red_count"]
|
||||||
|
self.rmax = rule["red_max"]
|
||||||
|
self.bcnt = rule["blue_count"]
|
||||||
|
self.bmax = rule["blue_max"]
|
||||||
|
self.red_score = {}
|
||||||
|
self.blue_score = {}
|
||||||
|
self.analyze()
|
||||||
|
|
||||||
|
def analyze(self):
|
||||||
|
rule, rmax, bmax, rcnt, bcnt = self.rule, self.rule["red_max"], self.rule["blue_max"], self.rule["red_count"], self.rule["blue_max"]
|
||||||
|
|
||||||
|
# 提取所有历史红球组合
|
||||||
|
clusters = []
|
||||||
|
for rec in self.data:
|
||||||
|
nums = sorted(int(x) for x in rec["reds"])
|
||||||
|
if len(nums) == rcnt:
|
||||||
|
clusters.append(nums)
|
||||||
|
|
||||||
|
if len(clusters) < 3:
|
||||||
|
# 数据不足，退化为频率模型
|
||||||
|
rf = Counter()
|
||||||
|
for nums in clusters:
|
||||||
|
for n in nums:
|
||||||
|
rf[n] += 1
|
||||||
|
max_f = max(rf.values()) if rf else 1
|
||||||
|
self.red_score = {i: rf.get(i, 0) / max_f for i in range(1, rmax + 1)}
|
||||||
|
return
|
||||||
|
|
||||||
|
# 简化版K-Means：使用质心法
|
||||||
|
# 计算每个位置的平均值（排序后）
|
||||||
|
sorted_clusters = [sorted(c) for c in clusters]
|
||||||
|
n_clusters = min(5, len(sorted_clusters))
|
||||||
|
|
||||||
|
# 计算每个位置的聚类中心
|
||||||
|
position_means = [[] for _ in range(rcnt)]
|
||||||
|
for cluster in sorted_clusters:
|
||||||
|
for pos in range(rcnt):
|
||||||
|
position_means[pos].append(cluster[pos])
|
||||||
|
|
||||||
|
# 计算每个号码的"接近中心"得分
|
||||||
|
position_avg = [sum(p) / len(p) for p in position_means if p]
|
||||||
|
|
||||||
|
self.red_score = {}
|
||||||
|
for i in range(1, rmax + 1):
|
||||||
|
# 计算该号码在所有位置上偏离平均值的程度
|
||||||
|
deviations = []
|
||||||
|
for pos, avg in enumerate(position_avg):
|
||||||
|
if len(sorted_clusters) > pos:
|
||||||
|
cluster_at_pos = [c[pos] for c in sorted_clusters if len(c) > pos]
|
||||||
|
if cluster_at_pos:
|
||||||
|
deviations.append(abs(i - avg))
|
||||||
|
avg_dev = sum(deviations) / len(deviations) if deviations else rmax / 2
|
||||||
|
# 得分：偏离越小得分越高（越接近典型组合）
|
||||||
|
score = max(0, 1 - avg_dev / (rmax * 0.5))
|
||||||
|
self.red_score[i] = max(score, 0.01) # 最小0.01
|
||||||
|
|
||||||
|
# 蓝球：直接用频率
|
||||||
|
all_blues = []
|
||||||
|
for rec in self.data:
|
||||||
|
bl = rec["blue"]
|
||||||
|
all_blues.append(int(bl) if isinstance(bl, str) else (bl[0] if isinstance(bl, list) else bl))
|
||||||
|
bf = Counter(all_blues)
|
||||||
|
max_bf = max(bf.values()) if bf else 1
|
||||||
|
self.blue_score = {i: bf.get(i, 0) / max_bf for i in range(1, bmax + 1)}
|
||||||
|
for i in range(1, bmax + 1):
|
||||||
|
if self.blue_score[i] == 0:
|
||||||
|
self.blue_score[i] = 0.01
|
||||||
|
|
||||||
|
# 归一化
|
||||||
|
max_r = max(self.red_score.values()) if self.red_score else 1
|
||||||
|
max_b = max(self.blue_score.values()) if self.blue_score else 1
|
||||||
|
for i in self.red_score:
|
||||||
|
self.red_score[i] /= max_r
|
||||||
|
for i in self.blue_score:
|
||||||
|
self.blue_score[i] /= max_b
|
||||||
|
|
||||||
|
|
||||||
|
# ==================== 模型5：残差回归模型 ====================
|
||||||
|
|
||||||
|
class ResidualModel:
|
||||||
|
"""残差分析 + 回归预测模型"""
|
||||||
|
def __init__(self, lt, data, rule):
|
||||||
|
self.lt = lt
|
||||||
|
self.data = data
|
||||||
|
self.rule = rule
|
||||||
|
self.rcnt = rule["red_count"]
|
||||||
|
self.rmax = rule["red_max"]
|
||||||
|
self.bcnt = rule["blue_count"]
|
||||||
|
self.bmax = rule["blue_max"]
|
||||||
|
self.red_score = {}
|
||||||
|
self.blue_score = {}
|
||||||
|
self.analyze()
|
||||||
|
|
||||||
|
def analyze(self):
|
||||||
|
rule, rmax, bmax, rcnt, bcnt = self.rule, self.rule["red_max"], self.rule["blue_max"], self.rule["red_count"], self.rule["blue_count"]
|
||||||
|
|
||||||
|
# 计算每期的和值、奇偶比、大小比作为特征
|
||||||
|
sums = []
|
||||||
|
for rec in self.data:
|
||||||
|
s = sum(int(x) for x in rec["reds"])
|
||||||
|
sums.append(s)
|
||||||
|
|
||||||
|
if len(sums) < 10:
|
||||||
|
# 数据不足，退化为均匀分布
|
||||||
|
self.red_score = {i: 1.0 / rmax for i in range(1, rmax + 1)}
|
||||||
|
self.blue_score = {i: 1.0 / bmax for i in range(1, bmax + 1)}
|
||||||
|
return
|
||||||
|
|
||||||
|
# 计算和值的移动平均（最近10期）
|
||||||
|
window = min(10, len(sums))
|
||||||
|
recent_sums = sums[-window:]
|
||||||
|
avg_sum = sum(recent_sums) / len(recent_sums)
|
||||||
|
std_sum = math.sqrt(sum((s - avg_sum) ** 2 for s in recent_sums) / len(recent_sums))
|
||||||
|
|
||||||
|
# 计算每个号码在"和值附近"组合中的出现频率
|
||||||
|
# 思路：哪些号码更常出现在和值接近均值的组合中
|
||||||
|
score_by_num = {i: 0.0 for i in range(1, rmax + 1)}
|
||||||
|
count_by_num = {i: 0 for i in range(1, rmax + 1)}
|
||||||
|
|
||||||
|
for rec in self.data:
|
||||||
|
nums = [int(x) for x in rec["reds"]]
|
||||||
|
s = sum(nums)
|
||||||
|
# 计算该组合和值与均值的距离
|
||||||
|
dist = abs(s - avg_sum)
|
||||||
|
if dist <= std_sum * 2: # 落在2个标准差内
|
||||||
|
for n in nums:
|
||||||
|
score_by_num[n] += 1 / (dist + 1) # 距离越近得分越高
|
||||||
|
count_by_num[n] += 1
|
||||||
|
|
||||||
|
# 归一化
|
||||||
|
max_score = max(score_by_num.values()) if max(score_by_num.values()) > 0 else 1
|
||||||
|
self.red_score = {}
|
||||||
|
for i in range(1, rmax + 1):
|
||||||
|
if count_by_num[i] > 0:
|
||||||
|
self.red_score[i] = (score_by_num[i] / count_by_num[i]) / max_score
|
||||||
|
else:
|
||||||
|
self.red_score[i] = 1.0 / rmax # 均匀分布
|
||||||
|
|
||||||
|
# 蓝球：和值相关性分析
|
||||||
|
blues_by_sum = defaultdict(list)
|
||||||
|
for rec in self.data:
|
||||||
|
bl = rec["blue"]
|
||||||
|
bl_val = int(bl) if isinstance(bl, str) else (bl[0] if isinstance(bl, list) else bl)
|
||||||
|
s = sum(int(x) for x in rec["reds"])
|
||||||
|
blues_by_sum[s].append(bl_val)
|
||||||
|
|
||||||
|
self.blue_score = {}
|
||||||
|
for i in range(1, bmax + 1):
|
||||||
|
# 计算该蓝球在"和值附近"出现的频率
|
||||||
|
total = 0
|
||||||
|
for s, blues in blues_by_sum.items():
|
||||||
|
dist = abs(s - avg_sum)
|
||||||
|
if dist <= std_sum * 2:
|
||||||
|
total += len([b for b in blues if b == i]) / (dist + 1)
|
||||||
|
self.blue_score[i] = max(total / 100, 1.0 / bmax)
|
||||||
|
|
||||||
|
# 归一化
|
||||||
|
max_r = max(self.red_score.values()) if self.red_score else 1
|
||||||
|
max_b = max(self.blue_score.values()) if self.blue_score else 1
|
||||||
|
for i in self.red_score:
|
||||||
|
self.red_score[i] /= max_r
|
||||||
|
for i in self.blue_score:
|
||||||
|
self.blue_score[i] /= max_b
|
||||||
|
|
||||||
|
|
||||||
|
# ==================== 集成学习引擎 ====================
|
||||||
|
|
||||||
|
class EnsembleLearner:
|
||||||
|
"""集成学习引擎：多模型加权 + 回测优化权重"""
|
||||||
|
|
||||||
|
def __init__(self, lt, data, rule):
|
||||||
|
self.lt = lt
|
||||||
|
self.data = data
|
||||||
|
self.rule = rule
|
||||||
|
self.rcnt = rule["red_count"]
|
||||||
|
self.rmax = rule["red_max"]
|
||||||
|
self.bcnt = rule["blue_count"]
|
||||||
|
self.bmax = rule["blue_max"]
|
||||||
|
|
||||||
|
# 初始化5个模型
|
||||||
|
self.models = {
|
||||||
|
'freq': FrequencyModel(lt, data, rule),
|
||||||
|
'bayes': BayesianModel(lt, data, rule),
|
||||||
|
'markov': MarkovModel(lt, data, rule),
|
||||||
|
'kmeans': KMeansModel(lt, data, rule),
|
||||||
|
'residual': ResidualModel(lt, data, rule)
|
||||||
|
}
|
||||||
|
|
||||||
|
# 初始权重（等权）
|
||||||
|
self.weights = {m: 1.0 / len(self.models) for m in self.models}
|
||||||
|
self.initial_weights = self.weights.copy()
|
||||||
|
|
||||||
|
# 回测结果
|
||||||
|
self.backtest_results = None
|
||||||
|
|
||||||
|
def get_combined_score(self):
|
||||||
|
"""获取集成后的综合评分"""
|
||||||
|
red_combined = {}
|
||||||
|
blue_combined = {}
|
||||||
|
|
||||||
|
for model_name, model in self.models.items():
|
||||||
|
w = self.weights[model_name]
|
||||||
|
for i in range(1, self.rmax + 1):
|
||||||
|
red_combined[i] = red_combined.get(i, 0) + w * model.red_score[i]
|
||||||
|
for i in range(1, self.bmax + 1):
|
||||||
|
blue_combined[i] = blue_combined.get(i, 0) + w * model.blue_score[i]
|
||||||
|
|
||||||
|
# 归一化
|
||||||
|
max_r = max(red_combined.values()) if red_combined else 1
|
||||||
|
max_b = max(blue_combined.values()) if blue_combined else 1
|
||||||
|
for i in red_combined:
|
||||||
|
red_combined[i] /= max_r
|
||||||
|
for i in blue_combined:
|
||||||
|
blue_combined[i] /= max_b
|
||||||
|
|
||||||
|
return red_combined, blue_combined
|
||||||
|
|
||||||
|
def _get_combined_for_weights(self, weights):
|
||||||
|
"""根据给定权重获取组合评分"""
|
||||||
|
red_c = {}
|
||||||
|
blue_c = {}
|
||||||
|
for model_name, model in self.models.items():
|
||||||
|
ww = weights[model_name]
|
||||||
|
for i in range(1, self.rmax + 1):
|
||||||
|
red_c[i] = red_c.get(i, 0) + ww * model.red_score[i]
|
||||||
|
for i in range(1, self.bmax + 1):
|
||||||
|
blue_c[i] = blue_c.get(i, 0) + ww * model.blue_score[i]
|
||||||
|
return red_c, blue_c
|
||||||
|
|
||||||
|
def _predict_from_scores(self, red_c, blue_c, rmax, bmax, rcnt, bcnt):
|
||||||
|
"""根据评分预测号码"""
|
||||||
|
sorted_r = sorted(red_c.items(), key=lambda x: x[1], reverse=True)
|
||||||
|
top_r = [n for n, s in sorted_r[:rmax]]
|
||||||
|
sorted_b = sorted(blue_c.items(), key=lambda x: x[1], reverse=True)
|
||||||
|
top_b = [n for n, s in sorted_b[:bmax]]
|
||||||
|
|
||||||
|
sel = []
|
||||||
|
cand = top_r[:]
|
||||||
|
weights_r = [red_c.get(n, 0.5) for n in cand]
|
||||||
|
while len(sel) < rcnt and cand:
|
||||||
|
tw = sum(weights_r)
|
||||||
|
if tw <= 0: pk = random.choice(cand)
|
||||||
|
else:
|
||||||
|
rv = random.random() * tw
|
||||||
|
cum = 0
|
||||||
|
for i, ww in enumerate(weights_r):
|
||||||
|
cum += ww
|
||||||
|
if rv <= cum:
|
||||||
|
pk = cand[i]
|
||||||
|
break
|
||||||
|
if pk not in sel: sel.append(pk)
|
||||||
|
idx = cand.index(pk); cand.pop(idx); weights_r.pop(idx)
|
||||||
|
sel.sort()
|
||||||
|
|
||||||
|
bls = []
|
||||||
|
cand_b = top_b[:]
|
||||||
|
weights_b = [blue_c.get(n, 0.5) for n in cand_b]
|
||||||
|
while len(bls) < bcnt and cand_b:
|
||||||
|
tw = sum(weights_b)
|
||||||
|
if tw <= 0: pk = random.choice(cand_b)
|
||||||
|
else:
|
||||||
|
rv = random.random() * tw
|
||||||
|
cum = 0
|
||||||
|
for i, ww in enumerate(weights_b):
|
||||||
|
cum += ww
|
||||||
|
if rv <= cum:
|
||||||
|
pk = cand_b[i]
|
||||||
|
break
|
||||||
|
if pk not in bls: bls.append(pk)
|
||||||
|
idx = cand_b.index(pk); cand_b.pop(idx); weights_b.pop(idx)
|
||||||
|
bls.sort()
|
||||||
|
|
||||||
|
return sel, bls
|
||||||
|
|
||||||
|
def backtest_optimize(self, test_periods=30):
|
||||||
|
"""
|
||||||
|
通过回测优化各模型权重
|
||||||
|
使用简单网格搜索 + 梯度上升混合方法
|
||||||
|
"""
|
||||||
|
rule = self.rule
|
||||||
|
rmax, bmax = self.rule["red_max"], self.rule["blue_max"]
|
||||||
|
|
||||||
|
print(f"\n{'='*60}")
|
||||||
|
print(f"🧠 集成学习权重优化 | 彩种: {rule['name']} | 数据期数: {len(self.data)}")
|
||||||
|
print(f"{'='*60}")
|
||||||
|
|
||||||
|
# 准备回测数据（按时间顺序）
|
||||||
|
sorted_data = sorted(self.data, key=lambda x: int(x["issue"]))
|
||||||
|
if len(sorted_data) < test_periods + 10:
|
||||||
|
test_periods = max(10, len(sorted_data) - 10)
|
||||||
|
|
||||||
|
# 评分函数：计算推荐与实际匹配的分数
|
||||||
|
def evaluate(weights, data_slice):
|
||||||
|
red_comp, blue_comp = self._get_combined_for_weights(weights)
|
||||||
|
pred_r, pred_b = self._predict_from_scores(red_comp, blue_comp, rmax, bmax, rule["red_count"], rule["blue_count"])
|
||||||
|
|
||||||
|
actual = data_slice
|
||||||
|
act_r = [int(x) for x in actual["reds"]]
|
||||||
|
act_b = actual["blue"] if isinstance(actual["blue"], list) else [int(actual["blue"])]
|
||||||
|
|
||||||
|
# 评分：命中红球数*2 + 命中蓝球数
|
||||||
|
hit_r = sum(1 for n in pred_r if n in act_r)
|
||||||
|
hit_b = sum(1 for n in pred_b if n in act_b if (isinstance(act_b, list) and n in act_b) or (isinstance(act_b, int) and n == act_b))
|
||||||
|
return hit_r * 2 + hit_b
|
||||||
|
|
||||||
|
# 简单网格搜索优化权重
|
||||||
|
best_weights = self.weights.copy()
|
||||||
|
best_score = float('-inf')
|
||||||
|
|
||||||
|
# 初始评估
|
||||||
|
current_score = 0
|
||||||
|
for i in range(test_periods):
|
||||||
|
if i + 10 < len(sorted_data):
|
||||||
|
slice_data = sorted_data[i + 10:i + 11] # 下一期作为测试
|
||||||
|
current_score += evaluate(self.weights, slice_data[0])
|
||||||
|
|
||||||
|
print(f" 初始等权总分: {current_score:.1f}")
|
||||||
|
|
||||||
|
# 简单梯度上升（每个模型权重微调）
|
||||||
|
improved = True
|
||||||
|
iterations = 0
|
||||||
|
max_iterations = 20
|
||||||
|
|
||||||
|
while improved and iterations < max_iterations:
|
||||||
|
improved = False
|
||||||
|
iterations += 1
|
||||||
|
|
||||||
|
for model_name in self.weights:
|
||||||
|
for delta in [-0.05, 0.05]: # 微调权重
|
||||||
|
test_w = self.weights.copy()
|
||||||
|
test_w[model_name] = max(0.01, min(0.9, test_w[model_name] + delta))
|
||||||
|
|
||||||
|
# 归一化
|
||||||
|
total = sum(test_w.values())
|
||||||
|
test_w = {k: v/total for k, v in test_w.items()}
|
||||||
|
|
||||||
|
score = 0
|
||||||
|
for i in range(test_periods):
|
||||||
|
if i + 10 < len(sorted_data):
|
||||||
|
slice_data = sorted_data[i + 10:i + 11]
|
||||||
|
score += evaluate(test_w, slice_data[0])
|
||||||
|
|
||||||
|
if score > best_score:
|
||||||
|
best_score = score
|
||||||
|
best_weights = test_w.copy()
|
||||||
|
improved = True
|
||||||
|
|
||||||
|
self.weights = best_weights
|
||||||
|
self.backtest_results = {
|
||||||
|
'iterations': iterations,
|
||||||
|
'best_score': best_score,
|
||||||
|
'test_periods': test_periods,
|
||||||
|
'model_weights': best_weights
|
||||||
|
}
|
||||||
|
|
||||||
|
# 输出各模型权重
|
||||||
|
print(f" 优化后权重:")
|
||||||
|
for m, w in sorted(self.weights.items(), key=lambda x: -x[1]):
|
||||||
|
model_names = {
|
||||||
|
'freq': '频率加权',
|
||||||
|
'bayes': '贝叶斯平滑',
|
||||||
|
'markov': '马尔可夫链',
|
||||||
|
'kmeans': 'K-Means聚类',
|
||||||
|
'residual': '残差回归'
|
||||||
|
}
|
||||||
|
print(f" {model_names.get(m, m):<12s}: {w:.4f}")
|
||||||
|
|
||||||
|
print(f" 最终集成总分: {best_score:.1f} (测试{test_periods}期)")
|
||||||
|
|
||||||
|
# 返回综合评分
|
||||||
|
return self.get_combined_score()
|
||||||
|
|
||||||
|
def get_recommendations(self, count=5):
|
||||||
|
"""基于集成评分生成推荐"""
|
||||||
|
red_comp, blue_comp = self.get_combined_score()
|
||||||
|
rmax, bmax = self.rule["red_max"], self.rule["blue_max"]
|
||||||
|
rcnt, bcnt = self.rule["red_count"], self.rule["blue_count"]
|
||||||
|
|
||||||
|
sorted_r = sorted(red_comp.items(), key=lambda x: x[1], reverse=True)
|
||||||
|
top_r = [n for n, s in sorted_r[:rmax]]
|
||||||
|
sorted_b = sorted(blue_comp.items(), key=lambda x: x[1], reverse=True)
|
||||||
|
top_b = [n for n, s in sorted_b[:bmax]]
|
||||||
|
|
||||||
|
seed = hashlib.md5(f"{self.lt}_ensemble".encode()).hexdigest()
|
||||||
|
res = []
|
||||||
|
for ci in range(count):
|
||||||
|
cs = hashlib.md5(f"ensemble_c{ci}_{seed}".encode()).hexdigest()
|
||||||
|
cr = random.Random(cs)
|
||||||
|
|
||||||
|
sel = []
|
||||||
|
cand = top_r[:]
|
||||||
|
w = [red_comp.get(n, 0.5) for n in cand]
|
||||||
|
while len(sel) < rcnt and cand:
|
||||||
|
tw = sum(w)
|
||||||
|
if tw <= 0: pk = cr.choice(cand)
|
||||||
|
else:
|
||||||
|
rv = cr.random() * tw
|
||||||
|
cum = 0
|
||||||
|
for i, ww in enumerate(w):
|
||||||
|
cum += ww
|
||||||
|
if rv <= cum:
|
||||||
|
pk = cand[i]
|
||||||
|
break
|
||||||
|
if pk not in sel: sel.append(pk)
|
||||||
|
idx = cand.index(pk); cand.pop(idx); w.pop(idx)
|
||||||
|
sel.sort()
|
||||||
|
|
||||||
|
bls = []
|
||||||
|
cand_b = top_b[:]
|
||||||
|
wb = [blue_comp.get(n, 0.5) for n in cand_b]
|
||||||
|
while len(bls) < bcnt and cand_b:
|
||||||
|
tw = sum(wb)
|
||||||
|
if tw <= 0: pk = cr.choice(cand_b)
|
||||||
|
else:
|
||||||
|
rv = cr.random() * tw
|
||||||
|
cum = 0
|
||||||
|
for i, ww in enumerate(wb):
|
||||||
|
cum += ww
|
||||||
|
if rv <= cum:
|
||||||
|
pk = cand_b[i]
|
||||||
|
break
|
||||||
|
if pk not in bls: bls.append(pk)
|
||||||
|
idx = cand_b.index(pk); cand_b.pop(idx); wb.pop(idx)
|
||||||
|
bls.sort()
|
||||||
|
|
||||||
|
score = round(sum(red_comp.get(n, 0) for n in sel) + sum(blue_comp.get(n, 0) for n in bls), 4)
|
||||||
|
mid = rmax / 2
|
||||||
|
res.append({
|
||||||
|
"reds": sel,
|
||||||
|
"blues": bls,
|
||||||
|
"score": score,
|
||||||
|
"odd_even": f"{sum(1 for n in sel if n % 2 == 1)}:{rcnt - sum(1 for n in sel if n % 2 == 1)}",
|
||||||
|
"big_small": f"{sum(1 for n in sel if n > mid)}:{rcnt - sum(1 for n in sel if n > mid)}",
|
||||||
|
"sum": sum(sel)
|
||||||
|
})
|
||||||
|
|
||||||
|
res.sort(key=lambda x: x["score"], reverse=True)
|
||||||
|
return res
|
||||||
|
|
||||||
|
|
||||||
|
# ==================== 主函数 ====================
|
||||||
|
|
||||||
|
def ensemble_recommendation(lt, data, rule, count=5):
|
||||||
|
"""集成学习推荐主接口"""
|
||||||
|
learner = EnsembleLearner(lt, data, rule)
|
||||||
|
red_comp, blue_comp = learner.backtest_optimize(test_periods=30)
|
||||||
|
recommendations = learner.get_recommendations(count=count)
|
||||||
|
|
||||||
|
return {
|
||||||
|
'weights': learner.weights,
|
||||||
|
'backtest': learner.backtest_results,
|
||||||
|
'recommendations': recommendations,
|
||||||
|
'combined_scores': {
|
||||||
|
'red': red_comp,
|
||||||
|
'blue': blue_comp
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
# 简单测试
|
||||||
|
print("集成学习模块加载成功！")
|
||||||
|
print("可用模型：频率加权、贝叶斯平滑、马尔可夫链、K-Means聚类、残差回归")
|
||||||
@@ -0,0 +1,285 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
回滚迭代跟踪工具 - 管理 AI 进化版本和回滚机制
|
||||||
|
"""
|
||||||
|
|
||||||
|
import json
|
||||||
|
import os
|
||||||
|
from datetime import datetime
|
||||||
|
from typing import Dict, List, Optional, Any
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
|
||||||
|
class EvolutionTracker:
|
||||||
|
"""回滚迭代跟踪器"""
|
||||||
|
|
||||||
|
def __init__(self, data_dir: str = "./evolution_tracking"):
|
||||||
|
self.data_dir = Path(data_dir)
|
||||||
|
self.data_dir.mkdir(parents=True, exist_ok=True)
|
||||||
|
self.version_file = self.data_dir / "versions.json"
|
||||||
|
self.performance_log = self.data_dir / "performance.json"
|
||||||
|
self.rollback_log = self.data_dir / "rollbacks.json"
|
||||||
|
|
||||||
|
def initialize(self):
|
||||||
|
"""初始化跟踪器"""
|
||||||
|
# 初始化版本记录
|
||||||
|
if not self.version_file.exists():
|
||||||
|
initial_version = {
|
||||||
|
"version": 0,
|
||||||
|
"created_at": datetime.now().isoformat(),
|
||||||
|
"performance": {
|
||||||
|
"win_rate": 0.0,
|
||||||
|
"total_bets": 0,
|
||||||
|
"total_winnings": 0.0
|
||||||
|
}
|
||||||
|
}
|
||||||
|
self.save_version(initial_version)
|
||||||
|
|
||||||
|
# 初始化性能日志
|
||||||
|
if not self.performance_log.exists():
|
||||||
|
self.save_performance({
|
||||||
|
"history": [],
|
||||||
|
"current": {
|
||||||
|
"total_bets": 0,
|
||||||
|
"win_rate": 0.0,
|
||||||
|
"earnings": 0.0,
|
||||||
|
"total_winnings": 0.0
|
||||||
|
}
|
||||||
|
})
|
||||||
|
|
||||||
|
# 初始化回滚日志
|
||||||
|
if not self.rollback_log.exists():
|
||||||
|
self.save_rollbacks({
|
||||||
|
"history": [],
|
||||||
|
"current_version": 0
|
||||||
|
})
|
||||||
|
|
||||||
|
def save_version(self, version_data: Dict):
|
||||||
|
"""保存版本信息"""
|
||||||
|
with open(self.version_file, 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(version_data, f, ensure_ascii=False, indent=2)
|
||||||
|
|
||||||
|
def load_version(self) -> Dict:
|
||||||
|
"""加载当前版本"""
|
||||||
|
if self.version_file.exists():
|
||||||
|
with open(self.version_file, 'r', encoding='utf-8') as f:
|
||||||
|
return json.load(f)
|
||||||
|
return {}
|
||||||
|
|
||||||
|
def record_performance(self, version: int, win_rate: float, earnings: float):
|
||||||
|
"""记录性能数据"""
|
||||||
|
performance = self.load_performance()
|
||||||
|
|
||||||
|
entry = {
|
||||||
|
"version": version,
|
||||||
|
"recorded_at": datetime.now().isoformat(),
|
||||||
|
"win_rate": win_rate,
|
||||||
|
"earnings": earnings,
|
||||||
|
"total_bets": performance["current"].get("total_bets", 0),
|
||||||
|
"total_winnings": performance["current"].get("total_winnings", 0.0)
|
||||||
|
}
|
||||||
|
|
||||||
|
performance["history"].append(entry)
|
||||||
|
|
||||||
|
# 更新当前性能
|
||||||
|
current = performance["current"]
|
||||||
|
current["total_bets"] += 1
|
||||||
|
current["win_rate"] = win_rate
|
||||||
|
current["earnings"] = earnings
|
||||||
|
current["total_winnings"] = current.get("total_winnings", 0.0) + earnings
|
||||||
|
|
||||||
|
self.save_performance(performance)
|
||||||
|
|
||||||
|
def load_performance(self) -> Dict:
|
||||||
|
"""加载性能日志"""
|
||||||
|
if self.performance_log.exists():
|
||||||
|
with open(self.performance_log, 'r', encoding='utf-8') as f:
|
||||||
|
return json.load(f)
|
||||||
|
return {"history": [], "current": {}}
|
||||||
|
|
||||||
|
def save_performance(self, performance: Dict):
|
||||||
|
"""保存性能日志"""
|
||||||
|
with open(self.performance_log, 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(performance, f, ensure_ascii=False, indent=2)
|
||||||
|
|
||||||
|
def record_rollback(self, from_version: int, to_version: int, reason: str):
|
||||||
|
"""记录回滚操作"""
|
||||||
|
rollbacks = self.load_rollbacks()
|
||||||
|
|
||||||
|
entry = {
|
||||||
|
"from_version": from_version,
|
||||||
|
"to_version": to_version,
|
||||||
|
"reason": reason,
|
||||||
|
"timestamp": datetime.now().isoformat()
|
||||||
|
}
|
||||||
|
|
||||||
|
rollbacks["history"].append(entry)
|
||||||
|
rollbacks["current_version"] = to_version
|
||||||
|
|
||||||
|
self.save_rollbacks(rollbacks)
|
||||||
|
|
||||||
|
def load_rollbacks(self) -> Dict:
|
||||||
|
"""加载回滚日志"""
|
||||||
|
if self.rollback_log.exists():
|
||||||
|
with open(self.rollback_log, 'r', encoding='utf-8') as f:
|
||||||
|
return json.load(f)
|
||||||
|
return {"history": [], "current_version": 0}
|
||||||
|
|
||||||
|
def save_rollbacks(self, rollbacks: Dict):
|
||||||
|
"""保存回滚日志"""
|
||||||
|
with open(self.rollback_log, 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(rollbacks, f, ensure_ascii=False, indent=2)
|
||||||
|
|
||||||
|
def get_version_history(self) -> List[Dict]:
|
||||||
|
"""获取版本历史"""
|
||||||
|
performance = self.load_performance()
|
||||||
|
return performance.get("history", [])
|
||||||
|
|
||||||
|
def get_best_version(self) -> Optional[Dict]:
|
||||||
|
"""获取性能最好的版本"""
|
||||||
|
history = self.get_version_history()
|
||||||
|
if not history:
|
||||||
|
return None
|
||||||
|
|
||||||
|
best = max(history, key=lambda x: x.get("win_rate", 0))
|
||||||
|
return best
|
||||||
|
|
||||||
|
def compare_versions(self, version1: int, version2: int) -> Dict:
|
||||||
|
"""比较两个版本的性能"""
|
||||||
|
history = self.get_version_history()
|
||||||
|
|
||||||
|
v1_data = next((h for h in history if h["version"] == version1), None)
|
||||||
|
v2_data = next((h for h in history if h["version"] == version2), None)
|
||||||
|
|
||||||
|
if not v1_data or not v2_data:
|
||||||
|
return {"error": "版本不存在"}
|
||||||
|
|
||||||
|
comparison = {
|
||||||
|
"version1": version1,
|
||||||
|
"version2": version2,
|
||||||
|
"v1_win_rate": v1_data.get("win_rate", 0),
|
||||||
|
"v2_win_rate": v2_data.get("win_rate", 0),
|
||||||
|
"v1_earnings": v1_data.get("earnings", 0.0),
|
||||||
|
"v2_earnings": v2_data.get("earnings", 0.0),
|
||||||
|
"better_version": version1 if v1_data.get("win_rate", 0) > v2_data.get("win_rate", 0) else version2
|
||||||
|
}
|
||||||
|
|
||||||
|
return comparison
|
||||||
|
|
||||||
|
def generate_report(self) -> str:
|
||||||
|
"""生成进化报告"""
|
||||||
|
performance = self.load_performance()
|
||||||
|
rollbacks = self.load_rollbacks()
|
||||||
|
|
||||||
|
report = [
|
||||||
|
"="*60,
|
||||||
|
"AI 进化跟踪报告",
|
||||||
|
"="*60,
|
||||||
|
f"当前版本：{rollbacks.get('current_version', 0)}",
|
||||||
|
f"回滚历史数：{len(rollbacks.get('history', []))}",
|
||||||
|
"\n当前性能数据：",
|
||||||
|
f" 胜率：{performance['current'].get('win_rate', 0):.2%}",
|
||||||
|
f" 累计投注：{performance['current'].get('total_bets', 0)}",
|
||||||
|
f" 累计收益：¥{performance['current'].get('total_winnings', 0.0):.2f}",
|
||||||
|
"\n版本历史：",
|
||||||
|
]
|
||||||
|
|
||||||
|
history = performance.get("history", [])
|
||||||
|
if history:
|
||||||
|
for i, entry in enumerate(history[-10:], 1): # 显示最后 10 个
|
||||||
|
report.extend([
|
||||||
|
f" [v{entry['version']}] 胜率：{entry['win_rate']:.2%}, "
|
||||||
|
f"收益：¥{entry['earnings']:.2f}, "
|
||||||
|
f"记录时间：{entry['recorded_at']}",
|
||||||
|
""
|
||||||
|
])
|
||||||
|
else:
|
||||||
|
report.append(" 暂无历史数据")
|
||||||
|
|
||||||
|
report.extend(["\n回滚历史：", ""])
|
||||||
|
|
||||||
|
if rollbacks.get("history"):
|
||||||
|
for i, entry in enumerate(rollbacks["history"]):
|
||||||
|
report.extend([
|
||||||
|
f" 回滚 #{i+1}: v{entry['from_version']} → v{entry['to_version']}, "
|
||||||
|
f"原因：{entry['reason']}, "
|
||||||
|
f"时间：{entry['timestamp']}",
|
||||||
|
""
|
||||||
|
])
|
||||||
|
else:
|
||||||
|
report.append(" 暂无回滚记录")
|
||||||
|
|
||||||
|
report.extend(["="*60, ""])
|
||||||
|
return "\n".join(report)
|
||||||
|
|
||||||
|
def optimize_weights(self, learning_rate: float = 0.1, epochs: int = 100) -> Dict:
|
||||||
|
"""优化模型权重 - 模拟进化过程"""
|
||||||
|
import random
|
||||||
|
|
||||||
|
# 初始化权重
|
||||||
|
weights = {
|
||||||
|
"red_hot": 0.3,
|
||||||
|
"red_cold": 0.2,
|
||||||
|
"consecutive": 0.15,
|
||||||
|
"sum_range": 0.15,
|
||||||
|
"parity": 0.1,
|
||||||
|
"entropy": 0.1
|
||||||
|
}
|
||||||
|
|
||||||
|
# 模拟进化过程
|
||||||
|
for epoch in range(epochs):
|
||||||
|
# 模拟数据输入
|
||||||
|
sample_data = [random.randint(1, 33) for _ in range(10)]
|
||||||
|
|
||||||
|
# 计算损失
|
||||||
|
loss = sum((w - random.random())**2 for w in weights.values())
|
||||||
|
|
||||||
|
# 梯度下降
|
||||||
|
for key in weights:
|
||||||
|
gradient = sum(random.random() for _ in range(10)) / len(sample_data)
|
||||||
|
weights[key] -= learning_rate * gradient
|
||||||
|
|
||||||
|
# 归一化权重
|
||||||
|
total = sum(weights.values())
|
||||||
|
weights = {k: round(v/total, 4) for k, v in weights.items()}
|
||||||
|
|
||||||
|
return {
|
||||||
|
"optimized_weights": weights,
|
||||||
|
"epochs_completed": epochs,
|
||||||
|
"final_loss": round(loss, 4)
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
"""主函数 - 测试回滚跟踪工具"""
|
||||||
|
tracker = EvolutionTracker()
|
||||||
|
tracker.initialize()
|
||||||
|
|
||||||
|
# 模拟一些版本记录
|
||||||
|
versions = [
|
||||||
|
(1, 0.05, 100.0),
|
||||||
|
(2, 0.12, 150.0),
|
||||||
|
(3, 0.08, 120.0),
|
||||||
|
(4, 0.15, 200.0),
|
||||||
|
(5, 0.18, 250.0)
|
||||||
|
]
|
||||||
|
|
||||||
|
for version, win_rate, earnings in versions:
|
||||||
|
tracker.record_performance(version, win_rate, earnings)
|
||||||
|
|
||||||
|
# 模拟回滚
|
||||||
|
tracker.record_rollback(5, 4, "性能下降")
|
||||||
|
|
||||||
|
# 生成报告
|
||||||
|
print(tracker.generate_report())
|
||||||
|
|
||||||
|
# 优化权重
|
||||||
|
optimized = tracker.optimize_weights()
|
||||||
|
print("\n优化结果：")
|
||||||
|
print(f" 完成轮数：{optimized['epochs_completed']}")
|
||||||
|
print(f" 最终损失：{optimized['final_loss']}")
|
||||||
|
print(f" 优化权重：{optimized['optimized_weights']}")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
File diff suppressed because it is too large.
Load diff
Reference in new issue
Block a user