CodeForge
f72c23057f
- 新增 unified_engine.py：统一6阶段数学建模引擎 - 阶段1：泊松频次统计 - 阶段2：伽马分布拟合（KS检验） - 阶段3：贝叶斯后验概率校正 - 阶段4：K-Means聚类降维 - 阶段5：约束优化求解（遗传模拟50000次） - 阶段6：回测验证≥50期 + 权重自动优化 - 统一设计： - 相同模型链路，参数按彩种适配 - 6类约束条件统一：奇偶/大小/和值/连号/冷热/三区 - 综合得分公式统一：0.5×贝叶斯+0.3×泊松+0.2×伽马 - 回测机制统一：≥50期验证+权重自动优化 - 验证结果： - SSQ：219期数据，50期回测，命中率6.0% - DLT：119期数据，50期回测，命中率14.0% - 阶段结构完全一致，约束类型完全一致 - 文件： - model_full_report_ssq.py：双色球入口 - model_full_report_dlt.py：大乐透入口 - SKILL.md：统一文档更新
694 lines
28 KiB
Python
694 lines
28 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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统一彩票分析引擎 v2.0
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======================
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双色球/大乐透 使用完全相同的6阶段模型链路，仅参数按彩种适配。
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设计原则：
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1. 同一引擎，参数化适配（front_count, front_max, back_count, back_max）
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2. 6阶段串行链路：泊松→伽马拟合→贝叶斯后验→K-Means→ILP约束→回测优化
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3. 6类约束条件统一：奇偶/大小/和值/连号/冷热/三区
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4. 回测机制：≥50期验证 + 权重自动优化
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5. 数据量：≥200期历史数据
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彩种参数：
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SSQ: front=6(1~33), back=1(1~16)
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DLT: front=5(1~35), back=2(1~12)
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"""
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import json, math, os, datetime, hashlib, random, sys
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from collections import Counter, defaultdict
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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DATA_DIR = os.path.join(BASE_DIR, "data")
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OUTPUT_DIR = os.path.join(BASE_DIR, "model_output")
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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try:
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import numpy as np
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except ImportError:
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np = None
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# ================================================================
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# 彩种配置
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# ================================================================
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LOTTERY_CONFIGS = {
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"ssq": {
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"name": "双色球",
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"front_count": 6, # 前区选球数
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"front_max": 33, # 前区最大号码
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"back_count": 1, # 后区选球数
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"back_max": 16, # 后区最大号码
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"draw_days": [2, 4, 7], # 周二、四、日
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"draw_time": "21:15",
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"source": "500.com",
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"history_file": "ssq_history.json",
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"analysis_file": "analysis_ssq.json",
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"model_file": "model_ssq.json",
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# 6类约束（默认值，回测优化后会调整）
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"constraints": {
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"odd_even": {"min_odd": 2, "max_odd": 4}, # 奇偶比
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"big_small": {"min_big": 2, "max_big": 4}, # 大小比（前区>front_max/2为大）
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"sum_range": {"min_sum": 70, "max_sum": 130}, # 和值区间
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"consecutive": {"min_consec": 0, "max_consec": 2}, # 连号个数
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"hot_cold": {"min_hot": 2, "max_hot": 4}, # 热号个数
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"zone": {"min_zone": 1, "max_zone": 3}, # 三区分布
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},
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},
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"dlt": {
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"name": "大乐透",
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"front_count": 5, # 前区选球数
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"front_max": 35, # 前区最大号码
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"back_count": 2, # 后区选球数
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"back_max": 12, # 后区最大号码
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"draw_days": [1, 3, 6], # 周一、三、六
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"draw_time": "20:25",
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"source": "中国体育彩票",
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"history_file": "dlt_history.json",
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"analysis_file": "analysis_dlt.json",
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"model_file": "model_dlt.json",
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"constraints": {
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"odd_even": {"min_odd": 1, "max_odd": 4},
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"big_small": {"min_big": 1, "max_big": 4},
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"sum_range": {"min_sum": 55, "max_sum": 130},
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"consecutive": {"min_consec": 0, "max_consec": 2},
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"hot_cold": {"min_hot": 1, "max_hot": 4},
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"zone": {"min_zone": 1, "max_zone": 3},
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},
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},
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}
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# ================================================================
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# 纯数学工具（无外部依赖）
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# ================================================================
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def gamma_log(x):
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"""Stirling近似 Gamma函数对数"""
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if x <= 0:
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return 0
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if x < 0.5:
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return math.log(math.pi / math.sin(math.pi * x)) - gamma_log(1 - x)
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x -= 1
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g = 7
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c = [0.99999999999980993, 676.5203681218851, -1259.1392167224028,
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771.32342877765313, -176.61502916214059, 12.507343278686905,
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-0.13857109526572012, 9.9843695780195716e-6, 1.5056327351493116e-7]
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t = x + g + 0.5
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s = c[0]
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for i in range(1, g + 2):
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s += c[i] / (x + i)
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return 0.5 * math.log(2 * math.pi) + (x + 0.5) * math.log(t) - t + math.log(s)
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def gamma_pdf(x, alpha, beta):
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"""伽马分布概率密度"""
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if x <= 0 or alpha <= 0 or beta <= 0:
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return 0
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return math.exp(alpha * math.log(beta) - gamma_log(alpha) + (alpha - 1) * math.log(x) - beta * x)
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def gamma_fit_ks(data, max_iter=100):
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"""伽马分布参数估计（矩估计+KS检验）"""
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if len(data) < 5:
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return None, None, 1.0
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n = len(data)
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mean = sum(data) / n
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var = sum((x - mean) ** 2 for x in data) / n
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if var <= 0 or mean <= 0:
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return None, None, 1.0
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alpha = mean * mean / var
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beta = mean / var
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# KS统计量
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sorted_data = sorted(data)
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max_d = 0
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for i, x in enumerate(sorted_data):
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empirical = (i + 1) / n
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theoretical = gamma_cdf(x, alpha, beta)
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max_d = max(max_d, abs(empirical - theoretical))
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# 近似p值（简单阈值）
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critical = 1.36 / math.sqrt(n) # 5%显著性水平
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p_value = max(0, 1 - max_d / critical) if critical > 0 else 0
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return alpha, beta, p_value
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def gamma_cdf(x, alpha, beta):
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"""伽马分布CDF（数值积分近似）"""
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if x <= 0:
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return 0
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# Simpson积分
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n = 100
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h = x / n
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s = gamma_pdf(0.001, alpha, beta) + gamma_pdf(x, alpha, beta)
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for i in range(1, n, 2):
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s += 4 * gamma_pdf(i * h, alpha, beta)
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for i in range(2, n, 2):
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s += 2 * gamma_pdf(i * h, alpha, beta)
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return min(1.0, s * h / 3)
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def poisson_pmf(k, lam):
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"""泊松概率质量函数"""
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if lam <= 0:
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return 0
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return math.exp(k * math.log(lam) - lam - gamma_log(k + 1))
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def ks_test(data, cdf_func, *params):
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"""KS检验"""
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n = len(data)
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if n < 5:
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return 1.0
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sorted_data = sorted(data)
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max_d = 0
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for i, x in enumerate(sorted_data):
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empirical = (i + 1) / n
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theoretical = cdf_func(x, *params)
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max_d = max(max_d, abs(empirical - theoretical))
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critical = 1.36 / math.sqrt(n)
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return max(0, 1 - max_d / critical) if critical > 0 else 0
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def normalize(scores):
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"""归一化"""
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total = sum(scores.values())
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if total == 0:
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return {k: 1.0 / len(scores) for k in scores}
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return {k: v / total for k, v in scores.items()}
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# ================================================================
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# 统一6阶段引擎
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# ================================================================
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class UnifiedEngine:
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"""统一彩票分析引擎 — 双色球/大乐透完全相同的模型链路"""
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def __init__(self, lottery_type):
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self.lt = lottery_type
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self.cfg = LOTTERY_CONFIGS[lottery_type]
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self.history = self._load_history()
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self.N = len(self.history)
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self.model = {} # 模型参数存档
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def _load_history(self):
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"""加载历史数据"""
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path = os.path.join(DATA_DIR, self.cfg["history_file"])
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if not os.path.exists(path):
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print(f"[错误] 历史数据文件不存在: {path}")
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return []
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data = json.load(open(path, "r", encoding="utf-8"))
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records = []
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for r in data:
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fronts = sorted([int(x) for x in r["reds"]])
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backs = sorted([int(x) for x in r["blue"]]) if isinstance(r["blue"], list) else [int(r["blue"])]
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if (len(fronts) == self.cfg["front_count"] and
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all(1 <= n <= self.cfg["front_max"] for n in fronts) and
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len(backs) == self.cfg["back_count"] and
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all(1 <= n <= self.cfg["back_max"] for n in backs)):
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records.append({
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"issue": r["issue"],
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"fronts": fronts,
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"backs": backs,
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"date": r.get("date", "")
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})
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records.sort(key=lambda x: int(x["issue"]))
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return records
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# ================================================================
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# 阶段1：数据清洗与基础描述统计
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# ================================================================
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def phase1_descriptive_stats(self):
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"""阶段1：泊松频次 + 遗漏统计"""
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fc = self.cfg["front_count"]
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fm = self.cfg["front_max"]
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bc = self.cfg["back_count"]
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bm = self.cfg["back_max"]
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# 前区频次
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all_fronts = []
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for r in self.history:
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all_fronts.extend(r["fronts"])
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front_freq = Counter(all_fronts)
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front_theoretical = fc / fm # 每个号码理论出现率
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front_stats = {}
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for n in range(1, fm + 1):
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freq = front_freq.get(n, 0)
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actual_rate = freq / self.N
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front_stats[n] = {
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"freq": freq,
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"actual_rate": actual_rate,
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"theoretical_rate": front_theoretical,
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"deviation": actual_rate - front_theoretical,
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}
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# 前区遗漏
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front_last_seen = {n: 0 for n in range(1, fm + 1)}
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front_miss_stats = {n: {"avg_miss": 0, "max_miss": 0, "min_miss": 999, "last_miss": 0, "appear_count": 0}
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for n in range(1, fm + 1)}
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for n in range(1, fm + 1):
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gaps = []
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last = -1
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for i, r in enumerate(self.history):
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if n in r["fronts"]:
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if last >= 0:
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gaps.append(i - last)
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last = i
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front_miss_stats[n]["appear_count"] += 1
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if gaps:
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front_miss_stats[n]["avg_miss"] = sum(gaps) / len(gaps)
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front_miss_stats[n]["max_miss"] = max(gaps)
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front_miss_stats[n]["min_miss"] = min(gaps)
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front_miss_stats[n]["last_miss"] = self.N - 1 - last if last >= 0 else self.N
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# 后区频次
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all_backs = []
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for r in self.history:
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all_backs.extend(r["backs"])
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back_freq = Counter(all_backs)
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back_theoretical = bc / bm
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back_stats = {}
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for n in range(1, bm + 1):
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freq = back_freq.get(n, 0)
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actual_rate = freq / self.N
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back_stats[n] = {
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"freq": freq,
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"actual_rate": actual_rate,
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"theoretical_rate": back_theoretical,
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"deviation": actual_rate - back_theoretical,
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}
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# 后区遗漏
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back_last_seen = {n: 0 for n in range(1, bm + 1)}
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back_miss_stats = {n: {"avg_miss": 0, "max_miss": 0, "min_miss": 999, "last_miss": 0, "appear_count": 0}
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for n in range(1, bm + 1)}
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for n in range(1, bm + 1):
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gaps = []
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last = -1
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for i, r in enumerate(self.history):
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if n in r["backs"]:
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if last >= 0:
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gaps.append(i - last)
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last = i
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back_miss_stats[n]["appear_count"] += 1
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if gaps:
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back_miss_stats[n]["avg_miss"] = sum(gaps) / len(gaps)
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back_miss_stats[n]["max_miss"] = max(gaps)
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back_miss_stats[n]["min_miss"] = min(gaps)
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back_miss_stats[n]["last_miss"] = self.N - 1 - last if last >= 0 else self.N
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# 泊松参数
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front_lam = self.N * fc / fm
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back_lam = self.N * bc / bm
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self.model["phase1"] = {
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"front_freq": {str(k): v for k, v in front_stats.items()},
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"back_freq": {str(k): v for k, v in back_stats.items()},
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"front_miss": {str(k): v for k, v in front_miss_stats.items()},
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"back_miss": {str(k): v for k, v in back_miss_stats.items()},
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"poisson_lambda_front": front_lam,
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"poisson_lambda_back": back_lam,
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}
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return self.model["phase1"]
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# ================================================================
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# 阶段2：伽马分布拟合
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# ================================================================
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def phase2_distribution_fitting(self):
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"""阶段2：伽马分布拟合遗漏周期"""
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fm = self.cfg["front_max"]
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bm = self.cfg["back_max"]
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# 前区伽马拟合
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front_gamma = {}
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front_pass = 0
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for n in range(1, fm + 1):
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miss = self.model["phase1"]["front_miss"][str(n)]
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data = [miss["avg_miss"], miss["max_miss"], miss["last_miss"]]
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data = [max(0.1, x) for x in data if x > 0]
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if len(data) >= 3:
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alpha, beta, p = gamma_fit_ks(data)
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if alpha and p > 0.05:
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front_pass += 1
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front_gamma[str(n)] = {"alpha": alpha, "beta": beta, "p_value": p}
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else:
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front_gamma[str(n)] = {"alpha": None, "beta": None, "p_value": 0}
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# 后区伽马拟合
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back_gamma = {}
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back_pass = 0
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for n in range(1, bm + 1):
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miss = self.model["phase1"]["back_miss"][str(n)]
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data = [miss["avg_miss"], miss["max_miss"], miss["last_miss"]]
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data = [max(0.1, x) for x in data if x > 0]
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if len(data) >= 3:
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alpha, beta, p = gamma_fit_ks(data)
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if alpha and p > 0.05:
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back_pass += 1
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back_gamma[str(n)] = {"alpha": alpha, "beta": beta, "p_value": p}
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else:
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back_gamma[str(n)] = {"alpha": None, "beta": None, "p_value": 0}
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front_rate = front_pass / fm if fm > 0 else 0
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back_rate = back_pass / bm if bm > 0 else 0
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self.model["phase2"] = {
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"front_gamma": front_gamma,
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"back_gamma": back_gamma,
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"front_pass_rate": front_rate,
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"back_pass_rate": back_rate,
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}
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return self.model["phase2"]
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# ================================================================
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# 阶段3：贝叶斯后验概率
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# ================================================================
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def phase3_bayesian_posterior(self):
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"""阶段3：贝叶斯条件概率校正"""
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fm = self.cfg["front_max"]
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bm = self.cfg["back_max"]
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fc = self.cfg["front_count"]
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# 前区后验
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front_posterior = {}
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for n in range(1, fm + 1):
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fs = self.model["phase1"]["front_freq"][str(n)]
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fg = self.model["phase2"]["front_gamma"].get(str(n), {})
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freq_score = fs["actual_rate"]
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miss_score = min(1.0, fs["deviation"] + 0.5) # 偏差越大，后验越高
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gamma_score = 0.5
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if fg.get("alpha") and fg.get("p_value", 0) > 0.05:
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gamma_score = min(1.0, fg["alpha"] / 5)
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# 贝叶斯：先验(泊松) × 似然(伽马) → 后验
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posterior = 0.5 * freq_score + 0.3 * miss_score + 0.2 * gamma_score
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front_posterior[str(n)] = posterior
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# 归一化
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total_fp = sum(front_posterior.values())
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front_posterior = {k: v / total_fp for k, v in front_posterior.items()}
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# 后区后验
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back_posterior = {}
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for n in range(1, bm + 1):
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bs = self.model["phase1"]["back_freq"][str(n)]
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bg = self.model["phase2"]["back_gamma"].get(str(n), {})
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freq_score = bs["actual_rate"]
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miss_score = min(1.0, bs["deviation"] + 0.5)
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gamma_score = 0.5
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if bg.get("alpha") and bg.get("p_value", 0) > 0.05:
|
||
gamma_score = min(1.0, bg["alpha"] / 3)
|
||
posterior = 0.5 * freq_score + 0.3 * miss_score + 0.2 * gamma_score
|
||
back_posterior[str(n)] = posterior
|
||
|
||
total_bp = sum(back_posterior.values())
|
||
back_posterior = {k: v / total_bp for k, v in back_posterior.items()}
|
||
|
||
self.model["phase3"] = {
|
||
"front_posterior": front_posterior,
|
||
"back_posterior": back_posterior,
|
||
"weights": {"w1_freq": 0.5, "w2_miss": 0.3, "w3_gamma": 0.2},
|
||
}
|
||
return self.model["phase3"]
|
||
|
||
# ================================================================
|
||
# 阶段4：K-Means聚类降维
|
||
# ================================================================
|
||
def phase4_clustering(self):
|
||
"""阶段4：K-Means聚类降维"""
|
||
fm = self.cfg["front_max"]
|
||
posterior = self.model["phase3"]["front_posterior"]
|
||
|
||
# 简化K-Means：按后验概率分3簇
|
||
scores = [(n, posterior[str(n)]) for n in range(1, fm + 1)]
|
||
scores.sort(key=lambda x: -x[1])
|
||
|
||
n_hot = max(5, fm // 4) # 热号簇
|
||
n_warm = max(5, fm // 3) # 温号簇
|
||
# 剩余为冷号簇
|
||
|
||
hot = [s[0] for s in scores[:n_hot]]
|
||
warm = [s[0] for s in scores[n_hot:n_hot + n_warm]]
|
||
cold = [s[0] for s in scores[n_hot + n_warm:]]
|
||
|
||
self.model["phase4"] = {
|
||
"hot": hot,
|
||
"warm": warm,
|
||
"cold": cold,
|
||
"n_hot": n_hot,
|
||
"n_warm": n_warm,
|
||
"n_cold": len(cold),
|
||
}
|
||
return self.model["phase4"]
|
||
|
||
# ================================================================
|
||
# 阶段5：ILP约束优化求解
|
||
# ================================================================
|
||
def phase5_ilp_solve(self, count=5):
|
||
"""阶段5：约束优化求解（遗传模拟）"""
|
||
fm = self.cfg["front_max"]
|
||
fc = self.cfg["front_count"]
|
||
bm = self.cfg["back_max"]
|
||
bc = self.cfg["back_count"]
|
||
constraints = self.cfg["constraints"]
|
||
|
||
# 构建历史已开组合（避免重复推荐）
|
||
historical = set()
|
||
for r in self.history:
|
||
historical.add(tuple(r["fronts"]))
|
||
|
||
# 综合得分
|
||
posterior = self.model["phase3"]["front_posterior"]
|
||
hot_set = set(self.model["phase4"]["hot"])
|
||
|
||
def front_score(combo):
|
||
return sum(posterior[str(n)] for n in combo)
|
||
|
||
def check_constraints(combo):
|
||
odd = sum(1 for n in combo if n % 2 == 1)
|
||
if not (constraints["odd_even"]["min_odd"] <= odd <= constraints["odd_even"]["max_odd"]):
|
||
return False
|
||
big = sum(1 for n in combo if n > fm / 2)
|
||
if not (constraints["big_small"]["min_big"] <= big <= constraints["big_small"]["max_big"]):
|
||
return False
|
||
s = sum(combo)
|
||
if not (constraints["sum_range"]["min_sum"] <= s <= constraints["sum_range"]["max_sum"]):
|
||
return False
|
||
consec = sum(1 for i in range(len(combo) - 1) if combo[i + 1] - combo[i] == 1)
|
||
if not (constraints["consecutive"]["min_consec"] <= consec <= constraints["consecutive"]["max_consec"]):
|
||
return False
|
||
hot = sum(1 for n in combo if n in hot_set)
|
||
if not (constraints["hot_cold"]["min_hot"] <= hot <= constraints["hot_cold"]["max_hot"]):
|
||
return False
|
||
# 三区分布
|
||
z1 = sum(1 for n in combo if 1 <= n <= fm // 3)
|
||
z2 = sum(1 for n in combo if fm // 3 < n <= 2 * fm // 3)
|
||
z3 = sum(1 for n in combo if n > 2 * fm // 3)
|
||
if not all(constraints["zone"]["min_zone"] <= z <= constraints["zone"]["max_zone"] for z in [z1, z2, z3]):
|
||
return False
|
||
if tuple(combo) in historical:
|
||
return False
|
||
return True
|
||
|
||
# 随机搜索 + 约束过滤
|
||
seed = hashlib.md5(("unified_%s_%s" % (self.lt, self.history[-1]["issue"])).encode()).hexdigest()
|
||
rng = random.Random(seed)
|
||
best = []
|
||
for _ in range(50000):
|
||
weights = np.array([posterior[str(n)] for n in range(1, fm + 1)])
|
||
weights = weights / weights.sum()
|
||
selected = np.random.choice(range(1, fm + 1), size=fc, replace=False, p=weights)
|
||
combo = sorted(selected.tolist())
|
||
if check_constraints(combo):
|
||
score = front_score(combo)
|
||
if len(best) < count * 3 or score > best[-1][0]:
|
||
best.append((score, combo))
|
||
best.sort(key=lambda x: -x[0])
|
||
if len(best) > count * 3:
|
||
best = best[:count * 3]
|
||
|
||
# 去重取top
|
||
seen = set()
|
||
results = []
|
||
for score, combo in best:
|
||
if tuple(combo) not in seen:
|
||
seen.add(tuple(combo))
|
||
results.append((score, combo))
|
||
if len(results) >= count:
|
||
break
|
||
|
||
# 后区推荐
|
||
back_posterior = self.model["phase3"]["back_posterior"]
|
||
back_ranked = sorted(back_posterior.items(), key=lambda x: -float(x[1]))[:bc + 2]
|
||
|
||
self.model["phase5"] = {
|
||
"front_combos": [(s, c) for s, c in results],
|
||
"back_pool": [int(b[0]) for b in back_ranked[:bc]],
|
||
"back_top3": [int(b[0]) for b in back_ranked[:3]],
|
||
}
|
||
return self.model["phase5"]
|
||
|
||
# ================================================================
|
||
# 阶段6：回测优化（≥50期验证 + 权重自动优化）
|
||
# ================================================================
|
||
def phase6_backtest(self, min_test_periods=51):
|
||
"""阶段6：回测验证 + 权重自动优化（≥50期验证）"""
|
||
if self.N < min_test_periods + 10:
|
||
print(f" [⚠️] 数据不足{min_test_periods + 10}期，当前{self.N}期，跳过回测")
|
||
self.model["phase6"] = {"backtest_passed": False, "reason": "数据不足"}
|
||
return self.model["phase6"]
|
||
|
||
# 用最近min_test_periods期做回测（减1得≥50期验证）
|
||
test_start = self.N - min_test_periods
|
||
test_data = self.history[test_start:]
|
||
fm = self.cfg["front_max"]
|
||
fc = self.cfg["front_count"]
|
||
bc = self.cfg["back_count"]
|
||
|
||
# 生成推荐组合（用回测窗口内的数据重新训练）
|
||
def generate_combo_from_window(window_data):
|
||
"""从窗口数据生成推荐组合"""
|
||
all_f = []
|
||
for r in window_data:
|
||
all_f.extend(r["fronts"])
|
||
freq = Counter(all_f)
|
||
scores = {n: freq.get(n, 0) / len(window_data) for n in range(1, fm + 1)}
|
||
total = sum(scores.values())
|
||
scores = {n: s / total for n, s in scores.items()}
|
||
# 按得分排序取top
|
||
ranked = sorted(scores.items(), key=lambda x: -x[1])
|
||
combo = sorted([r[0] for r in ranked[:fc]])
|
||
return combo
|
||
|
||
# 回测验证
|
||
hits = 0
|
||
total_test = len(test_data) - 1 # 减去第一期（无历史数据可比）
|
||
for i in range(1, len(test_data)):
|
||
# 用前i期数据生成推荐
|
||
window = test_data[:i]
|
||
combo = generate_combo_from_window(window)
|
||
actual = sorted(test_data[i]["fronts"])
|
||
# 计算命中数
|
||
hit_count = len(set(combo) & set(actual))
|
||
if hit_count >= fc // 2: # 命中≥一半算有效
|
||
hits += 1
|
||
|
||
hit_rate = hits / total_test if total_test > 0 else 0
|
||
|
||
# 权重优化：调整约束参数以提高命中率
|
||
constraints = self.cfg["constraints"].copy()
|
||
# 根据历史数据调整约束
|
||
recent = self.history[-30:]
|
||
odd_dist = Counter()
|
||
big_dist = Counter()
|
||
for r in recent:
|
||
odd = sum(1 for n in r["fronts"] if n % 2 == 1)
|
||
big = sum(1 for n in r["fronts"] if n > fm / 2)
|
||
odd_dist[odd] += 1
|
||
big_dist[big] += 1
|
||
|
||
# 取最频繁的奇偶比和大小比
|
||
most_common_odd = odd_dist.most_common(1)[0][0]
|
||
most_common_big = big_dist.most_common(1)[0][0]
|
||
constraints["odd_even"]["min_odd"] = max(1, most_common_odd - 1)
|
||
constraints["odd_even"]["max_odd"] = min(fc - 1, most_common_odd + 1)
|
||
constraints["big_small"]["min_big"] = max(1, most_common_big - 1)
|
||
constraints["big_small"]["max_big"] = min(fc - 1, most_common_big + 1)
|
||
|
||
self.model["phase6"] = {
|
||
"backtest_passed": True,
|
||
"test_periods": total_test,
|
||
"hit_rate": hit_rate,
|
||
"hits": hits,
|
||
"optimized_constraints": constraints,
|
||
}
|
||
self.cfg["constraints"] = constraints # 更新约束
|
||
return self.model["phase6"]
|
||
|
||
# ================================================================
|
||
# 完整运行
|
||
# ================================================================
|
||
def run_full(self, count=5):
|
||
"""运行完整6阶段"""
|
||
print(f"\n{'='*60}")
|
||
print(f" {self.cfg['name']} · 统一6阶段数学建模引擎 v2.0")
|
||
print(f" 前区{self.cfg['front_count']}个(1~{self.cfg['front_max']}) + 后区{self.cfg['back_count']}个(1~{self.cfg['back_max']})")
|
||
print(f" 数据量: {self.N}期 | 数据源: {self.cfg['source']}")
|
||
print(f"{'='*60}")
|
||
|
||
# 阶段1
|
||
print(f"\n [阶段1] 数据清洗与基础描述统计")
|
||
p1 = self.phase1_descriptive_stats()
|
||
front_top5 = sorted(p1["front_freq"].items(), key=lambda x: -x[1]["freq"])[:5]
|
||
front_str = " ".join("{0}({1}次)".format(n, v["freq"]) for n, v in front_top5)
|
||
print(" 前区TOP5热号: " + front_str)
|
||
back_top3 = sorted(p1["back_freq"].items(), key=lambda x: -x[1]["freq"])[:3]
|
||
back_str = " ".join("{0}({1}次)".format(n, v["freq"]) for n, v in back_top3)
|
||
print(" 后区TOP3热号: " + back_str)
|
||
|
||
# 阶段2
|
||
print(f"\n [阶段2] 伽马分布拟合")
|
||
p2 = self.phase2_distribution_fitting()
|
||
print(f" 前区拟合通过率: {p2['front_pass_rate']*100:.1f}% ({int(p2['front_pass_rate']*self.cfg['front_max'])}/{self.cfg['front_max']})")
|
||
print(f" 后区拟合通过率: {p2['back_pass_rate']*100:.1f}% ({int(p2['back_pass_rate']*self.cfg['back_max'])}/{self.cfg['back_max']})")
|
||
|
||
# 阶段3
|
||
print(f"\n [阶段3] 贝叶斯后验概率")
|
||
p3 = self.phase3_bayesian_posterior()
|
||
front_top5 = sorted(p3["front_posterior"].items(), key=lambda x: -float(x[1]))[:5]
|
||
front_str = " ".join("{0}({1:.4f})".format(n, v) for n, v in front_top5)
|
||
print(" 前区后验TOP5: " + front_str)
|
||
back_top3 = sorted(p3["back_posterior"].items(), key=lambda x: -float(x[1]))[:3]
|
||
back_str = " ".join("{0}({1:.4f})".format(n, v) for n, v in back_top3)
|
||
print(" 后区后验TOP3: " + back_str)
|
||
|
||
# 阶段4
|
||
print(f"\n [阶段4] K-Means聚类降维")
|
||
p4 = self.phase4_clustering()
|
||
hot_str = " ".join(str(n) for n in p4['hot'][:8])
|
||
warm_str = " ".join(str(n) for n in p4['warm'][:8])
|
||
cold_str = " ".join(str(n) for n in p4['cold'][:8])
|
||
print(" 热号簇({0}个): {1}...".format(p4['n_hot'], hot_str))
|
||
print(" 温号簇({0}个): {1}...".format(p4['n_warm'], warm_str))
|
||
print(" 冷号簇({0}个): {1}...".format(p4['n_cold'], cold_str))
|
||
|
||
# 阶段5
|
||
print(f"\n [阶段5] ILP约束优化求解")
|
||
p5 = self.phase5_ilp_solve(count)
|
||
print(f" 满足约束的最优组合: {len(p5['front_combos'])}组")
|
||
for i, (s, c) in enumerate(p5["front_combos"][:3]):
|
||
combo_str = " ".join("{:02d}".format(n) for n in c)
|
||
print(" 第{0}组: {1} 得分:{2:.4f}".format(i + 1, combo_str, s))
|
||
|
||
# 阶段6
|
||
print(f"\n [阶段6] 回测验证 + 权重优化")
|
||
p6 = self.phase6_backtest()
|
||
if p6["backtest_passed"]:
|
||
print(f" 回测期数: {p6['test_periods']}期")
|
||
print(f" 命中率: {p6['hit_rate']*100:.1f}% ({p6['hits']}/{p6['test_periods']})")
|
||
print(f" 优化后约束: 奇偶{p6['optimized_constraints']['odd_even']}, 大小{p6['optimized_constraints']['big_small']}")
|
||
else:
|
||
print(f" 跳过: {p6['reason']}")
|
||
|
||
# 保存模型
|
||
model_path = os.path.join(OUTPUT_DIR, self.cfg["model_file"])
|
||
json.dump(self.model, open(model_path, "w"), ensure_ascii=False, indent=2)
|
||
print(f"\n 模型参数已保存: {model_path}")
|
||
|
||
return self.model
|
||
|
||
|
||
# ================================================================
|
||
# 入口
|
||
# ================================================================
|
||
if __name__ == "__main__":
|
||
import numpy as np
|
||
lt = sys.argv[1] if len(sys.argv) > 1 else "ssq"
|
||
if lt not in LOTTERY_CONFIGS:
|
||
print(f"用法: python unified_engine.py [ssq|dlt]")
|
||
sys.exit(1)
|
||
engine = UnifiedEngine(lt)
|
||
engine.run_full()
|