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c4bb8632ca
- 新增 model_full_report_dlt.py: 大乐透专用全量报告脚本 - 统一4块结构: 回滚迭代→数据采集→推理分析→机选号码 - 统一术语: 前区/后区(大乐透) 对齐 红球/蓝球(双色球) - 统一评分: TOP10前区+TOP5后区 归一化概率输出 - 更新 SKILL.md: 双色球/大乐透统一模板+文件结构
260 lines
11 KiB
Python
260 lines
11 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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大乐透全量报告：与双色球 model_full_report.py 统一4块结构化输出
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使用集成学习模型（5模型融合），输出格式与双色球完全对齐
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"""
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import json, math, os, datetime, hashlib, random, sys, io
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from collections import Counter
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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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DLT_FILE = os.path.join(DATA_DIR, "dlt_history.json")
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FEEDBACK_FILE = os.path.join(DATA_DIR, "feedback.json")
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CONFIG_FILE = os.path.join(DATA_DIR, "config.json")
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# ================================================================
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# 加载数据
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# ================================================================
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data = json.load(open(DLT_FILE, "r", encoding="utf-8"))
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records = []
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historical_combos = set()
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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) == 5 and all(1 <= n <= 35 for n in fronts) and len(backs) == 2 and all(1 <= n <= 12 for n in backs):
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records.append({"issue": r["issue"], "fronts": fronts, "backs": backs, "date": r.get("date", "")})
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historical_combos.add(tuple(fronts))
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records.sort(key=lambda x: int(x["issue"]))
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N = len(records)
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latest = records[-1]
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# ================================================================
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# 加载回滚统计
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# ================================================================
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config = json.load(open(CONFIG_FILE, "r")) if os.path.exists(CONFIG_FILE) else {"iteration_count": 0}
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feedback = json.load(open(FEEDBACK_FILE, "r")) if os.path.exists(FEEDBACK_FILE) else {
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"iterations": [], "accuracy_history": [],
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"ssq": {"total": 0, "correct_reds": 0, "correct_blues": 0, "best": 0, "profitable_wins": 0, "total_earnings": 0.0},
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"dlt": {"total": 0, "correct_reds": 0, "correct_blues": 0, "best": 0, "profitable_wins": 0, "total_earnings": 0.0}
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}
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# ================================================================
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# 计算下期信息
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# ================================================================
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prev_issue = latest["issue"]
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prev_date = latest.get("date", "")
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prev_fronts = latest["fronts"]
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prev_backs = latest["backs"]
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next_issue = str(int(prev_issue) + 1)
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today = datetime.datetime.now()
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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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draw_hour, draw_min = map(int, draw_time.split(":"))
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now_hour, now_min = today.hour, today.minute
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today_draw = today.isoweekday() in draw_days
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time_past = (now_hour > draw_hour) or (now_hour == draw_hour and now_min >= draw_min)
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if today_draw and not time_past:
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delta = 0
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else:
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delta = 1
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while (today + datetime.timedelta(days=delta)).isoweekday() not in draw_days:
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delta += 1
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next_draw = today + datetime.timedelta(days=delta)
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next_draw_str = next_draw.strftime("%Y-%m-%d")
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wd_map = {1:"周一",2:"周二",3:"周三",4:"周四",5:"周五",6:"周六",7:"周日"}
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next_wd = wd_map.get(next_draw.isoweekday(), "")
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# ================================================================
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# 基础统计分析（与双色球贝叶斯后验对齐）
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# ================================================================
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# 前区频率统计
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all_fronts = []
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for r in records:
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all_fronts.extend(r["fronts"])
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front_freq = Counter(all_fronts)
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total_fronts = len(all_fronts)
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front_probs = {n: front_freq.get(n, 0) / total_fronts for n in range(1, 36)}
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# 后区频率统计
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all_backs = []
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for r in records:
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all_backs.extend(r["backs"])
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back_freq = Counter(all_backs)
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total_backs = len(all_backs)
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back_probs = {n: back_freq.get(n, 0) / total_backs for n in range(1, 13)}
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# 前区遗漏统计
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front_last_seen = {n: 0 for n in range(1, 36)}
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for i, r in enumerate(reversed(records)):
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for n in r["fronts"]:
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if front_last_seen[n] == 0:
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front_last_seen[n] = i
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# 后区遗漏统计
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back_last_seen = {n: 0 for n in range(1, 13)}
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for i, r in enumerate(reversed(records)):
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for n in r["backs"]:
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if back_last_seen[n] == 0:
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back_last_seen[n] = i
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# 综合评分 = 频率权重 × 0.4 + 遗漏回补 × 0.3 + 近期热度 × 0.3
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front_scores = {}
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for n in range(1, 36):
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freq_score = front_probs.get(n, 0)
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miss_score = min(1.0, front_last_seen[n] / 20) # 遗漏越大，回补概率越高
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# 近期热度：最近10期出现次数
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recent_count = sum(1 for r in records[-10:] if n in r["fronts"])
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hot_score = recent_count / 10
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front_scores[n] = 0.4 * freq_score + 0.3 * miss_score + 0.3 * hot_score
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back_scores = {}
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for n in range(1, 13):
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freq_score = back_probs.get(n, 0)
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miss_score = min(1.0, back_last_seen[n] / 15)
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recent_count = sum(1 for r in records[-10:] if n in r["backs"])
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hot_score = recent_count / 10
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back_scores[n] = 0.4 * freq_score + 0.3 * miss_score + 0.3 * hot_score
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# 归一化
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total_front_score = sum(front_scores.values())
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front_probs_norm = {n: s / total_front_score for n, s in front_scores.items()}
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total_back_score = sum(back_scores.values())
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back_probs_norm = {n: s / total_back_score for n, s in back_scores.items()}
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# ================================================================
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# 加载集成学习模块（抑制中间输出）
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# ================================================================
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sys.path.insert(0, BASE_DIR)
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HAS_ENSEMBLE = False
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ensemble_result = None
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try:
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# 临时重定向stdout以抑制集成学习的中间输出
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old_stdout = sys.stdout
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sys.stdout = io.StringIO()
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from ensemble_learning import ensemble_recommendation
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rule = {"name": "大乐透", "red_max": 35, "red_count": 5, "blue_max": 12, "blue_count": 2,
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"draw_days": [1, 3, 6], "draw_time": "20:25", "source": "中国体育彩票"}
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sorted_hist = sorted(records, key=lambda x: int(x["issue"]), reverse=True)[:100]
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hist_for_ensemble = [{"issue": r["issue"], "reds": r["fronts"], "blue": r["backs"], "date": r.get("date", "")} for r in sorted_hist]
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ensemble_result = ensemble_recommendation("dlt", hist_for_ensemble, rule, count=5)
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sys.stdout = old_stdout
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if ensemble_result and ensemble_result.get("recommendations"):
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HAS_ENSEMBLE = True
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except Exception as e:
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sys.stdout = old_stdout
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pass
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# ================================================================
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# 4块输出（与双色球统一格式）
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# ================================================================
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print("\n" + "=" * 60)
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print(" [1/4] 回滚迭代")
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print("=" * 60)
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ver = config.get("iteration_count", 0)
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stats = feedback.get("dlt", {})
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total_recs = stats.get("total", 0)
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profitable = stats.get("profitable_wins", 0)
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win_rate = "%.1f%%" % (profitable / total_recs * 100) if total_recs > 0 else "0.0%"
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earnings = stats.get("total_earnings", 0.0)
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print(" 当前版本号: v%d (集成学习模型)" % ver)
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print(" 版本胜率: %s (%d期推荐 / %d次盈利)" % (win_rate, total_recs, profitable))
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print(" 累计中奖收益金额: RMB %.2f" % earnings)
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# ==================== 第2块：数据采集 ====================
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print("\n" + "=" * 60)
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print(" [2/4] 数据采集")
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print("=" * 60)
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print(" 采集期数范围: 第%s期 ~ 第%s期（%d期）" % (records[0]["issue"], prev_issue, N))
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print(" 上一期号: 第%s期" % prev_issue)
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print(" 开奖日期: %s" % prev_date)
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f_str = " ".join("%02d" % n for n in prev_fronts)
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b_str = " ".join("%02d" % n for n in prev_backs)
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print(" 开奖号码: 前区 %s 后区 %s" % (f_str, b_str))
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# ==================== 第3块：推理分析 ====================
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print("\n" + "=" * 60)
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print(" [3/4] 推理分析（集成学习模型 | 数据源: 中国体育彩票）")
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print("=" * 60)
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# 步骤1: 前区频率分析 TOP10（与双色球"贝叶斯后验概率 TOP10红球"对齐）
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top10_front = sorted(front_probs_norm.items(), key=lambda x: -x[1])[:10]
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print(" >> 步骤1 - 频率分析（前区TOP10）")
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print(" %s" % " ".join("%02d(%.4f)" % (n, s) for n, s in top10_front))
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# 步骤2: 后区分析 TOP5
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top5_back = sorted(back_probs_norm.items(), key=lambda x: -x[1])[:5]
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print(" >> 步骤2 - 后区分析（TOP5）")
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print(" %s" % " ".join("%02d(%.4f)" % (n, s) for n, s in top5_back))
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# 步骤3: 模式识别
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print(" >> 步骤3 - 模式识别")
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# 统计历史奇偶比和大小比
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oe_counter = Counter()
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bs_counter = Counter()
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for r in records[-30:]:
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odd = sum(1 for n in r["fronts"] if n % 2 == 1)
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even = 5 - odd
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oe_counter[f"{odd}:{even}"] += 1
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big = sum(1 for n in r["fronts"] if n > 17)
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small = 5 - big
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bs_counter[f"{big}:{small}"] += 1
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most_oe = oe_counter.most_common(1)[0][0] if oe_counter else "3:2"
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most_bs = bs_counter.most_common(1)[0][0] if bs_counter else "3:2"
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sum_min = min(r["fronts"][0] + r["fronts"][1] for r in records[-30:])
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sum_max = max(sum(r["fronts"]) for r in records[-30:])
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print(" 奇偶比: %s | 大小比: %s | 和值区间: [%d, %d]" % (most_oe, most_bs, sum_min, sum_max))
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# 步骤4: 权重体系
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print(" >> 步骤4 - 权重体系（集成学习模型）")
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if HAS_ENSEMBLE and ensemble_result.get("weights"):
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weights = ensemble_result["weights"]
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name_map = {'freq': '频率加权', 'bayes': '贝叶斯平滑', 'markov': '马尔可夫链',
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'kmeans': 'K-Means聚类', 'residual': '残差回归'}
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weight_parts = []
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for m, w in sorted(weights.items(), key=lambda x: -x[1]):
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weight_parts.append("%s%.2f%%" % (name_map.get(m, m), w * 100))
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print(" %s" % " + ".join(weight_parts))
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else:
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print(" 频率40% + 遗漏30% + 热号30%")
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# ==================== 第4块：机选号码 ====================
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print("\n" + "=" * 60)
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print(" [4/4] 机选号码（第%s期 | %s %s）" % (next_issue, next_draw_str, next_wd))
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print("=" * 60)
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if HAS_ENSEMBLE and ensemble_result.get("recommendations"):
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recommendations = ensemble_result["recommendations"]
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for i, r in enumerate(recommendations):
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f_str = " ".join("%02d" % n for n in r["reds"])
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b_str = " ".join("%02d" % n for n in r["blues"])
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marker = " ⭐" if i == 0 else ""
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print("\n 第%d组: 前区 %s 后区 %s%s" % (i + 1, f_str, b_str, marker))
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print(" 得分:%.4f | 奇偶:%s | 大小:%s | 和:%d" % (
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r["score"], r["odd_even"], r["big_small"], r["sum"]))
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# 保存推荐
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analysis_file = os.path.join(DATA_DIR, "analysis_dlt.json")
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analysis = json.load(open(analysis_file, "r")) if os.path.exists(analysis_file) else {}
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analysis["last_recommendation"] = {
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"fronts": recommendations[0]["reds"],
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"backs": recommendations[0]["blues"],
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"generated_at": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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"model_version": "ensemble_v7"
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}
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json.dump(analysis, open(analysis_file, "w"), ensure_ascii=False, indent=2)
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else:
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print(" [⚠️ 无法生成推荐，请检查数据]")
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print("\n" + "=" * 60)
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print(" ⚠️ 免责声明: 本推荐仅基于历史统计建模的相对概率评分，")
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print(" 不预测下期开奖，不保证中奖。彩票是独立随机事件。")
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print("=" * 60)
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print("\n 开奖后把实际号码发我，帮你回滚迭代校正精度 🍀")
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