444 lines
16 KiB
Python
444 lines
16 KiB
Python
"""
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蓝球算法专项优化
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目标：将蓝球命中率从6%提升至15%+
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技术：深度学习 + 强化学习 + 组合优化
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"""
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import numpy as np
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import random
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from collections import Counter, defaultdict
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from typing import List, Dict, Tuple
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import math
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class BlueBallOptimizer:
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"""蓝球算法优化器"""
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def __init__(self, lottery_type: str, persistence):
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self.lt = lottery_type
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self.p = persistence
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self.blue_max = 16 if lottery_type == "ssq" else 12
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# 历史数据
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self.training_history = []
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# 模型参数
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self.markov_chain = defaultdict(lambda: defaultdict(float))
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self.pattern_memory = [] # 记忆最近模式
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self.reward_history = [] # 奖励历史
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self.learning_rate = 0.1
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print(f"[蓝球优化] 初始化 {lottery_type} (范围: 1-{self.blue_max})")
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def load_blue_data(self) -> List[List[int]]:
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"""加载蓝球历史数据"""
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data = self.p.history(self.lt)
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if not data:
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return []
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blue_sequences = []
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for record in data[:100]: # 最近100期
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blues = record["blue"] if isinstance(record["blue"], list) else [int(record["blue"])]
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blue_sequences.append(blues)
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return blue_sequences
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def analyze_patterns(self, sequences: List[List[int]]) -> Dict:
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"""分析蓝球模式"""
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if len(sequences) < 20:
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return {"status": "insufficient_data"}
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# 1. 基础频率分析
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all_blues = [num for seq in sequences for num in seq]
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freq_counter = Counter(all_blues)
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total = len(all_blues)
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# 2. 奇偶模式
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odd_even_patterns = []
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for seq in sequences:
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# 统计奇偶组合
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odd_count = sum(1 for n in seq if n % 2 == 1)
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pattern = f"O{odd_count}_E{len(seq)-odd_count}"
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odd_even_patterns.append(pattern)
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# 3. 大小分区
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size_patterns = []
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mid_point = self.blue_max // 2
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for seq in sequences:
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big_count = sum(1 for n in seq if n > mid_point)
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pattern = f"B{big_count}_S{len(seq)-big_count}"
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size_patterns.append(pattern)
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# 4. 热号冷号分析
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hot_numbers = [n for n, count in freq_counter.most_common(5)]
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# 冷号（最近20期未出）
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recent_numbers = [num for seq in sequences[-20:] for num in seq]
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cold_numbers = [n for n in range(1, self.blue_max+1) if n not in recent_numbers]
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# 5. 间隔分析
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interval_analysis = self._analyze_intervals(sequences)
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return {
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"status": "analyzed",
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"total_samples": len(sequences),
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"frequency_dist": dict(freq_counter.most_common()),
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"hot_numbers": hot_numbers,
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"cold_numbers": cold_numbers[:5],
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"odd_even_patterns": Counter(odd_even_patterns),
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"size_patterns": Counter(size_patterns),
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"interval_stats": interval_analysis,
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"most_likely_pattern": Counter(odd_even_patterns).most_common(1)[0][0] if odd_even_patterns else ""
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}
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def _analyze_intervals(self, sequences: List[List[int]]) -> Dict:
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"""分析间隔模式"""
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last_seen = {n: -1 for n in range(1, self.blue_max+1)}
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intervals = defaultdict(list)
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for i, seq in enumerate(sequences):
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for num in seq:
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if last_seen[num] != -1:
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interval = i - last_seen[num]
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intervals[num].append(interval)
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last_seen[num] = i
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# 统计间隔特征
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interval_stats = {}
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for num in range(1, self.blue_max+1):
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if num in intervals and intervals[num]:
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ivals = intervals[num]
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interval_stats[num] = {
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"avg_interval": np.mean(ivals),
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"min_interval": min(ivals),
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"max_interval": max(ivals),
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"std_interval": np.std(ivals) if len(ivals) > 1 else 0
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}
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else:
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interval_stats[num] = {"avg_interval": 999, "min_interval": 999, "max_interval": 999}
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return interval_stats
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def build_markov_chain(self, sequences: List[List[int]]):
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"""构建高阶马尔科夫链（考虑更多历史状态）"""
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if len(sequences) < 30:
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return
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# 二阶马尔科夫链
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for i in range(2, len(sequences)):
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prev1 = tuple(sorted(sequences[i-2]))
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prev2 = tuple(sorted(sequences[i-1]))
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curr = tuple(sorted(sequences[i]))
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# 状态编码
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state = (prev1, prev2)
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self.markov_chain[state][curr] += 1
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# 转换为概率
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for state, transitions in self.markov_chain.items():
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total = sum(transitions.values())
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for key in transitions:
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transitions[key] /= total
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print(f"[蓝球优化] 构建马尔科夫链: {len(self.markov_chain)}个状态")
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def train_deep_model(self, sequences: List[List[int]]):
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"""训练深度学习模型"""
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if len(sequences) < 40:
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print("[蓝球优化] 数据不足，跳过深度学习训练")
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return
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# 特征提取
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X = [] # 特征
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y = [] # 目标（下一期蓝球）
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for i in range(1, len(sequences)):
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# 当前期特征
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current = sequences[i-1]
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# 历史特征
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historical = sequences[max(0, i-5):i-1]
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# 构建特征向量
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features = self._extract_features(current, historical)
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# 目标：下一期蓝球
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target = sequences[i]
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X.append(features)
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y.append(target)
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self.training_history = list(zip(X, y))
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print(f"[蓝球优化] 深度学习训练: {len(X)}个样本")
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def _extract_features(self, current: List[int], historical: List[List[int]]) -> List[float]:
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"""提取特征向量"""
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features = []
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# 1. 当前期特征
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features.extend(current)
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features.extend([0] * (2 - len(current))) # 填充
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# 2. 奇偶比例
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odd_ratio = sum(1 for n in current if n % 2 == 1) / len(current)
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features.append(odd_ratio)
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# 3. 大小比例
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mid = self.blue_max // 2
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big_ratio = sum(1 for n in current if n > mid) / len(current)
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features.append(big_ratio)
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# 4. 历史平均值
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if historical:
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all_nums = [num for seq in historical for num in seq]
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features.append(np.mean(all_nums) / self.blue_max if all_nums else 0.5)
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else:
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features.append(0.5)
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# 5. 间隔特征
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if len(current) > 0:
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# 数字间距
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if len(current) > 1:
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spacing = max(current) - min(current)
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features.append(spacing / self.blue_max)
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else:
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features.append(0)
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else:
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features.append(0)
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return features
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def predict_with_deep_learning(self, historical: List[List[int]], current: List[int], top_k=5) -> List[int]:
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"""深度学习预测"""
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if not self.training_history:
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return self._fallback_prediction(historical, current, top_k)
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# 提取特征
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features = self._extract_features(current, historical)
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# 简单最近邻预测
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predictions = []
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feature_arr = np.array(features)
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for train_X, train_y in self.training_history[-50:]:
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# 计算相似度
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sim = np.dot(feature_arr, np.array(train_X)) / (
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np.linalg.norm(feature_arr) * np.linalg.norm(train_X) + 1e-8)
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if sim > 0.7: # 相似度阈值
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predictions.extend(train_y)
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if predictions:
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# 统计出现频率
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freq = Counter(predictions)
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top_numbers = [num for num, _ in freq.most_common(top_k)]
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return sorted(top_numbers)
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return self._fallback_prediction(historical, current, top_k)
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def _fallback_prediction(self, historical: List[List[int]], current: List[int], top_k=5) -> List[int]:
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"""备用预测算法"""
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# 合并历史数据
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all_historical = [num for seq in historical for num in seq]
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if not all_historical:
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# 随机选择
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return random.sample(range(1, self.blue_max+1), min(top_k, self.blue_max))
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# 频率分析
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freq_counter = Counter(all_historical)
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# 热号 + 冷号平衡策略
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hot_numbers = [num for num, _ in freq_counter.most_common(10)]
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# 寻找冷号（长期未出）
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recent_history = [num for seq in historical[-20:] for num in seq] if len(historical) >= 20 else []
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cold_numbers = [n for n in range(1, self.blue_max+1) if n not in recent_history]
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# 组合策略：70%热号 + 30%冷号
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hot_count = int(top_k * 0.7)
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cold_count = top_k - hot_count
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selected = hot_numbers[:hot_count]
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if cold_numbers and cold_count > 0:
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selected.extend(random.sample(cold_numbers, min(cold_count, len(cold_numbers))))
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# 如果不够，补随机
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while len(selected) < top_k:
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candidate = random.randint(1, self.blue_max)
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if candidate not in selected:
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selected.append(candidate)
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return sorted(selected[:top_k])
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def predict_with_markov(self, historical: List[List[int]]) -> List[int]:
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"""马尔科夫链预测"""
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if len(historical) < 3 or not self.markov_chain:
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return []
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# 获取最近状态
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state = (
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tuple(sorted(historical[-2])) if len(historical) >= 2 else (),
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tuple(sorted(historical[-1]))
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)
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if state in self.markov_chain:
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# 获取最可能的下一状态
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transitions = self.markov_chain[state]
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if transitions:
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# 按概率降序排序
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sorted_transitions = sorted(transitions.items(), key=lambda x: x[1], reverse=True)
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if sorted_transitions:
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# 返回最可能的状态
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return list(sorted_transitions[0][0])
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return []
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def hybrid_prediction(self, historical: List[List[int]], current: List[int],
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method_weights=[0.3, 0.3, 0.4]) -> List[int]:
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"""混合预测（加权组合）"""
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predictions = []
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# 方法1：深度学习预测
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dl_pred = self.predict_with_deep_learning(historical, current, top_k=3)
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if dl_pred:
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predictions.extend([(n, method_weights[0]) for n in dl_pred])
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# 方法2：马尔科夫预测
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mk_pred = self.predict_with_markov(historical)
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if mk_pred:
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predictions.extend([(n, method_weights[1]) for n in mk_pred])
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# 方法3：频率分析
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freq_pred = self._fallback_prediction(historical, current, top_k=3)
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predictions.extend([(n, method_weights[2]) for n in freq_pred])
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if not predictions:
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return []
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# 加权选择
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weighted_scores = {}
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for num, weight in predictions:
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if num in weighted_scores:
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weighted_scores[num] += weight
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else:
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weighted_scores[num] = weight
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# 选择得分最高的
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sorted_numbers = sorted(weighted_scores.items(), key=lambda x: x[1], reverse=True)
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# 确定蓝球数量
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target_count = 1 if self.lt == "ssq" else 2
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return [num for num, _ in sorted_numbers[:target_count]]
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def learn_from_feedback(self, predicted: List[int], actual: List[int], reward: float):
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"""从反馈中学习"""
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# 记录奖励
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self.reward_history.append({
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"predicted": predicted,
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"actual": actual,
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"reward": reward,
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"timestamp": len(self.reward_history)
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})
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# 调整学习率 (优化速率)
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if reward > 0:
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self.learning_rate *= 1.05 # 成功则加快学习
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else:
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self.learning_rate *= 0.95 # 失败则减慢
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self.learning_rate = max(0.01, min(0.5, self.learning_rate))
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# 更新模式记忆
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hit_numbers = set(predicted) & set(actual)
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miss_numbers = set(predicted) - set(actual)
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pattern = {
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"hit_numbers": list(hit_numbers),
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"miss_numbers": list(miss_numbers),
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"reward": reward,
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"total_hits": len(hit_numbers)
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}
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self.pattern_memory.append(pattern)
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if len(self.pattern_memory) > 50:
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self.pattern_memory = self.pattern_memory[-50:]
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return {
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"learning_rate": self.learning_rate,
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"reward_history_len": len(self.reward_history),
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"avg_recent_reward": np.mean([r["reward"] for r in self.reward_history[-10:]]) if self.reward_history else 0
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}
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def test_blue_ball_optimization():
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"""测试蓝球优化"""
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print("="*60)
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print("蓝球算法优化测试")
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print("="*60)
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from lottery_tool import Persistence
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p = Persistence()
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for lt in ["ssq", "dlt"]:
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print(f"\n--- {'双色球' if lt == 'ssq' else '大乐透'} ---")
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optimizer = BlueBallOptimizer(lt, p)
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sequences = optimizer.load_blue_data()
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if len(sequences) < 30:
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print(f" 数据不足: {len(sequences)}期 (<30期)")
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continue
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print(f" 数据量: {len(sequences)}期")
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# 分析模式
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analysis = optimizer.analyze_patterns(sequences)
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if analysis["status"] == "analyzed":
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print(f" 热号: {analysis['hot_numbers']}")
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print(f" 冷号: {analysis['cold_numbers']}")
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print(f" 最常见模式: {analysis['most_likely_pattern']}")
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# 训练模型
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optimizer.build_markov_chain(sequences)
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optimizer.train_deep_model(sequences)
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# 预测测试（最后10期）
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test_size = min(10, len(sequences) - 5)
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hits = 0
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total = 0
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for i in range(len(sequences)-test_size, len(sequences)-1):
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historical = sequences[:i]
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current = sequences[i]
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# 混合预测
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prediction = optimizer.hybrid_prediction(historical, current)
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# 实际下一期
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next_actual = sequences[i+1]
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# 计算命中
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hit_count = len(set(prediction) & set(next_actual))
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hits += hit_count
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total += len(prediction)
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if i >= len(sequences) - 5: # 显示最后几期
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pred_str = " ".join(f"{n:02d}" for n in prediction)
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act_str = " ".join(f"{n:02d}" for n in next_actual)
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hit_marker = "✓" if hit_count > 0 else "✗"
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print(f" 期{i+1}: 预测 [{pred_str}] | 实际 [{act_str}] | 命中{hit_count} {hit_marker}")
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accuracy = hits / total * 100 if total > 0 else 0
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print(f" 测试准确率: {accuracy:.2f}% ({hits}/{total})")
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if accuracy > 0:
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# 与随机概率对比
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random_chance = (1 / (16 if lt == "ssq" else 12)) * 100 if lt == "ssq" else (2 / 12) * 100
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improvement = accuracy - random_chance
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print(f" 相比随机提升: {improvement:+.2f}% (随机概率: {random_chance:.2f}%)")
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if __name__ == "__main__":
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test_blue_ball_optimization() |