快乐8历史数据统计与六码覆盖率计算及优化

快乐8历史六码覆盖率计算与优化：

快乐8选号计算：基于量子权重【基于趋势和权重】、热号和冷号策略的综合选号。

历史6码覆盖率：计算推荐号码在历史开奖数据中包含6个及以上号码的占比。

由于真正的量子计算复杂且要消耗计算机的资源，普通电脑无法完成，我们使用简单版本的，伪量子算法来直接计算快乐8里六码覆盖率。

import random
import math
import numpy as np

# 用于本地伪量子权重计算（替代 QAOA）
class LocalQuantumSimulator:
    def run(self, problem):
        # 这里只是一个占位符，随机生成一个权重
        return {
            'eigenstate': {f'x{num}': random.uniform(0, 1) for num in range(1, 81)}
        }

# 本地 QUBO 求解器（替代 Quantum Sampler）
class LocalQUBOSampler:
    def sample_qubo(self, qubo_matrix, **kwargs):
        # 随机选择一组伪解决方案
        sample = {i: random.choice([0, 1]) for i in range(80)}
        return [{'sample': sample}]

# 快乐8智能选号系统简单版
class SimpleSelector:
    def __init__(self, data):
        self.history = data  # 历史数据
        self.numbers = list(range(1, 81))  # 可能的号码范围

    def generate_features(self):
        """为每个号码生成特征权重"""
        features = []
        for num in self.numbers:
            frequency = sum(num in draw for draw in self.history)
            quantum_rank = self.trend_strength(num) * random.uniform(0.9, 1.1)  # 伪量子增强权重
            hot_score = frequency * 1.2
            cold_score = (100 - frequency) * 0.8
            features.append({
                'number': num,
                'frequency': frequency,
                'quantum_rank': quantum_rank,
                'hot_score': hot_score,
                'cold_score': cold_score
            })
        return features

    @staticmethod
    def trend_strength(num):
        """简单趋势强度计算（替代量子方法）"""
        # 此处的参数可以通过实际数据调整优化
        presence = random.uniform(0, 1)
        recency = random.uniform(0, 1)
        volatility = random.uniform(0, 0.2)
        return 0.6 * presence + 0.3 * recency - 0.1 * volatility

    def optimize_selection(self, features):
        """基于特征优化选号"""
        selected = []
        sorted_features = sorted(features, key=lambda x: -x['quantum_rank'])
        for feature in sorted_features:
            if len(selected) < 15:
                selected.append(feature['number'])
        return selected

    def hybrid_selection(self):
        """多策略融合选号"""
        features = self.generate_features()
        quantum_selected = self.optimize_selection(features)
        hot_numbers = [f['number'] for f in sorted(features, key=lambda x: -x['hot_score'])[:8]]
        cold_numbers = [f['number'] for f in sorted(features, key=lambda x: -x['cold_score'])[:2]]

        final_selection = list(set(quantum_selected + hot_numbers + cold_numbers))[:20]
        return sorted(final_selection)

# 测试与运行
if __name__ == "__main__":
    raw_data = [
        [1,5,12,13,15,17,18,28,29,34,39,44,55,60,62,64,66,67,70,77],
        [6,9,11,13,14,18,24,28,29,36,38,41,42,49,51,57,66,71,75,78],
        [2,12,16,17,23,25,26,27,34,45,51,54,56,58,60,64,67,70,72,77],
        [5,7,14,18,19,23,26,27,36,37,44,46,50,53,56,58,64,66,73,78],
        [3,9,11,14,15,18,20,21,25,26,28,40,43,50,54,66,72,76,77,78],
        [3,7,8,12,13,34,40,43,47,50,54,55,58,61,63,64,66,72,74,78],
        [11,12,16,19,22,26,27,36,40,42,45,46,48,49,55,65,69,72,74,76],
        [7,16,22,27,28,34,37,38,39,40,45,46,51,57,63,64,65,73,77,80],
        [3,5,9,13,20,23,28,31,32,36,42,43,44,49,63,66,68,69,75,76],
        [9,13,14,18,22,28,33,35,41,45,48,49,51,53,58,68,71,73,74,80]
    ]

    selector = SimpleSelector(raw_data)
    final_selection = selector.hybrid_selection()
    coverage_rate = sum(len(set(draw) & set(final_selection)) >= 6 for draw in raw_data) / len(raw_data)

    print(f"优化选号：{final_selection}")
    print(f"历史6码覆盖率：{coverage_rate * 100:.1f}%")

运行5次的结果如下：

A：1, 4, 5, 10, 13, 18, 22, 26, 28, 34, 36, 39, 50, 54, 58, 61, 64, 65, 66, 71

B:4, 6, 10, 13, 18, 26, 28, 30, 44, 48, 51, 52, 57, 58, 63, 64, 66, 69, 73, 75

C:4, 7, 9, 10, 13, 18, 25, 26, 28, 31, 32, 33, 38, 40, 42, 54, 55, 58, 64, 66

D:1, 4, 10, 11, 13, 14, 17, 18, 25, 26, 28, 33, 38, 41, 49, 50, 52, 57, 58, 64

E:2, 3, 4, 10, 13, 14, 18, 26, 28, 29, 36, 48, 49, 53, 57, 58, 64, 66, 68, 78

YISHANG以上只是纯粹的计算以后的结果，最近几期都没有4这个号，但是，代码计算过遗漏值，所以，它固执地每一个都选了4。

统计历史数据 仅供各位娱乐