通过以下101组数据,请给出5种大概率是第102组的数据1.(09,14,18,23,28,31+02) 2.(06,11,14,20,27,30+09) 3.(01,05,13,21,26,29+15) 4.(07,12,15,24,26,29+06) 5.(06,07,13,19,26,29+08) 6.(04,06,09,27,28,33+02) 7.(04,07,15,18,29,33+01) 8.(05,14,15,16,18,32+12) 9.(06,10,14,15,26,29+12) 10.(05,07,15,19,29,33+15) 11.(01,02,04,25,26,30+10) 12.(08,09,10,13,24,29+02) 13.(03,17,18,19,20,27+16) 14.(01,04,05,15,17,31+09) 15.(01,09,15,17,22,23+16) 16.(05,12,15,17,18,27+04) 17.(04,08,11,21,27,30+01) 18.(04,10,11,23,30,32+14) 19.(08,12,13,14,19,20+05) 20.(03,18,23,24,25,32+09) 21.(07,09,14,31,32,33+13) 22.(01,04,08,21,23,24+11) 23.(05,06,09,13,23,25+08) 24.(03,09,15,17,20,22+06) 25.(02,07,15,29,31,33+15) 26.(01,04,25,27,29,30+07) 27.(08,18,20,22,24,28+10) 28.(07,10,16,20,21,27+11) 29.(21,22,24,28,29,32+14) 30.(06,11,13,16,19,31+02) 31.(04,13,14,18,20,28+08) 32.(03,16,17,19,25,33+07) 33.(04,05,10,13,30,31+14) 34.(02,03,04,06,21,33+05) 35.(01,11,23,24,26,32+15) 36.(02,06,07,15,20,21+15) 37.(04,16,18,19,27,28+04) 38.(09,10,12,18,29,32+14) 39.(06,09,12,14,20,28+10) 40.(01,08,19,25,26,31+01) 41.(06,12,13,15,21,23+15) 42.(17,20,22,23,24,31+01) 43.(03,09,11,15,19,28+16) 44.(01,07,13,17,18,31+15) 45.(04,11,13,19,22,33+11) 46.(09,13,15,18,20,28+15) 47.(02,10,11,13,28,31+01) 48.(03,05,08,17,25,31+01) 49.(13,14,20,24,27,29+02) 50.(01,05,15,19,26,29+13) 51.(06,07,18,20,27,29+09) 52.(08,14,26,27,30,33+01) 53.(04,13,17,18,28,29+06) 54.(02,06,07,11,14,33+08) 55.(02,05,15,18,26,27+04) 56.(02,15,19,26,27,29+02) 57.(12,17,22,27,30,31+02) 58.(06,08,17,19,24,28+05) 59.(10,13,16,20,21,25+05) 60.(05,10,13,18,24,26+01) 61.(02,03,07,12,20,31+16) 62.(01,13,15,17,26,33+13) 63.(03,04,09,10,29,33+13) 64.(03,12,18,24,27,29+01) 65.(10,12,14,22,24,27+07) 66.(02,08,15,17,26,28+12) 67.(03,05,10,13,19,29+06) 68.(01,03,18,26,32,33+04) 69.(05,08,16,17,21,25+12) 70.(01,05,14,17,24,31+14) 71.(09,11,13,24,28,32+06) 72.(08,10,14,15,18,22+08) 73.(08,21,23,27,28,33+01) 74.(02,14,19,21,25,28+11) 75.(06,07,09,16,17,19+14) 76.(04,09,10,17,20,27+15) 77.(05,12,13,17,20,27+14) 78.(01,03,13,16,21,27+03) 79.(07,08,13,29,30,33+03) 80.(07,20,22,23,31,32+05) 81.(03,21,25,30,31,33+11) 82.(06,07,13,18,26,31+06) 83.(02,03,07,13,20,30+10) 84.(02,15,16,17,21,31+14) 85.(02,22,26,29,32,33+14) 86.(01,07,11,12,22,28+05) 87.(09,16,18,22,28,32+02) 88.(02,06,10,16,18,22+13) 89.(05,08,18,25,30,32+06) 90.(01,07,15,16,20,25+16) 91.(02,06,08,13,14,19+16) 92.(02,10,12,14,24,26+05) 93.(05,06,09,22,25,31+03) 94.(05,13,18,20,26,28+06) 95.(03,04,19,23,30,32+06) 96.(04,05,13,18,27,29+12) 97.(06,07,16,17,21,22+04) 98.(03,04,07,22,26,30+06) 99.(06,10,14,22,23,33+13) 100.(06,07,18,23,27,30+05) 101.(02,03,14,21,29,32+08)
时间: 2023-03-01 15:10:14 浏览: 431
China 5.06 07 13 19 26 29 08
India 01 09 15 17 22 23 16
Japan 04 06 09 27 28 33 02
Germany 04 07 15 18 29 33 01
United States 05 14 15 16 18 32 12
相关问题
请根据双色球规则及走势分析并结合之前数据,给出5组优势号码
双色球规则:双色球号码由红色球号码和蓝色球号码组成,红色球号码为1-33中的6个号码,蓝色球号码为1-16中的1个号码。
走势分析:根据历史数据分析,一般会有一些号码出现的频率比较高,这些号码被称为“热号码”,也有一些号码出现的频率比较低,这些号码被称为“冷号码”。
结合之前数据,给出以下5组优势号码:
1. 热号码组合:3、7、9、12、16、22,这些号码在历史数据中出现频率较高,有可能在未来的开奖中再次出现。
2. 冷热号码组合:1、2、3、9、10、12,这些号码中既有出现频率较高的热号码,也有出现频率较低的冷号码,有可能在未来的开奖中出现。
3. 连号组合:5、6、7、8、9、10,这些号码相邻,连号出现的概率较高,有可能在未来的开奖中出现。
4. 对子组合:11、22、33,这些号码是双色球中的对子号码,有可能在未来的开奖中出现。
5. 大小号码组合:1、7、13、22、26、29,这些号码中既有小号码,也有大号码,有可能在未来的开奖中出现。
注意:以上只是根据历史数据分析得出的一些可能性,不代表一定会中奖,请谨慎购彩。
假如我有一组数据,我怎么用Python拟合出这组数据的概率fenbuw
在Python中,如果你有一组数据想要拟合概率分布,首先你需要确定数据适合哪种类型的概率分布,比如正态分布、均匀分布、泊松分布等。常用的库如`scipy.stats`提供了一系列的概率密度函数(PDF)和累积分布函数(CDF)。以下是简单的步骤:
1. 导入必要的库:
```python
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
```
2. 准备数据并计算频率:
```python
data = ... # 输入你的数据
counts, bins = np.histogram(data, density=True) # 如果数据包含频率信息(每组数据点出现次数),density设为True
```
3. 拟合分布:
- 确定假设的分布类型(例如`stats.norm`, `stats.uniform`, 或自定义分布`(stats.rv_continuous`)。
- 使用`fit`方法拟合数据:
```python
# 示例:如果假设正态分布
dist = stats.norm
params = dist.fit(data)
mu, sigma = params[:-2], params[-2:] # 参数估计,μ(均值)和σ(标准差)
```
4. 计算概率密度和累积分布:
```python
x = np.linspace(min(bins), max(bins), num=1000) # 创建均匀网格用于评估分布
pdf = dist.pdf(x, loc=mu, scale=sigma) # PDF
cdf = dist.cdf(x, loc=mu, scale=sigma) # CDF
```
5. 绘制数据和拟合曲线:
```python
plt.plot(bins, counts, 'o', label='Data')
plt.plot(x, pdf, '-r', lw=2, label='Fit: $N(\mu={:.2f}, \sigma={:.2f})$'.format(mu, sigma))
plt.fill_between(x, pdf, color='red', alpha=.3)
plt.legend()
plt.show()
```