Python – 统计中的离散超几何分布

scipy.stats.hypergeom()是一个超几何离散随机变量。它作为rv_discrete 类的实例继承自泛型方法。它使用特定于此特定发行版的详细信息来完成方法。

参数：

x : quantiles
loc : [optional]location parameter. Default = 0
scale : [optional]scale parameter. Default = 1
moments : [optional] composed of letters [‘mvsk’]; ‘m’ = mean, ‘v’ = variance, ‘s’ = Fisher’s skew and ‘k’ = Fisher’s kurtosis. (default = ‘mv’).

Results : hypergeometric discrete random variable

编程需要懂一点英语

代码 #1：创建超几何离散随机变量

# importing library
  
from scipy.stats import hypergeom 
    
numargs = hypergeom .numargs 
a, b = 0.2, 0.8
rv = hypergeom (a, b) 
    
print ("RV : \n", rv)

输出：

RV : 
 scipy.stats._distn_infrastructure.rv_frozen object at 0x0000016A4C0DF048

代码#2：超几何离散变量和概率分布

import numpy as np 
quantile = np.arange (0.01, 1, 0.1) 
  
# Random Variates 
R = hypergeom .pmf(a, b, c, 10) 
print ("Random Variates : \n", R) 
  
# PDF 
x = np.linspace(hypergeom.ppf(0.01, a, b, c),
                hypergeom.ppf(0.99, a, b, c), 10)
R = hypergeom.ppf(x, 1, 3, 3)
print ("\nProbability Distribution : \n", R)

输出：

Random Variates : 
 nan

Probability Distribution : 
 [nan nan nan nan nan nan nan nan nan nan]

代码#3：图形表示。

import numpy as np 
import matplotlib.pyplot as plt 
     
distribution = np.linspace(0, np.minimum(rv.dist.b, 2)) 
print("Distribution : \n", distribution)

输出：

Distribution : 
 [0.         0.04081633 0.08163265 0.12244898 0.16326531 0.20408163
 0.24489796 0.28571429 0.32653061 0.36734694 0.40816327 0.44897959
 0.48979592 0.53061224 0.57142857 0.6122449  0.65306122 0.69387755
 0.73469388 0.7755102  0.81632653 0.85714286 0.89795918 0.93877551
 0.97959184 1.02040816 1.06122449 1.10204082 1.14285714 1.18367347
 1.2244898  1.26530612 1.30612245 1.34693878 1.3877551  1.42857143
 1.46938776 1.51020408 1.55102041 1.59183673 1.63265306 1.67346939
 1.71428571 1.75510204 1.79591837 1.83673469 1.87755102 1.91836735
 1.95918367 2.