scipy stats.betaprime() | Python

scipy.stats.betaprime()是一个 beta 素数连续随机变量，使用标准格式和一些形状参数定义以完成其规范。

Parameters :
q : lower and upper tail probability
a, b : shape parameters
x : quantiles
loc : [optional]location parameter. Default = 0
scale : [optional]scale parameter. Default = 1
size : [tuple of ints, optional] shape or random variates.
moments : [optional] composed of letters [‘mvsk’]; ‘m’ = mean, ‘v’ = variance, ‘s’ = Fisher’s skew and ‘k’ = Fisher’s kurtosis. (default = ‘mv’).

Results : beta prime continuous random variable

编程需要懂一点英语

代码 #1：创建 betaprime 连续随机变量

# importing scipy
from scipy.stats import betaprime
  
numargs = betaprimeprime.numargs
[a, b] = [0.6, ] * numargs
rv = betaprimeprime(a, b)
  
print ("RV : \n", rv)

输出：

RV :

代码 #2：betaprime 随机变量和概率分布。

import numpy as np
quantile = np.arange (0.01, 1, 0.1)
   
# Random Variates
R = betaprime.rvs(a, b, scale = 2,  size = 10)
print ("Random Variates : \n", R)
  
# PDF
R = betaprime.pdf(quantile, a, b, loc = 0, scale = 1)
print ("\nProbability Distribution : \n", R)

输出：

Random Variates : 
 [ 1.59603917  1.92408727  1.2120992   0.34064091  2.68681773 22.99956678
  1.45523032  2.93360219 23.93717261 18.04203815]

Probability Distribution : 
 [2.58128122 0.8832351  0.61488062 0.47835546 0.39160163 0.33053737
 0.28490363 0.24941484 0.22101038 0.1977718 ]

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, 5))
print("Distribution : \n", distribution)
  
plot = plt.plot(distribution, rv.pdf(distribution))

输出：

Distribution : 
 [0.         0.10204082 0.20408163 0.30612245 0.40816327 0.51020408
 0.6122449  0.71428571 0.81632653 0.91836735 1.02040816 1.12244898
 1.2244898  1.32653061 1.42857143 1.53061224 1.63265306 1.73469388
 1.83673469 1.93877551 2.04081633 2.14285714 2.24489796 2.34693878
 2.44897959 2.55102041 2.65306122 2.75510204 2.85714286 2.95918367
 3.06122449 3.16326531 3.26530612 3.36734694 3.46938776 3.57142857
 3.67346939 3.7755102  3.87755102 3.97959184 4.08163265 4.18367347
 4.28571429 4.3877551  4.48979592 4.59183673 4.69387755 4.79591837
 4.89795918 5.        ]

代码#4：改变位置参数

from scipy.stats import arcsine
import matplotlib.pyplot as plt
import numpy as np
  
x = np.linspace(0, 1.0, 100)
  
# Varying positional arguments
y1 = betaprime.pdf(x, 2.75, 2.75)
y2 = betaprime.pdf(x, 3.25, 3.25)
plt.plot(x, y1, "*", x, y2, "r--")

输出：