scipy stats.genpareto() | Python

scipy.stats.genpareto()是一个广义的帕累托连续随机变量，它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 : generalized Pareto continuous random variable

编程需要懂一点英语

代码 #1：创建广义帕累托连续随机变量

from scipy.stats import genpareto 
  
numargs = genpareto .numargs
[a] = [0.7, ] * numargs
rv = genpareto (a)
  
print ("RV : \n", rv)

输出：

RV :

代码#2：广义帕累托随机变量。

import numpy as np
quantile = np.arange (0.01, 1, 0.1)
   
# Random Variates
R = genpareto.rvs(a, scale = 2,  size = 10)
print ("Random Variates : \n", R)

输出：

Random Variates : 
 [ 1.55978773  0.03897083  7.68148511  0.78339525  1.1217962   0.20434352
  1.16663003  2.06115353 12.82886098  0.27780119]

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, 3))
print("Distribution : \n", distribution)
  
plot = plt.plot(distribution, rv.pdf(distribution))

输出：

Distribution : 
 [0.         0.06122449 0.12244898 0.18367347 0.24489796 0.30612245
 0.36734694 0.42857143 0.48979592 0.55102041 0.6122449  0.67346939
 0.73469388 0.79591837 0.85714286 0.91836735 0.97959184 1.04081633
 1.10204082 1.16326531 1.2244898  1.28571429 1.34693878 1.40816327
 1.46938776 1.53061224 1.59183673 1.65306122 1.71428571 1.7755102
 1.83673469 1.89795918 1.95918367 2.02040816 2.08163265 2.14285714
 2.20408163 2.26530612 2.32653061 2.3877551  2.44897959 2.51020408
 2.57142857 2.63265306 2.69387755 2.75510204 2.81632653 2.87755102
 2.93877551 3.        ]

代码#4：改变位置参数

import matplotlib.pyplot as plt
import numpy as np
  
x = np.linspace(0, 5, 100)
  
# Varying positional arguments
y1 = genpareto.pdf(x, 1, 3)
y2 = genpareto.pdf(x, 1, 4)
plt.plot(x, y1, "*", x, y2, "r--")

输出：