Python Scipy stats.gumbel_l()用法及代码示例

scipy.stats.gumbel_l()是left-skewed Gumbel连续随机变量，使用标准格式和一些形状参数定义了该变量以完成其规格。

参数：
-> q :上下尾概率
-> x :分位数
-> loc :[可选]位置参数。默认值= 0
-> scale:[可选]比例参数。默认值= 1
-> size :[int型元组，可选]形状或随机变量。
-> moments:[可选]由字母['mvsk']组成； “ m” =均值，“ v” =方差，“ s” = Fisher的偏度，“ k” = Fisher的峰度。 (默认=“ MV”)。

Results:left-Skewed Gumbel连续随机变量

代码1：创建left-skewed Gumbel连续随机变量

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

输出：

RV:
 <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001E39A283518>

代码2：left-skewed Gumbel随机变量和概率分布

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

输出：

Random Variates:
 [ 0.55349097 -0.36709655 -0.25581806 -0.81730142  0.28719592 -0.30831366
 -2.69858598 -0.23586469 -1.01965346  6.44132721]

Probability Distribution:
 [0.36786111 0.36573943 0.36038433 0.35223844 0.34175873 0.32939568
 0.31557754 0.3006994  0.28511631 0.26913983]

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：更改位置参数

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

输出：