scipy stats.gumbel_l () | python

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

Results: left-Skewed Gumbel continuous random variable

Code # 1: Create continuous Gumbel random variable skewed to the left

from scipy.stats import gumbel_l 

 

numargs = gumbel_l .numargs

[] = [ 0.7 ,] * numargs

rv = gumbel_l ()

  

print ( "RV:" , rv) 

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x000001E39A283518 & gt; 

Code # 2: Left-hand Gumbel Random Variation and Probability Distribution

import numpy as np

quantile = np.arange ( 0.01 , 1 , 0.1 )

 
# Random Variants

R = gumbel_l.rvs (scale = 2 , size = 10 )

print ( " Random Variates: " , R)

 
# PDF

R = gumbel_l.pdf (quantile, loc = 0 , scale = 1 )

print ( "Probability Distribution:" , R)

Output:

 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 0.3155 7754 0.3006994 0.28511631 0.26913983] 

Code # 3: Graphic representation.

import numpy as np

import matplotlib.pyplot as plt

 

distribution = np.linspace ( 0 , np.minimum (rv.dist. b, 3 ))

print ( "Distribution:" , distribution)

 

plot = plt.plot (distribution, rv .pdf (distribution))

Output:

 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. ] 

Code # 4: Various Positional Arguments

import matplotlib. pyplot as plt

import numpy as np

 

x = np.linspace ( 0 , 5 , 100 )

 
# Various positional arguments

y1 = gumbel_l .pdf (x, 1 , 3 )

y2 = gumbel_l .pdf (x, 1 , 4 )

plt.plot (x, y1,  "*" , x, y2, "r--" )

Output: