scipy stats.gumbel_r () | 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: Right-Skewed Gumbel continuous random variable

Code # 1: Create right-hand continuous Gumbel random variable

from scipy.stats import gumbel_r

numargs = gumbel_r .numargs

[] = [ 0.7 ,] * numargs

   rv   =   gumbel_r () 
    
   print   (  "RV:"  , rv)

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x000001E39A4600F0 & gt;

Code # 2: Right-Hand Gumbel Random Variations and Probability Distribution

import numpy as np

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

   
  # Random Variants  
   R   =   gumbel_r .rvs (scale   =   2  , size   =   10  ) 
   print   ( " Random Variates: " , R)  
   
  # PDF  
   R   =   gumbel_r .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 0.3 1557754 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 < code class = "plain"> numpy as np

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

# Various positional arguments

   y1   =   gumbel_r .pdf (x,   1  ,   3  ) 
   y2   =   gumbel_r .pdf (x ,   1  ,   4  ) 
   plt.plot (x, y1,  " * " , x, y2,  " r-- " )

Output:

scipy stats.gumbel_r () | python

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