scipy stats.fisk () | python

NumPy | Python Methods and Functions

scipy.stats.fisk () — it is a continuous random variable fisk . It is also known as the logistic-logistic distribution and equates to the Burr distribution with d == 1 and is defined by a standard format and some form parameters to complete its specification.

Parameters:
q: lower and upper tail probability
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: fisk continuous random variable

Code # 1: Generating a continuous random variable

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

Output:

 RV: & lt; scipy.stats ._distn_infrastructure.rv_frozen object at 0x0000018D568102B0 & gt;

Code # 2: risk random variables and probability distributions.

Output:

 Random Variates: [7.79438195 3.97977194 3.20802248 3.02623867 9.36996936 8.54462365 0.47436888 0.4645239 2.1188909 1.49435511] Probability Distribution: [0.00357142 0.0392706 0.07489491 0.11037659 0.1456485 0.18064439 0.21529915 0.2495491 0.28333225 0.3 1658852]

Code # 3: Graphic representation.

import numpy as np

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

   
  # Random Variants  
   R   =   fisk.rvs (a, scale   =   2  , size   =   10  ) 
   print   (  "Random Variates:"  , R)  
   
  # PDF  
   R   =   fisk.pdf (a, quantile, loc   =   0  , scale   =   1  ) 
   print   (  "Probability Distribution:"  , R)

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))  < / p>

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 = fisk.pdf (x, 1 , 3 )

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

Output:

scipy stats.fisk () | python

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