scipy stats.genhalflogistic () | 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; a, b: shape parameters
– & gt; moments: [optional] composed of letters [`mvsk`]; `m` = mean, `v` = variance, `s` = Fisher`s skew and `k` = Fisher`s kurtosis. (default = `mv`).

Results: generalized half-logistic continuous random variable

Code # 1: Create generalized semilogistic continuous random variable

from scipy.stats import genhalflogistic 

 

numargs = genhalflogistic .numargs

[a] = [ 0.7 ,] * numargs

rv = genhalflogistic (a)

  

print ( " RV: " , rv) 

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x000001E39A2B2470 & gt; 

Code # 2: generalized semilogistic random variables and probability distribution

import numpy as np

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

 
# Random Variants

R = genhalflogistic.rvs (a, scale = 2 , size = 10 )

  print ( "Random Variates:" , R)

 
# PDF

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

print ( "Probability Distribution:" , R)

Output:

 Random Variates: [0.24206874 0.66813269 0.75441313 1.05887305 1.8791025 0.64401048 2.11419943 0.62545354 1.57690457 1.64762353] Probability Distribution: [0.44618142 0.47576242 0.50958299 0.54863742 0.59426198 0.64829814 0.71336075 0.7933007 0.89405304 1.02531554] 

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.plo t (distribution, rv.pdf (distribution))

Output:

 Distribution: [0. 0.02915452 0.05830904 0.08746356 0.11661808 0.14577259 0.17492711 0.20408163 0.23323615 0.26239067 0.29154519 0.32069971 0.34985423 0.37900875 0.40816327 0.43731778 0.4664723 0.49562682 0.52478134 0.55393586 0.58309038 0.6122449 0.64139942 0.67055394 0.69970845 0.72886297 0.75801749 0.78717201 0.81632653 0.84548105 0.87463557 0.90379009 0.93294461 0.96209913 0.99125364 1.02040816 1.04956268 1.0787172 1.10787172 1.13702624 1.16618076 1.19533528 1.2244898 1.25364431 1.28279883 1.31195335 1.34110787 1.37026239 1.39941691 1.42857143] 

Code # 4: Various Positional Arguments

import matplotlib.pyplot as plt

import   numpy as np

 

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

 
# Various positional arguments

y1 = genhalflogistic.pdf (x, a , 1 , 3 )

y2 = genhalflogistic. pdf (x, a, 1 , 4 )

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

Output: