scipy stats.halflogistic () | 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: Half-logistic continuous random variable

Code # 1: Create a semi-logistic continuous random variable

from scipy.stats import halflogistic 

  

numargs = halflogistic .numargs

[] = [ 0.7 ,] * numargs

rv = halflogistic ()

  

print ( "RV:" , rv) 

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x000001E39A2EA7B8 & gt; 

Code # 2: Semilogistic Random Variations and Probability Distribution

import numpy as np

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

 
# Random Variants

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

print ( " Random Variates: " , R)

 
# PDF

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

print ( "Probability Distribution:" , R)

Output:

 Random Variates: [1.51677656 4.2051329 3.00947016 5.00828865 8.23514322 0.46379571 1.75794767 2.84948119 0.31392647 1.36186056] Probability Distribution: [0.4999875 0.49849054 0.49452777 0.48817731 0.47956248 0.46884669 0.45622704 0.44192689 0.42618788 0.40926186] 

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.pd f (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 = halflogistic .pdf (x, 1 , 3 )

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

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

Exit: