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scipy stats.halfgennorm () | python

scipy.stats.halfgennorm () — upper half of a generalized normal continuous random variable. To complete its specific customization, it is defined with a standard format and some shape parameters. The object inherits from it a collection of generic methods and adds specific details to them.

Parameters :

 -"  α:  scale -"  β:  shape -"  μ:  location 

Code # 1: Generating a semi-generalized normal continuous random variable

from scipy.stats import halfgennorm 

 

numargs = halfgennorm.numargs

[a] = [ 0.7 ,] * numargs

rv = halfgennorm (a)

 

print ( "RV:" , rv) 

Exit:

 RV: scipy.stats._distn_infrastructure.rv_frozen object at 0x0000021FB55D8DD8 

Code # 2: Semi-Generalized Random Variables and Probability Distribution

import numpy as np

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

  
# Random Variants

R = h alfgennorm .rvs (. 2 , scale = 2 , size = 10 )

print ( "Random Variates:" , R)

 
# PDF

R = halfgennorm .pdf (quantile,. 2 , loc = 0 , scale = 1 )

print ( "Probability Distribution:" , R)

Exit:

 Random Variates: [1.41299459e + 03 3.51301175e + 04 1.79981484e + 05 2.90925518e + 02 2.70178121e + 05 1.31706797e + 05 3.25898913e + 01 1.62607410 e + 04 2.02263946e + 04 1.97078668e + 04] Probability Distribution: [0.00559658 0.0043805 0.00400834 0.0037776 0.00360957 0.00347731 0.00336825 0.00327549 0.00319482 0.00312348] 

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))

Exit :

 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 = halfgennorm .pdf (x, 1 , 3 )

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

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

Output:

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