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: