scipy stats.halfnorm () | python

NumPy | Python Methods and Functions

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-normal continuous random variable

Code # 1: Create a half-normal continuous random variable

from scipy.stats import halfnorm 

  

numargs = halfnorm.numargs

[] = [ 0.7 ,] * numargs

rv = halfnorm ()

 

print ( "RV:" , rv ) 

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x000001E39B53B630 & gt; 

Code # 2: Seminormal Random Variables and Probability Distribution

import numpy as np

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

 
# Random Variants

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

pr int ( "Random Variates:" , R)

 
# PDF

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

print ( "Probability Distribution:" , R)

Output:

 Random Variates: [3.95023511 1.97013912 2.00977927 1.88217027 2.24680027 0.7298033 0.56769996 0.62071753 1.74743798 0.35512999] Probability Distribution: [0.79784467 0.79307193 0.78048376 0.76045271 0.73356332 0.70058376 0.66242936 0.62012057 0.57473779 0.52737608] 

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 (distributi on))

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 = halfnorm.pdf (x, 1 , 3 )

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

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

Output:





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