scipy stats.exponpow () | python



scipy.stats.exponpow () — it is a continuous random variable with exponential power, which is defined by the standard format and some form parameters to complete its specification.

Parameters:
q: lower and upper tail probability
x: quantiles
loc: [optional] location parameter. Default = 0
scale: [optional] scale parameter. Default = 1
size: [tuple of ints, optional] shape or random variates.
moments: [optional] composed of letters [`mvsk `]; `m` = mean, `v` = variance, `s` = Fisher`s skew and `k` = Fisher`s kurtosis. (default = `mv`).

Results: exponential power continuous random variable

Code # 1: Generate Exponential Power continuous random variable

from scipy.stats import exponpow 

  

numargs = exponpow .numargs

[a] = [ 0.6 ,] * numargs

rv = exponpow (a)

  

print ( "RV:" , rv) 

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D566864A8 & gt; 

Code # 2: exponential cardinality of random variables and probability distribution.

import numpy as np

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

 
# Random Variants

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

print ( "Random Variates:" , R)

 
# PDF

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

print ( "Probability Distribution:" , R)

Output:

 Random Variates: [0.39218526 0.4418613 0.23005955 3.56399807 0.29120501 0.27121159 0.07933858 2.54235979 3.05448398 0.6408786] Probability Distribution: [0.00815589 0.09245642 0.18010922 0.26897814 0.35721501 0.44327698 0.525 92189 0.60418893 0.67737085 0.74498201] 

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))

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 = exponpow .pdf (x, 2 , 6 )

y2 = exponpow .pdf (x, 1 , 4 )

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

Output: