scipy stats.genexpon() | Python

scipy.stats.genexpon() is an generalized exponential continuous random variable that is defined with a standard format and some shape 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. -> a, b, c : shape parameters -> moments : [optional] composed of letters [‘mvsk’]; 'm' = mean, 'v' = variance, 's' = Fisher's skew and 'k' = Fisher's kurtosis. (default = 'mv'). Results : generalized exponential continuous random variable

Code #1 : Creating generalized exponential continuous random variable

Python3

from scipy.stats import genexpon 

numargs = genexpon .numargs
[a, b, c] = [0.7, ] * numargs
rv = genexpon (a, b, c)

print ("RV : \n", rv)

Output :

RV : 
 <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D57997F60>

Code #2 : generalized exponential random variates.

Python3 1==

import numpy as np
quantile = np.arange (0.01, 1, 0.1)
 
# Random Variates
R = genexpon.rvs(a, scale = 2,  size = 10)
print ("Random Variates : \n", R)

Output :

Random Variates : 
 [0.74505484 2.02790441 2.06823675 3.96275674 1.24274054 3.71331036
 0.53957521 0.37359838 2.53934153 2.36254065]

Probability Distribution : 
 [0.43109163 0.45222638 0.47102054 0.48773188 0.50258763 0.51578837
 0.52751153 0.53791424 0.54713591 0.55530037]

Code #3 : Graphical Representation.

Python3

import numpy as np
import matplotlib.pyplot as plt

distribution = np.linspace(0, np.minimum(rv.dist.b, 3))
print("Distribution : \n", 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 : Varying Positional Arguments

Python3 1==

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 5, 100)

# Varying positional arguments
y1 = genexpon.pdf(x, a, 1, 3)
y2 = genexpon.pdf(x, a, 1, 4)
plt.plot(x, y1, "*", x, y2, "r--")

Output :

scipy stats.genexpon() | Python

Explore