scipy stats.genexpon() | Python
Last Updated :
27 Mar, 2019
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
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.
import numpy as np
quantile = np.arange ( 0.01 , 1 , 0.1 )
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.
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
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace( 0 , 5 , 100 )
y1 = genexpon.pdf(x, a, 1 , 3 )
y2 = genexpon.pdf(x, a, 1 , 4 )
plt.plot(x, y1, "*" , x, y2, "r--" )
|
Output :
Like Article
Suggest improvement
Share your thoughts in the comments
Please Login to comment...