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)    # 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.
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)   # 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 :
