scipy.stats.dgamma () is an double gamma 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.
moments : [optional] composed of letters [‘mvsk’]; ‘m’ = mean, ‘v’ = variance, ‘s’ = Fisher’s skew and ‘k’ = Fisher’s kurtosis. (default = ‘mv’).Results : double gamma continuous random variable
Code #1 : Creating double gamma continuous random variable
from scipy.stats import chi   numargs = chi.numargs [a] = [0.6, ] * numargs rv = chi(a)   print ("RV : \n", rv) |
Output :
RV : <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001FDC8AA3940>
Code #2 : double gamma random variates and probability distribution.
import numpy as np quantile = np.arange (0.01, 1, 0.1)    # Random Variates R = chi.rvs(a, scale = 2, size = 10) print ("Random Variates : \n", R)   # PDF R = chi.pdf(a, quantile, loc = 0, scale = 1) print ("\nProbability Distribution : \n", R) |
Output :
Random Variates : [-1.95099046 -0.92462647 -0.44728222 -1.02853811 0.26525202 0.33532233 -1.74580986 -0.02263675 0.02631306 0.01852519] Probability Distribution : [0.00457609 0.05019958 0.09422768 0.13505809 0.1714982 0.20274293 0.22833692 0.24812679 0.2622088 0.27087564]
Code #3 : Graphical Representation.
import numpy as np import matplotlib.pyplot as plt   distribution = np.linspace(0, np.minimum(rv.dist.b, 5)) print("Distribution : \n", distribution)   plot = plt.plot(distribution, rv.pdf(distribution)) |
Output :
Distribution : Distribution : [0. 0.10204082 0.20408163 0.30612245 0.40816327 0.51020408 0.6122449 0.71428571 0.81632653 0.91836735 1.02040816 1.12244898 1.2244898 1.32653061 1.42857143 1.53061224 1.63265306 1.73469388 1.83673469 1.93877551 2.04081633 2.14285714 2.24489796 2.34693878 2.44897959 2.55102041 2.65306122 2.75510204 2.85714286 2.95918367 3.06122449 3.16326531 3.26530612 3.36734694 3.46938776 3.57142857 3.67346939 3.7755102 3.87755102 3.97959184 4.08163265 4.18367347 4.28571429 4.3877551 4.48979592 4.59183673 4.69387755 4.79591837 4.89795918 5. ]
Code #4 : Varying Positional Arguments
<div class="noIdeBtnDiv"> import matplotlib.pyplot as plt import numpy as np   x = np.linspace(0, 5, 100)   # Varying positional arguments y1 = chi.pdf(x, 1, 6) y2 = chi.pdf(x, 1, 4) plt.plot(x, y1, "*", x, y2, "r--") |
