scipy.stats.johnsonsb() is a Johnson SB 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 : Johnson SB continuous random variable
Code #1 : Creating Johnson SB continuous random variable
# importing library from scipy.stats import johnsonsb numargs = johnsonsb.numargs a, b = 4.32, 3.18rv = johnsonsb(a, b) print ("RV : \n", rv) |
Output :
RV : scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D50286C8
Code #2 : Johnson SB continuous variates and probability distribution
import numpy as np quantile = np.arange (0.01, 1, 0.1) # Random Variates R = johnsonsb.rvs(a, b, scale = 2, size = 10) print ("Random Variates : \n", R) # PDF R = johnsonsb.pdf(a, b, quantile, loc = 0, scale = 1) print ("\nProbability Distribution : \n", R) |
Output :
Random Variates : [0.42212956 0.60876766 0.35494705 0.42892958 0.25316345 0.51872977 0.2355019 0.44657975 0.54971277 0.36683771] Probability Distribution : [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
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. 0.02040816 0.04081633 0.06122449 0.08163265 0.10204082 0.12244898 0.14285714 0.16326531 0.18367347 0.20408163 0.2244898 0.24489796 0.26530612 0.28571429 0.30612245 0.32653061 0.34693878 0.36734694 0.3877551 0.40816327 0.42857143 0.44897959 0.46938776 0.48979592 0.51020408 0.53061224 0.55102041 0.57142857 0.59183673 0.6122449 0.63265306 0.65306122 0.67346939 0.69387755 0.71428571 0.73469388 0.75510204 0.7755102 0.79591837 0.81632653 0.83673469 0.85714286 0.87755102 0.89795918 0.91836735 0.93877551 0.95918367 0.97959184 1. ]
Code #4 : Varying Positional Arguments
import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 5, 100) # Varying positional arguments y1 = johnsonsb .pdf(x, 1, 3) y2 = johnsonsb .pdf(x, 1, 4) plt.plot(x, y1, "*", x, y2, "r--") |
Output :
