scipy.stats.fisk() is an fisk continuous random variable. It is also known as the log-logistic distribution, and equals the Burr distribution with d == 1 and 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 : fisk continuous random variable
Code #1 : Creating fisk continuous random variable
from scipy.stats import fisk   numargs = fisk.numargs [a] = [0.7, ] * numargs rv = fisk(a)   print ("RV : \n", rv)  |
Output :
RV : <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D568102B0>
Code #2 : fisk random variates and probability distribution.
import numpy as np quantile = np.arange (0.01, 1, 0.1)    # Random Variates R = fisk.rvs(a, scale = 2, size = 10) print ("Random Variates : \n", R)   # PDF R = fisk.pdf(a, quantile, loc = 0, scale = 1) print ("\nProbability Distribution : \n", R) |
Output :
Random Variates : [7.79438195 3.97977194 3.20802248 3.02623867 9.36996936 8.54462365 0.47436888 0.4645239 2.1188909 1.49435511] Probability Distribution : [0.00357142 0.0392706 0.07489491 0.11037659 0.1456485 0.18064439 0.21529915 0.2495491 0.28333225 0.31658852]
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 = fisk.pdf(x, 1, 3) y2 = fisk.pdf(x, 1, 4) plt.plot(x, y1, "*", x, y2, "r--") |
Output :
