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# scipy stats.fisk () | python

scipy.stats.fisk () — it is a continuous random variable fisk . It is also known as the logistic-logistic distribution and equates to the Burr distribution with d == 1 and is defined by a standard format and some form 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: Generating a continuous random variable

` `

` from scipy.stats import fisk   numargs = fisk.numargs [a] = [ 0.7 ,] * numargs rv = fisk (a)    print ( "RV:" , rv)  `

` `

Output:

` RV: "scipy.stats ._distn_infrastructure.rv_frozen object at 0x0000018D568102B0" `

Code # 2: risk random variables and probability distributions.

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 0.3 1658852] `

Code # 3: Graphic representation.

 ` import ` ` numpy as np ` ` quantile ` ` = ` ` np.arange (` ` 0.01 ` `, ` ` 1 ` `, ` ` 0.1 ` `) `   ` # Random Variants ` ` R ` ` = ` ` fisk.rvs (a, scale ` ` = ` ` 2 ` `, size ` ` = ` ` 10 ` `) ` ` print ( "Random Variates:" , R) ``   # PDF R = fisk.pdf (a, quantile, loc = 0 , scale = 1 ) print ( "Probability Distribution:" , R) `
 ` import ` ` numpy as np ` ` import ` ` matplotlib.pyplot as plt `   ` distribution ` ` = ` ` np.linspace (` ` 0 ` `, np.minimum (rv.dist.b, ` ` 3 ` `)) ` ` print ` ` (` ` "Distribution:" ` `, distribution) `   ` plot ` ` = ` ` plt.plot (distribution, rv.pdf (distribution)) ` < / p>

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: Various Positional Arguments

 ` import ` ` matplotlib. pyplot as plt ` ` import ` ` numpy as np `   ` x ` ` = ` ` np.linspace (` ` 0 ` `, ` ` 5 ` `, ` ` 100 ` `) `   ` # Various positional arguments ` ` y1 ` ` = ` ` fisk.pdf (x, ` ` 1 ` `, ` ` 3 ` `) ` ` y2 ` ` = ` ` fisk.pdf (x, ` ` 1 ` `, ` ` 4 ` `) ` ` plt.plot (x, y1, ` ` "*" ` `, x, y2, ` ` "r--" ` `) `

Output:

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