scipy stats.genlogistic () | python

scipy.stats.genlogistic () — it is a generic boolean continuous random variable that is defined by a standard format and some form parameters to complete its specification.

Parameters:
- & gt; q: lower and upper tail probability
- & gt; a, b: shape parameters
- & gt; x: quantiles
- & gt; loc: [optional] location parameter. Default = 0
- & gt; scale: [optional] scale parameter. Default = 1
- & gt; size: [tuple of ints, optional] shape or random variates.
- & gt; moments: [optional] composed of letters ['mvsk']; 'm' = mean, 'v' = variance, 's' = Fisher's skew and 'k' = Fisher's kurtosis. (default = 'mv').

Results: generalized logistic continuous random variable

Code # 1: Creating a generalized logistic continuous random variable

from scipy.stats import genlogistic

numargs = genlogistic .numargs

[a] = [ 0.7 ,] * numargs

rv = genlogistic (a)

print ( " RV : " , rv)

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D578F4D30 & gt;

Code # 2: Generalized Logistic Random Variation and Probability Distribution.

import numpy as np

quantile = np.arange ( 0.01 , 1 , 0.1 )

# Random Variants

R = genlogistic.rvs (a, scale = 2 , size = 10 )

print ( "Random Variates:" , R)

# PDF

R = genlogistic.pdf (a, quantile, loc = 0 , scale = 1 )

print ( "Probability Distribution:" , R)

Output:

 Random Variates: [-2.25279702 -1.09146871 -0.01100363 -3.95860336 5.07952934 -2.3073455 -3.11698062 -0.32931819 8.84452349 -3.06546109] Probability Distribution: [0.00330477 0.03491595 0.06402364 0.09077633 0.11531469 0. 13777195 0.15827427 0.17694109 0.19388545 0.20921433]

Code # 3: Graphic representation.

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))

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 = genlogistic.pdf (x, 1 , 3 )

y2 = genlogistic.pdf (x, 1 , 4 )

plt.plot (x, y1, "*" , x, y2, "r--" )

Output:

scipy stats.genlogistic () | python

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