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scipy stats.genhalflogistic () | python

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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.
-" a, b: shape parameters
-" moments: [optional] composed of letters [’mvsk’]; ’m’ = mean, ’v’ = variance, ’s’ = Fisher’s skew and ’k’ = Fisher’s kurtosis. (default = ’mv’).

Results: generalized half-logistic continuous random variable

Code # 1: Create generalized semilogistic continuous random variable

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

Output:

` RV: "scipy.stats._distn_infrastructure.rv_frozen object at 0x000001E39A2B2470" `

Code # 2: generalized semilogistic random variables and probability distribution

 ` import ` ` numpy as np ` ` quantile ` ` = ` ` np.arange (` ` 0.01 ` `, ` ` 1 ` `, ` ` 0.1 ` `) `   ` # Random Variants ` ` R ` ` = ` ` genhalflogistic.rvs (a, scale ` ` = ` ` 2 ` `, size ` ` = ` ` 10 ` `) `  ` print ` ` (` ` "Random Variates:" ` `, R) `   ` # PDF ` ` R ` ` = ` ` genhalflogistic.pdf (a, quantile, loc ` ` = ` ` 0 ` `, scale ` ` = ` ` 1 ` `) ` ` print ` ` (` ` "Probability Distribution:" ` `, R) `

Output:

` Random Variates: [0.24206874 0.66813269 0.75441313 1.05887305 1.8791025 0.64401048 2.11419943 0.62545354 1.57690457 1.64762353] Probability Distribution: [0.44618142 0.47576242 0.50958299 0.54863742 0.59426198 0.64829814 0.71336075 0.7933007 0.89405304 1.02531554] `

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.plo t (distribution, rv.pdf (distribution)) `

Output:

` Distribution: [0. 0.02915452 0.05830904 0.08746356 0.11661808 0.14577259 0.17492711 0.20408163 0.23323615 0.26239067 0.29154519 0.32069971 0.34985423 0.37900875 0.40816327 0.43731778 0.4664723 0.49562682 0.52478134 0.55393586 0.58309038 0.6122449 0.64139942 0.67055394 0.69970845 0.72886297 0.75801749 0.78717201 0.81632653 0.84548105 0.87463557 0.90379009 0.93294461 0.96209913 0.99125364 1.02040816 1.04956268 1.0787172 1.10787172 1.13702624 1.16618076 1.19533528 1.2244898 1.25364431 1.28279883 1.31195335 1.34110787 1.37026239 1.39941691 1.42857143] `

Code # 4: Various Positional Arguments

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

Output: