  # scipy stats.genextreme () | python

NumPy | Python Methods and Functions

scipy.stats.genextreme () — generalized continuous random variable of extreme value, which is defined by a standard format and some form parameters to complete its specification.

Parameters:
- & gt; q: lower and upper tail probability
- & 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; a, b, c: shape parameters
- & gt; moments: [optional] composed of letters [`mvsk`]; `m` = mean, `v` = variance,
`s` = Fisher`s skew and `k` = Fisher`s kurtosis. (default = `mv`).

Results: generalized extreme value continuous random variable

for == 0

for x & lt; = 1 / a, and & gt; 0

Code # 1: Generate a generalized extreme continuous random variable

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

Output:

` RV: & lt; scipy. stats._distn_infrastructure.rv_frozen object at 0x000001E399AB5A58 & gt; `

Code # 2: generalized random values ​​of extreme values.

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

Output:

` Random Variates: [1.0976659 -4.30499477 -1.30818332 1.54664658 1.44268486 1.80027137 1.52868675 1.8569798 1.36066713 - 1.85945751] Probability Distribution: [0.30397758 0.32272193 0.34399063 0.3683456 0.39653387 0.42957283 0.46888883 0.516553 45 0.57571147 0.65141728] `

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 (distrib ution)) `

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 = genextreme.pdf (x , a, 1 , 3 ) y2 = genextreme.pdf (x, a, 1 , 4 ) plt.plot (x, y1, "*" , x, y2, "r--" ) `

Exit: