  # scipy stats.gausshyper () | python

NumPy | Python Methods and Functions

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, z: 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: Gauss hyper-geometric continuous random variable

Code # 1: Create hypergeometric continuous Gaussian random variable

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

Output:

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

Code # 2: Gaussian Hypergeometric Random Variables and Probability Distribution.

` `

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

Output:

` Random Variates: [1.45915082 0.58184603 1.91448022 1.23505789 0.9253147 0.36681062 0.19628827 0.91795248 1.95313724 1.63728124] Probability Distribution: [0.83983413 0.82838709 0.81749232 0.80714179 0.79731436 0.78798255 0.77911641 0.77068563 0.76266077 0.75501387] `

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. 0.02040816 0.04081633 0.06122449 0.08163265 0.10204082 0.12244898 0.14285714 0.16326531 0.18367347 0.20408163 0.2244898 0.24489796 0.26530612 0.28571429 0.30612245 0.32653061 0.34693878 0.36734694 0.3877551 0.40816327 0.42857143 0.44897959 0.46938776 0.48979592 0.51020408 0.53061224 0.55102041 0.57142857 0.59183673 0.6122449 0.63265306 0.65306122 0.67346939 0.69387755 0.71428571 0.73469388 0.75510204 0.7755102 0.79591837 0.81632653 0.83673469 0.85714286 0.87755102 0.89795918 0.91836735 0.93877551 0.95918367 0.97959184 1. ] `

Code # 4: Various Positional Arguments

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

Exit: