scipy stats.gausshyper () | python

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:

scipy stats.gausshyper () | python

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