scipy stats.gengamma () | python

scipy.stats.gengamma () — it is a generalized gamma 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; 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: 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 gamma continuous random variable

Code # 1: Generating a generalized gamma -continuous random variable

from scipy.stats import gengamma 

 

numargs = gengamma .numargs

[a, b] = [ 0.7 ,] * numargs

rv = gengamma (a, b)

  

print ( "RV:" , rv) 

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x000001E39A2A8BE0 & gt; 

Code # 2: Generalized Gamma Random Variables and Probability Distribution

import numpy as np

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

 
# Random Variants

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

print ( " Random Variates: " , R)

 
# PDF

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

print ( "Probability Distribution:" , R)

Output:

 Random Variates: [1.28899567e-01 6.07031120 e-06 7.58807426e-01 1.02689244e + 00 2.75752340e-02 8.07943863e-03 4.69774065e-01 2.48110421e-01 4.64544740e-01 7.04892852e + 00] Probability Distribution: [0.004053 0.04502864 0.08671695 0.12897998 0.17168235 0.21469197 0.25788056 0.30112428 0.34430406 0.38730608] 

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 = gengamma.pdf (x, a, 1 , 3 )

y2 = gengamma.pdf ( x, a, 1 , 4 )

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

Output: