1. Python
  2. scipy stats.gengamma () | python

scipy stats.gengamma () | python

NumPy | Python Methods and Functions

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:





Books for developers

Internet of Things and Big Data Technologies for Next Generation Healthcare

The series “Studies in Big Data” (SBD) publishes new developments and advances in the various areas of Big Data-quickly and with a high quality. The intent is to cover the theory, research, develo...

10/07/2020

Python for Programmers

Python for Programmers: with Big Data and Artificial Intelligence Case Studies This book, written for programmers with a high-level experience in another language, uses how-to instructions to teach...

08/08/2021

The Self-Taught Programmer: The Definitive Guide to Programming Professionally

This book is not just about learning the code; even if you learn to program. If you want to program professionally, learning to code is not enough; For this reason, in addition to helping you program,...

05/09/2021

Veracity of Big Data

Topics on Big Data are growing rapidly. From the first 3 V’s that originally characterized Big Data, the industry now has identified 42 V’s associated with Big Data. The list of how we characteriz...

10/07/2020
Get Solution for free from DataCamp guru