scipy stats.gamma () | python

NumPy | Python Methods and Functions

scipy.stats.gamma () — it is a continuous gamma variable random variable that is defined by the 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: 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: gamma continuous random variable

Code # 1: Create gamma continuous random variable random variable

from scipy.stats import gamma 

  

numargs = gamma .numargs

[a] = [ 0.7 ,] * numargs

rv = gamma (a)

  

prin t ( "RV:" , rv) 

Output:

 RV : & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D57997F60 & gt; 

Code # 2: Generic Gamma Random Variables.

import numpy as np

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

 
# Random Variants

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

print ( " Random Variates: " , R)

 
# PDF

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

print ( " Probability Distribution: " , R)

Output:

 Random Variates: [0.01601209 0.05164555 1.22072489 0.53476245 0.11529018 0.16966403 0.59198231 0.71995529 0.86063603 3.81492177] Probability Distribution: [0.001976910916 0.34020629 0.38910556 0.42939763 0.46081639 0.48344302] < / pre> 

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

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

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

Output:





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