scipy stats.dgamma () | python



scipy.stats.dgamma () — it is a continuous double-gamut random variable defined by a standard format and some form parameters to complete its specification.

Parameters:
q: lower and upper tail probability
x: quantiles
loc: [optional] location parameter. Default = 0
scale: [optional] scale parameter. Default = 1
size: [tuple of ints, optional] shape or random variates.
moments: [optional] composed of letters [`mvsk `]; `m` = mean, `v` = variance, `s` = Fisher`s skew and `k` = Fisher`s kurtosis. (default = `mv`).

Results: double gamma continuous random variable

Code # 1: Generating a continuous random variable double gamut values ​​

from scipy.stats import chi 

 

numargs = chi.numargs

[a] = [ 0.6 ,] * numargs

rv = chi (a)

 

print ( " RV: " , rv) 

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x000001FDC8AA3940 & gt; 

Code # 2: Random Double Gamut and Probability Distribution.

import numpy as np

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

 
# Random Variants

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

prin t ( "Random Variates:" , R)

 
# PDF

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

print ( "Probability Distribution:" , R)

Output:

 Random Variates: [-1.95099046 -0.92462647 -0.44728222 -1.02853811 0.26525202 0.33532233 -1.74580986 -0.02263675 0.02631306 0.01852519] Probability Distribution: [0.00457609 0.05019958 0.09422768 0.13505809 0.1714982 0.20274293 0.22833692 0.2481267 9 0.2622088 0.27087564] 

Code # 3: Graphic representation.

import numpy as np

import matplotlib.pyplot as plt

 

distribution = np.linspace ( 0 , np.minimum (rv.dist.b , 5 ))

print ( "Distribution:" , distribution)

 

plot = plt.plot (distribution, rv.pdf (distribu tion))

Output:

 Distribution: Distribution: [0. 0.10204082 0.20408163 0.30612245 0.40816327 0.51020408 0.6122449 0.71428571 0.81632653 0.91836735 1.02040816 1.12244898 1.2244898 1.32653061 1.42857143 1.53061224 1.63265306 1.73469388 1.83673469 1.93877551 2.04081633 2.14285714 2.24489796 2.34693878 2.44897959 2.55102041 2.65306122 2.75510204 2.85714286 2.95918367 3.06122449 3.16326531 3.26530612 3.36734694 3.46938776 3.57142857 3.67346939 3.7755102 3.87755102 3.97959184 4.08163265 4.18367347 4.28571429 4.3877551 4.48979592 4.59183673 4.69387755 4.79591837 4.89795918 5. ] 

Code # 4: Various Positional Arguments

& lt; div class = "noIdeBtnDiv" & gt; 

import matplotlib.pyplot as plt

import numpy as np

 

x = np.linspace ( 0 , 5 , 100 )

 
# Various positional arguments

y1 = chi.pdf (x, 1 , 6 )

y2 = chi.pdf (x, 1 , 4 )

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

Output: