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# scipy stats.dgamma () | python

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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: "scipy.stats._distn_infrastructure.rv_frozen object at 0x000001FDC8AA3940" `

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

 ` "div ` ` class ` ` = ` ` "noIdeBtnDiv" " `` 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:

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