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# scipy stats.exponpow () | python

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scipy.stats.exponpow () — it is a continuous random variable with exponential power, which is defined by the 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: exponential power continuous random variable

Code # 1: Generate Exponential Power continuous random variable

 ` from ` ` scipy.stats ` ` import ` ` exponpow ` ` `  ` numargs ` ` = ` ` exponpow .numargs ` ` [a] ` ` = ` ` [` ` 0.6 ` `,] ` ` * ` ` numargs ` ` rv ` ` = ` ` exponpow (a) ` `  `` print ( "RV:" , rv) `

Output:

` RV: "scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D566864A8" `

Code # 2: exponential cardinality of random variables and probability distribution.

` `

` import numpy as np quantile = np.arange ( 0.01 , 1 , 0.1 )   # Random Variants R = exponpow.rvs (a, scale = 2 , size = 10 ) print ( "Random Variates:" , R)   # PDF R = exponpow.pdf (a, quantile, loc = 0 , scale = 1 ) print ( "Probability Distribution:" , R) `

Output:

` Random Variates: [0.39218526 0.4418613 0.23005955 3.56399807 0.29120501 0.27121159 0.07933858 2.54235979 3.05448398 0.6408786] Probability Distribution: [0.00815589 0.09245642 0.18010922 0.26897814 0.35721501 0.44327698 0.525 92189 0.60418893 0.67737085 0.74498201] `

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 ` ` = ` ` exponpow .pdf (x, ` ` 2 ` `, ` ` 6 ` `) ` ` y2 ` ` = ` ` exponpow .pdf (x, ` ` 1 ` `, ` ` 4 ` `) ` ` plt.plot (x, y1, ` ` " * "` `, x, y2, ` `" r-- "` `) `

Output: