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scipy stats.exponweib () | python

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scipy.stats.exponweib () — an exponential continuous Weibull random variable that is 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: exponential Weibull continuous random variable

Code # 1: Create exponential continuous random variable Weibull random variable

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

Output:

` RV: "scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D5660E1D0" `

Code # 2: Weibull Exponential Random Variables and Probability Distribution.

` `

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

` `

Output:

` Random Variates: [8.17460511 e + 00 1.33286202e + 00 1.77493153e + 01 1.83861272e-01 5.32255458e-01 1.34520149e + 00 1.91022498e-02 3.08216056e-03 6.46223522e-03 1.75786657e-01] Probability Distribution: [0.00442484 0.0491 9014 0.09470438 0.14070318 0.1869346 0.2331608 0.27915913 0.32472306 0.36966267 0.41380492] `

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 (distribution)) `

Output:

` 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

 ` import ` ` matplotlib. pyplot as plt ` ` import ` < code class = "plain"> numpy as np   ` x ` ` = ` ` np.linspace (` ` 0 ` `, ` ` 5 ` `, ` ` 100 ` `) `   ` # Various positional arguments ` ` y1 ` ` = ` ` exponweib .pdf (x, ` ` 2 ` `, ` ` 6 ` `) ` ` y2 ` ` = ` ` exponweib .pdf (x , ` ` 1 ` `, ` ` 4 ` `) ` ` plt.plot (x, y1, ` `" * "` `, x, y2, ` `" r-- "` `) `

Output: