scipy stats.genexpon () | python

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scipy.stats.genexpon () is a generalized exponential continuous random variable that is defined by a 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, b, c: 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: generalized exponential continuous random variable

Code # 1: Generate generalized exponential continuous random variable

 ` from ` ` scipy.stats ` ` import ` ` genexpon ` ` ` ` numargs ` ` = ` ` genexpon .numargs ` ` [a, b, c] ` ` = ` ` [` ` 0.7 ` `,] ` ` * ` ` numargs ` ` rv ` ` = ` ` genexpon (a, b, c) ` ` ` ` print ` ` (` `" RV: "` `, rv) `

Output:

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

Code # 2: generalized exponential random variables.

 ` import ` ` numpy as np ` ` quantile ` ` = ` ` np.arange (` ` 0.01 ` `, ` ` 1 ` `, ` ` 0.1 ` `) ` ` ` ` # Random Variants ` ` R ` ` = ` ` genexpon.rvs (a, scale ` ` = ` ` 2 ` `, size ` ` = ` ` 10 ` `) ` ` print ` ` (` ` "Random Variates:" ` `, R) `

Output:

``` Random Variates: [0.74505484 2.02790441 2.06823675 3.96275674 1.24274054 3.71331036 0.53957521 0.37359838 2.53934153 2.36254065] Probability Distribution: [0.43109163 0.45222638 0.47102054 0.48773188 0.50258763 0.51578837 0.52751153 0.5135300. ` ``       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 ` ` = ` ` genexpon.pdf (x, a, ` ` 1 ` `, ` ` 3 ` ` ) `  ` y2 ` ` = ` ` genexpon.pdf ( x, a, ` ` 1 ` `, ` ` 4 ` `) `  ` plt.plot (x, y1, ` `" * "` `, x, y2, ` `" r-- "` `) `   Output:

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Frank Lehnman

London | 2023-03-26

Simply put and clear. Thank you for sharing. scipy stats.genexpon () | python and other issues with http Python module was always my weak point 😁. Will use it in my bachelor thesis

Oliver Krasiko

Munchen | 2023-03-26

dis Python module is always a bit confusing 😭 scipy stats.genexpon () | python is not the only problem I encountered. Checked yesterday, it works!

Javier Krasiko

Moscow | 2023-03-26

I was preparing for my coding interview, thanks for clarifying this - scipy stats.genexpon () | python in Python is not the simplest one. Will get back tomorrow with feedback

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