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# scipy stats.halfgennorm () | python

scipy.stats.halfgennorm () — upper half of a generalized normal continuous random variable. To complete its specific customization, it is defined with a standard format and some shape parameters. The object inherits from it a collection of generic methods and adds specific details to them.

Parameters :

` -"  α:  scale -"  β:  shape -"  μ:  location `

Code # 1: Generating a semi-generalized normal continuous random variable

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

Exit:

` RV: scipy.stats._distn_infrastructure.rv_frozen object at 0x0000021FB55D8DD8 `

Code # 2: Semi-Generalized Random Variables and Probability Distribution

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

Exit:

` Random Variates: [1.41299459e + 03 3.51301175e + 04 1.79981484e + 05 2.90925518e + 02 2.70178121e + 05 1.31706797e + 05 3.25898913e + 01 1.62607410 e + 04 2.02263946e + 04 1.97078668e + 04] Probability Distribution: [0.00559658 0.0043805 0.00400834 0.0037776 0.00360957 0.00347731 0.00336825 0.00327549 0.00319482 0.00312348] `

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)) `

Exit :

` 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 ` ` = ` ` halfgennorm .pdf (x, ` ` 1 ` `, ` ` 3 ` `) ` ` y2 ` ` = ` ` halfgennorm .pdf (x , ` ` 1 ` `, ` ` 4 ` `) ` ` plt.plot (x, y1, ` `" * "` `, x, y2, ` `" r-- "` `) `

Output:

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