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# scipy stats.foldnorm () | python

|

Parameters:
-" q: lower and upper tail probability
-" a: shape parameters
-" 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: folded normal continuous random variable

Code # 1: Create a folded normal continuous random variable

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

Exit:

` RV: "scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D56531160" `

Code # 2: folded normal random variables and probability distribution.

` `

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

` `

Output:

` Random Variates: [1.91938545 1.98147825 2.45557747 6.33452251 1.94893049 1.67444448 1.33462558 2.94928303 0.87723162 1.16012323] Probability Distribution: [0.62449194 0.6225821 0.61750041 0.60927878 0.59797273 0.58366613 0.5664765 9 0.54656084 0.52411892 0.49939664] `

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

Output:

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