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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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