scipy stats.dweibull () | python

scipy.stats.dweibull () — it is a continuous random double Weibull 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: double weibull continuous random variable

Code # 1: Generating a continuous random double Weibull values ​​

from scipy.stats import dweibull 

  

numargs = dweibull.numargs

[a] = [ 0.6 ,] * numargs

rv = dweibull (a)

 

  print ( "RV:" , rv) 

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x000001FDC8AA8E80 & gt; 

Code # 2: double Weibull random variables and probability distribution.

import numpy as np

quantile = np.arange ( 0.01 , 1 , 0.1 )

 
# Random Variants

R = dweibull.rvs (a, scale = 2 , size = 10 )

print ( " Random Variates: " , R)

 
# PDF

R = dweibull.pdf (a, quantile, loc = 0 , scale = 1 )

print ( " Probability Distribution: " , R)

Output:

 Random Variates: [1.49793669 2.02019269 -1.8530545 -0.79018341 0.96852783 -14.70570461 -1.7957089 0.79819141 4.34335483 -0.96031661] Probability Distribution: [0.00306562 0.03367007 0.06402237 0.09391113 0.12314439 0.15155039 0.17897 785 0.20529592 0.23039382 0.25418014] 

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, r v.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 = dweibull.pdf (x, 1 , 6 )

y2 = dweibull.pdf (x, 1 , 5 )

plt.plot (x, y1, "* " , x, y2, " r-- " )

Output: