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Python – Inverse Weibull Distribution in Statistics

  • Last Updated : 10 Jan, 2020

scipy.stats.invweibull() is an inverted weibull continuous random variable that is defined with a standard format and some shape 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 : Inverse weibull continuous random variable

Code #1 : Creating inverted weibull continuous random variable




# importing library
from scipy.stats import invweibull  
    
numargs = invweibull.numargs 
[a] = [0.6, ] * numargs 
rv = invweibull(a) 
    
print ("RV : \n", rv)  

Output :

RV : 
 scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D4EAE9C8


Code #2 : inverted weibull continuous variates and probability distribution




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

Output :

Random Variates : 
 [ 2.46502056 32.97160826  8.65843435  1.21357636  0.22162243  1.05724138
  7.5574935   0.0624836   0.83384033 17.29417907]

Probability Distribution : 
 [0.00613124 0.06733615 0.12799203 0.18757349 0.24553408 0.30131353
 0.35434638 0.40407156 0.44994318 0.49144206]

Code #3 : Graphical Representation.




import numpy as np 
import matplotlib.pyplot as plt 
     
distribution = np.linspace(0, np.minimum(rv.dist.b, 3)) 
print("Distribution : \n", 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 : Varying Positional Arguments




import matplotlib.pyplot as plt 
import numpy as np 
     
x = np.linspace(0, 5, 100
     
# Varying positional arguments 
y1 = invweibull .pdf(x, 1, 3
y2 = invweibull .pdf(x, 1, 4
plt.plot(x, y1, "*", x, y2, "r--"

Output :


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