Python – Weibull Minimum Distribution in Statistics

10 Jan, 2020
scipy.stats.weibull_min() is a Weibull minimum continuous random variable. It is inherited from the of generic methods as an instance of the rv_continuous class. It completes the methods with details specific for this particular distribution.
Parameters :
q : lower and upper tail probabilityx : quantilesloc : [optional]location parameter. Default = 0scale : [optional]scale parameter. Default = 1size : [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 : Weibull minimum continuous random variable
Code #1 : Creating Weibull minimum continuous random variable
# importing library  from scipy.stats import weibull_min     numargs = weibull_min .numargs a, b = 0.2, 0.8rv = weibull_min (a, b)     print ("RV : \n", rv)  
Output :
RV : 
 scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9DA00E108

Code #2 : Weibull minimum continuous variates and probability distribution
import numpy as np quantile = np.arange (0.01, 1, 0.1)   # Random Variates R = weibull_min .rvs(a, b, size = 10) print ("Random Variates : \n", R)   # PDF x = np.linspace(weibull_min.ppf(0.01, a, b),                weibull_min.ppf(0.99, a, b), 10)R = weibull_min.pdf(x, 1, 3)print ("\nProbability Distribution : \n", R) 
Output :
Random Variates : 
 [12.76832063  0.80471316  0.80000281  0.80001071  0.80000427  2.1282417
  1.9774416  27.87159473  0.80431529  0.80000885]

Probability Distribution : 
 [0.00000000e+000 1.01939341e-099 1.15142533e-199 1.30055804e-299
 0.00000000e+000 0.00000000e+000 0.00000000e+000 0.00000000e+000
 0.00000000e+000 0.00000000e+000]

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.         0.04081633 0.08163265 0.12244898 0.16326531 0.20408163
 0.24489796 0.28571429 0.32653061 0.36734694 0.40816327 0.44897959
 0.48979592 0.53061224 0.57142857 0.6122449  0.65306122 0.69387755
 0.73469388 0.7755102  0.81632653 0.85714286 0.89795918 0.93877551
 0.97959184 1.02040816 1.06122449 1.10204082 1.14285714 1.18367347
 1.2244898  1.26530612 1.30612245 1.34693878 1.3877551  1.42857143
 1.46938776 1.51020408 1.55102041 1.59183673 1.63265306 1.67346939
 1.71428571 1.75510204 1.79591837 1.83673469 1.87755102 1.91836735
 1.95918367 2.        ]
  
Code #4 : Varying Positional Arguments
import matplotlib.pyplot as plt import numpy as np   x = np.linspace(0, 5, 100)      # Varying positional arguments y1 = weibull_min.pdf(x, a, b) y2 = weibull_min.pdf(x, a, b) plt.plot(x, y1, "*", x, y2, "r--") 
Output :
Python scipy-stats-functions
Python
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