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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