Python – Kolmogorov-Smirnov Distribution in Statistics
scipy.stats.kstwobign() is Kolmogorov-Smirnov two-sided test for large N test that is defined with a standard format and some shape parameters to complete its specification. It is a statistical test that measures the maximum absolute distance of the theoretical CDF from the empirical CDF.
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.
Results : kstwobign continuous random variable
Code #1 : Creating kstwobign continuous random variable
from scipy.stats import kstwobign
numargs = kstwobign.numargs
a, b = 4.32 , 3.18
rv = kstwobign(a, b)
print ( "RV : \n" , rv)
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Output :
RV :
scipy.stats._distn_infrastructure.rv_frozen object at 0x000002A9D54959C8
Code #2 : kstwobign continuous variates and probability distribution
import numpy as np
quantile = np.arange ( 0.01 , 1 , 0.1 )
R = kstwobign.rvs(a, b, scale = 2 , size = 10 )
print ( "Random Variates : \n" , R)
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Output :
Random Variates :
[3.88510141 3.48394857 3.66124797 3.88484201 3.86533511 3.21176073
4.10238585 3.42397866 3.85111721 4.36433596]
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))
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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 )
y1 = kstwobign .pdf(x, 1 , 3 )
y2 = kstwobign .pdf(x, 1 , 4 )
plt.plot(x, y1, "*" , x, y2, "r--" )
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Output :
Last Updated :
10 Jan, 2020
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