Advertisement
Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- from scipy.stats import anderson_ksamp
- a = [-1.8, -2.4, -2.4, -0.0, -1.5, -2.7, -1.8, -3.0, -1.8, -1.2, -3.0, -3.0, -2.8, -3.0, -2.1, -0.0, 0.6, -2.5, -2.4, -0.0, -2.7, -0.0, -2.5, -2.1, -0.9, -3.0, -0.6, -0.6, -1.5, -2.2, -1.2, -2.4, -2.4, -3.0, 1.5, -1.8, 1.5, -2.7, -3.0, -2.5, -2.5, -1.5, -1.5, -2.1, -2.1, -3.0, -0.6, -2.7, -3.0, -1.5, -0.6, -0.0, -2.1, 0.6, -2.0, -3.0, -3.0, -2.4, -3.0, -1.8, -0.0, -0.0, -3.0, -1.5, -3.0, -3.0, -1.5, -2.5, -3.0, -2.8, -3.0, -2.2, -0.6, -0.0, -1.5, -2.7, -2.1, -2.1, -2.2, -2.1, -0.6, -0.0, -2.5, -2.1, -1.5, -3.0, -2.2, -1.8, -2.7, -2.4, -1.5, -2.1, -2.9, -2.4, -0.9, -0.0, -0.0, -2.4, -2.7, -0.6, -2.2, -3.0, -1.5, -0.9, -3.0, -3.0, -0.0, -2.7, -2.7, -1.5, -2.2, -3.0, -0.0, 1.5, -3.0, -2.7, -2.2, -2.9, -2.2, -3.0, -1.8, -0.0, -3.0, -1.5, -2.7, -3.0, -3.0, -2.9, -3.0, -3.0, -3.0, -3.0, -2.5, -0.0, 0.9, -3.0, -0.0, -3.0, 3.0, -3.0, -3.0, -1.2, -2.1, 1.5, -0.0, -0.9, -3.0, -2.7, -1.5, -2.4, -2.1, -3.0, -0.9, -3.0, -0.8, -1.5, -2.1, -2.7, -0.0, -0.0, -2.2, -1.8, -2.1, -2.2, -3.0, 0.6, -2.4, -2.2, -2.4, -2.5, -1.5, -0.0, -2.7, -3.0, -3.0, -2.1, -0.0, -2.4, -2.4, -0.0, -2.0, -0.9, -2.4, -3.0, -1.4, -2.7, -2.7, -3.0, -3.0, -2.7, -1.2, -2.1, -3.0, -0.0, -3.0, -2.7, -2.7, -3.0, -3.0, -2.5, -3.0, -1.8, -1.5, -2.7, -2.4, -1.8, -3.0, -2.7, 2.1, -3.0, -2.2, -2.2, 0.6, -0.9, 6.0, -3.0, -2.1, -3.0, -2.1, -2.5, -3.0, -1.5, -2.5, 3.0, -2.1, -3.0, -3.0, -1.5, -2.1, -2.7, -2.5, 1.5, -2.1, -0.0, -0.0, -3.0, -0.6, 1.5, -2.7, -2.4, -2.1, -3.0, -2.7, -3.0, 9.0, -3.0, -1.7, -3.0, -0.0, -3.0, -2.2, -0.0, -0.6, -2.7, -2.7, -3.0, -3.0, -1.7, -2.1, -2.0, -3.0, -2.1, -3.0, -1.1, -3.0, -3.0, -2.4, -1.5, -3.0, -2.2, -3.0, -1.5, -2.7, -3.0, -3.0, -3.0, -2.2, -3.0, -2.1, -2.1, -2.4, -3.0, -3.0, -0.0, -3.0, -3.0, -2.1, -2.0, 1.5, -3.0, -3.0, -2.1, -2.9, -2.4, -3.0, -3.0, -1.5, -2.2, -0.9, -1.8, -2.1, -1.8, -1.5, -3.0, -1.5, -3.0, -1.5, -3.0, -2.4, 1.5, -2.7, -3.0, -1.8, -1.8, -1.5, -2.1, -2.7, -2.7, -2.7, -3.0, -1.5, -2.7, -3.0, -2.7, -3.0, -1.5, -1.5, -3.0, 0.6, -0.6, -3.0, -2.1, -2.4, -2.1, -3.0, -2.2, -3.0, -1.8, -1.2, -3.0, -0.8, -2.4, -2.5, -3.0, -1.5, -1.2, -0.0, -2.7, -2.4, -3.0, -3.0, -2.1, -2.1, -2.1, -3.0, -2.7, -2.4, -2.1, -1.5, -2.1, -0.6, -3.0, -3.0, -3.0, -3.0]
- b = [-1.2, 6.0, -3.0, 1.5, 12.0, 1.5, 3.0, -0.0, -1.5, -0.0, 6.0, -0.0, 1.8, -1.5, -3.0, -3.0, 1.5, -0.0, 1.5, -3.0, -3.0, -0.0, -1.2, -3.0, 22.5, -0.0, -3.0, -3.0, -2.1, 3.0, 2.4, 1.5, -2.1, 4.5, -3.0, -3.0, 12.0, 6.0, -3.0, 3.0, -3.0, 12.0, -2.8, 0.6, 4.5, 3.0, -0.0, -3.0, -1.0, -3.0, -0.0, 3.0, 2.1, -0.6, -3.0, -3.0, 1.5, 1.5, -0.6, -0.0, 1.5, 6.0, -2.2, -2.1, -0.0, -0.8, 6.0, -3.0, -3.0, 9.0, -3.0, -0.9, 15.0, 1.5, -2.9, 19.5, 4.5, -3.0, 1.5, -1.8, 1.5, 0.9, -3.0, -3.0, 9.0, -0.3, 9.0, -2.1, -2.2, -3.0, 12.0, -2.1, -1.2, -3.0, 4.5, -1.2, -0.0, 12.0, -0.9, -3.0, -0.6, -3.0, -1.5, -0.0, 27.0, 4.5, -2.4, -2.7, -0.0, 3.0, 1.5, 3.0, -3.0, 16.5, -2.1, -0.6, -3.0, -1.5, -1.2, -3.0, 4.4, -2.5, -2.1, 3.0, -1.2, -3.0, 12.0, -1.8, -0.9, -3.0, -3.0, -0.0, -3.0, -1.2, -3.0, -3.0, -0.0, 1.5, -3.0, 7.5, -3.0, -2.1, 3.0, 1.5, 3.0, -3.0, 1.5, -3.0, 19.5, -3.0, -2.2, 27.0, 3.0, -1.5, -3.0, -3.0, 4.5, -1.2, 12.0, 3.0, 3.0, -2.1, 6.0, -2.2, -3.0, -2.4, 6.0, -3.0, -1.9, -0.6, -0.0, 3.6, 15.0, -3.0, -3.0, 7.5, -0.0, 4.5, -2.4, -3.0, -3.0, -3.0, -2.0, -3.0, -3.0, -3.0, -2.6, 3.0, -0.0, -3.0, 4.5, -1.2, -3.0, -3.0, 3.0, -3.0, 1.6, 1.5, -3.0, -3.0, -3.0, -3.0, 3.0, -3.0, -1.5, -3.0, -1.5, 12.0, -1.5, 3.0, 9.6, -0.0, -1.8, -3.0, -2.1, -0.6, 9.0, -3.0, 19.5, -2.4, -1.8, 15.0, -3.0, -3.0, 27.0, 7.5, 12.0, -3.0, -2.0, -3.0, 12.0, -3.0, -3.0, 0.9, -3.0, -2.5, 12.6, -1.5, -0.6, -3.0, -3.0, -0.0, -0.8, -3.0, 3.0, -0.0, 4.5, -3.0, -0.0, 0.6, 0.3, -2.7, -0.0, 4.5, -3.0, 3.0, -1.5, 9.0, -3.0, 1.5, -3.0, 6.0, -2.2, -0.0, -3.0, -3.0, 3.0, -3.0, -2.7, -0.0, -3.0, 0.6, 4.5, 1.5, 1.5, 1.5, -2.1, 7.5, -3.0, -3.0, -3.0, -2.1, -3.0, 6.0, -1.5, -2.0, -3.0, -0.6, 6.0, -1.5, -3.0, 6.0, -3.0, 3.0, 3.0, -3.0, 7.5, -3.0, -0.0, -3.0, -0.0, -3.0, -3.0, 0.6, -3.0, -3.0, -3.0, -3.0, -2.4, -0.0, 1.5, 3.0, 2.1, -3.0, 3.0, 4.5, -3.0, -2.4, -3.0, -2.2, 7.5, 2.1, -3.0, -0.0, -2.0, -3.0, -3.0, -1.8, -3.0, 4.5, -1.5, -3.0, 4.5, 3.0, 15.0, 4.5, -2.1, 12.0, 6.0, 4.5, 27.0, -3.0, 3.0, 0.6, -3.0, 0.6, -2.4, 4.5, -1.5, -2.2, 12.0, -2.0, 1.5, 9.0, -1.5, -1.2, -2.0, 1.5, -1.2, -1.8, -3.0, -0.0, -0.0, -3.0, -0.9]
- print(anderson_ksamp([a,b]))
- Anderson_ksampResult(statistic=53.560696338122263, critical_values=array([ 0.325, 1.226, 1.961, 2.718, 3.752]), significance_level=7238105.535194747)
- from scipy.stats import ks_2samp
- from statsmodels.distributions.empirical_distribution import ECDF
- import matplotlib.pyplot as plt
- import numpy as np
- a = [-1.8, -2.4, -2.4, -0.0, -1.5, -2.7, -1.8, -3.0, -1.8, -1.2, -3.0, -3.0, -2.8, -3.0, -2.1, -0.0, 0.6, -2.5, -2.4, -0.0, -2.7, -0.0, -2.5, -2.1, -0.9, -3.0, -0.6, -0.6, -1.5, -2.2, -1.2, -2.4, -2.4, -3.0, 1.5, -1.8, 1.5, -2.7, -3.0, -2.5, -2.5, -1.5, -1.5, -2.1, -2.1, -3.0, -0.6, -2.7, -3.0, -1.5, -0.6, -0.0, -2.1, 0.6, -2.0, -3.0, -3.0, -2.4, -3.0, -1.8, -0.0, -0.0, -3.0, -1.5, -3.0, -3.0, -1.5, -2.5, -3.0, -2.8, -3.0, -2.2, -0.6, -0.0, -1.5, -2.7, -2.1, -2.1, -2.2, -2.1, -0.6, -0.0, -2.5, -2.1, -1.5, -3.0, -2.2, -1.8, -2.7, -2.4, -1.5, -2.1, -2.9, -2.4, -0.9, -0.0, -0.0, -2.4, -2.7, -0.6, -2.2, -3.0, -1.5, -0.9, -3.0, -3.0, -0.0, -2.7, -2.7, -1.5, -2.2, -3.0, -0.0, 1.5, -3.0, -2.7, -2.2, -2.9, -2.2, -3.0, -1.8, -0.0, -3.0, -1.5, -2.7, -3.0, -3.0, -2.9, -3.0, -3.0, -3.0, -3.0, -2.5, -0.0, 0.9, -3.0, -0.0, -3.0, 3.0, -3.0, -3.0, -1.2, -2.1, 1.5, -0.0, -0.9, -3.0, -2.7, -1.5, -2.4, -2.1, -3.0, -0.9, -3.0, -0.8, -1.5, -2.1, -2.7, -0.0, -0.0, -2.2, -1.8, -2.1, -2.2, -3.0, 0.6, -2.4, -2.2, -2.4, -2.5, -1.5, -0.0, -2.7, -3.0, -3.0, -2.1, -0.0, -2.4, -2.4, -0.0, -2.0, -0.9, -2.4, -3.0, -1.4, -2.7, -2.7, -3.0, -3.0, -2.7, -1.2, -2.1, -3.0, -0.0, -3.0, -2.7, -2.7, -3.0, -3.0, -2.5, -3.0, -1.8, -1.5, -2.7, -2.4, -1.8, -3.0, -2.7, 2.1, -3.0, -2.2, -2.2, 0.6, -0.9, 6.0, -3.0, -2.1, -3.0, -2.1, -2.5, -3.0, -1.5, -2.5, 3.0, -2.1, -3.0, -3.0, -1.5, -2.1, -2.7, -2.5, 1.5, -2.1, -0.0, -0.0, -3.0, -0.6, 1.5, -2.7, -2.4, -2.1, -3.0, -2.7, -3.0, 9.0, -3.0, -1.7, -3.0, -0.0, -3.0, -2.2, -0.0, -0.6, -2.7, -2.7, -3.0, -3.0, -1.7, -2.1, -2.0, -3.0, -2.1, -3.0, -1.1, -3.0, -3.0, -2.4, -1.5, -3.0, -2.2, -3.0, -1.5, -2.7, -3.0, -3.0, -3.0, -2.2, -3.0, -2.1, -2.1, -2.4, -3.0, -3.0, -0.0, -3.0, -3.0, -2.1, -2.0, 1.5, -3.0, -3.0, -2.1, -2.9, -2.4, -3.0, -3.0, -1.5, -2.2, -0.9, -1.8, -2.1, -1.8, -1.5, -3.0, -1.5, -3.0, -1.5, -3.0, -2.4, 1.5, -2.7, -3.0, -1.8, -1.8, -1.5, -2.1, -2.7, -2.7, -2.7, -3.0, -1.5, -2.7, -3.0, -2.7, -3.0, -1.5, -1.5, -3.0, 0.6, -0.6, -3.0, -2.1, -2.4, -2.1, -3.0, -2.2, -3.0, -1.8, -1.2, -3.0, -0.8, -2.4, -2.5, -3.0, -1.5, -1.2, -0.0, -2.7, -2.4, -3.0, -3.0, -2.1, -2.1, -2.1, -3.0, -2.7, -2.4, -2.1, -1.5, -2.1, -0.6, -3.0, -3.0, -3.0, -3.0]
- b = [-1.2, 6.0, -3.0, 1.5, 12.0, 1.5, 3.0, -0.0, -1.5, -0.0, 6.0, -0.0, 1.8, -1.5, -3.0, -3.0, 1.5, -0.0, 1.5, -3.0, -3.0, -0.0, -1.2, -3.0, 22.5, -0.0, -3.0, -3.0, -2.1, 3.0, 2.4, 1.5, -2.1, 4.5, -3.0, -3.0, 12.0, 6.0, -3.0, 3.0, -3.0, 12.0, -2.8, 0.6, 4.5, 3.0, -0.0, -3.0, -1.0, -3.0, -0.0, 3.0, 2.1, -0.6, -3.0, -3.0, 1.5, 1.5, -0.6, -0.0, 1.5, 6.0, -2.2, -2.1, -0.0, -0.8, 6.0, -3.0, -3.0, 9.0, -3.0, -0.9, 15.0, 1.5, -2.9, 19.5, 4.5, -3.0, 1.5, -1.8, 1.5, 0.9, -3.0, -3.0, 9.0, -0.3, 9.0, -2.1, -2.2, -3.0, 12.0, -2.1, -1.2, -3.0, 4.5, -1.2, -0.0, 12.0, -0.9, -3.0, -0.6, -3.0, -1.5, -0.0, 27.0, 4.5, -2.4, -2.7, -0.0, 3.0, 1.5, 3.0, -3.0, 16.5, -2.1, -0.6, -3.0, -1.5, -1.2, -3.0, 4.4, -2.5, -2.1, 3.0, -1.2, -3.0, 12.0, -1.8, -0.9, -3.0, -3.0, -0.0, -3.0, -1.2, -3.0, -3.0, -0.0, 1.5, -3.0, 7.5, -3.0, -2.1, 3.0, 1.5, 3.0, -3.0, 1.5, -3.0, 19.5, -3.0, -2.2, 27.0, 3.0, -1.5, -3.0, -3.0, 4.5, -1.2, 12.0, 3.0, 3.0, -2.1, 6.0, -2.2, -3.0, -2.4, 6.0, -3.0, -1.9, -0.6, -0.0, 3.6, 15.0, -3.0, -3.0, 7.5, -0.0, 4.5, -2.4, -3.0, -3.0, -3.0, -2.0, -3.0, -3.0, -3.0, -2.6, 3.0, -0.0, -3.0, 4.5, -1.2, -3.0, -3.0, 3.0, -3.0, 1.6, 1.5, -3.0, -3.0, -3.0, -3.0, 3.0, -3.0, -1.5, -3.0, -1.5, 12.0, -1.5, 3.0, 9.6, -0.0, -1.8, -3.0, -2.1, -0.6, 9.0, -3.0, 19.5, -2.4, -1.8, 15.0, -3.0, -3.0, 27.0, 7.5, 12.0, -3.0, -2.0, -3.0, 12.0, -3.0, -3.0, 0.9, -3.0, -2.5, 12.6, -1.5, -0.6, -3.0, -3.0, -0.0, -0.8, -3.0, 3.0, -0.0, 4.5, -3.0, -0.0, 0.6, 0.3, -2.7, -0.0, 4.5, -3.0, 3.0, -1.5, 9.0, -3.0, 1.5, -3.0, 6.0, -2.2, -0.0, -3.0, -3.0, 3.0, -3.0, -2.7, -0.0, -3.0, 0.6, 4.5, 1.5, 1.5, 1.5, -2.1, 7.5, -3.0, -3.0, -3.0, -2.1, -3.0, 6.0, -1.5, -2.0, -3.0, -0.6, 6.0, -1.5, -3.0, 6.0, -3.0, 3.0, 3.0, -3.0, 7.5, -3.0, -0.0, -3.0, -0.0, -3.0, -3.0, 0.6, -3.0, -3.0, -3.0, -3.0, -2.4, -0.0, 1.5, 3.0, 2.1, -3.0, 3.0, 4.5, -3.0, -2.4, -3.0, -2.2, 7.5, 2.1, -3.0, -0.0, -2.0, -3.0, -3.0, -1.8, -3.0, 4.5, -1.5, -3.0, 4.5, 3.0, 15.0, 4.5, -2.1, 12.0, 6.0, 4.5, 27.0, -3.0, 3.0, 0.6, -3.0, 0.6, -2.4, 4.5, -1.5, -2.2, 12.0, -2.0, 1.5, 9.0, -1.5, -1.2, -2.0, 1.5, -1.2, -1.8, -3.0, -0.0, -0.0, -3.0, -0.9]
- ecdf1, ecdf2 = ECDF(a), ECDF(b)
- xs = np.linspace(min(a+b),max(a+b), num=10000)
- plt.figure(figsize=(12,8))
- plt.plot(xs,ecdf1(xs), xs,ecdf2(xs))
- plt.show()
- print(ks_2samp(a,b))
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement