In [20]:

    

import scipy.stats
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline


    

In [32]:

    

nat = scipy.stats.norm.rvs(loc=-0.5, scale=0.5, size=1000)
droog = scipy.stats.norm.rvs(loc=0.5, scale=0.5, size=1000)


    

In [34]:

    

_ = plt.hist(nat, label='nat')
_ = plt.hist(droog, label='droog')
plt.legend()


    

    
Out[34]:





<matplotlib.legend.Legend at 0x111c58750>






    

In [ ]:

    




    

In [36]:

    

test = scipy.stats.norm.rvs(loc=0, scale=1, size=1000)
_ = plt.hist(test)


    

In [39]:

    

loc_nat, scale_nat = scipy.stats.norm.fit(nat)
loc_droog, scale_droog = scipy.stats.norm.fit(droog)


    

In [58]:

    

z_nat = lambda value: (value - loc_nat)/scale_nat
z_droog = lambda value: (value - loc_droog)/scale_droog


    

In [66]:

    

isnat = np.abs(z_nat(test)) < np.abs(z_droog(test))
_ = plt.hist(test[isnat], label='nat')
_ = plt.hist(test[~isnat], label='droog')
plt.legend()


    

    
Out[66]:





<matplotlib.legend.Legend at 0x111561a50>






    

In [68]:

    

import sklearn.neighbors.NearestNeighbors


    

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