In [1]:

    

%matplotlib inline
from sklearn.neighbors import KernelDensity
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd


    

In [2]:

    

# values = np.random.normal(size=(100, 2))
values = np.concatenate((np.random.rand(100, 2) * 100, np.random.rand(25,2)*5))

x_lim = [np.min(values[:,0]), np.max(values[:,0])]
y_lim = [np.min(values[:,1]), np.max(values[:,1])]


    

In [3]:

    

sns.distplot(values[:,0])
sns.distplot(values[:,1])


    

    
Out[3]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fabacdff518>






    

In [4]:

    

plt.plot(values[:,0], values[:,1], 'o')


    

    
Out[4]:





[<matplotlib.lines.Line2D at 0x7faba99ab6d8>]






    

In [5]:

    

density = KernelDensity()
density.fit(values)


    

    
Out[5]:





KernelDensity(algorithm='auto', atol=0, bandwidth=1.0, breadth_first=True,
       kernel='gaussian', leaf_size=40, metric='euclidean',
       metric_params=None, rtol=0)

In [6]:

    

samples = density.sample(10)
scores = density.score_samples(samples)

df = pd.DataFrame({ 'x': samples[:,0], 'y': samples[:,1], 'score': scores})
df


    

    
Out[6]:







  

    

      

      
score
      
x
      
y
    
  
  

    

      
0
      
-5.139336
      
0.526086
      
3.235091
    
    

      
1
      
-7.245322
      
33.159791
      
4.127004
    
    

      
2
      
-5.855119
      
4.337169
      
-0.691243
    
    

      
3
      
-7.133952
      
63.798958
      
98.172368
    
    

      
4
      
-7.384817
      
24.392525
      
78.664047
    
    

      
5
      
-6.034676
      
-0.580813
      
0.882175
    
    

      
6
      
-6.763766
      
96.753210
      
75.944110
    
    

      
7
      
-6.739576
      
0.829453
      
34.725105
    
    

      
8
      
-6.682077
      
56.099210
      
34.285628
    
    

      
9
      
-7.709388
      
48.959824
      
65.713919
    
  

In [7]:

    

argmin_score = np.argmin(scores)
min_sample = samples[argmin_score]
min_score = scores[argmin_score]
print("Min idx: %d, sample: %s, score: %.2f" % (argmin_score, min_sample, min_score))


    

    




Min idx: 9, sample: [ 48.9598241   65.71391925], score: -7.71