In [1]:

    

%matplotlib inline
import numpy as np
from sklearn.neighbors import NearestNeighbors
from sklearn import preprocessing
import matplotlib.pyplot as plt


    

In [2]:

    

lrgs = np.loadtxt('../data/CFHTLS_LRGs.txt')
features = lrgs[:,2:]
positions = lrgs[:,:2]
scaler = preprocessing.StandardScaler().fit(features)
scaled_features = scaler.transform(features)
test_gal = np.array([.6,25,24,23,22,21])
scaled_test = scaler.transform(test_gal)


    

In [55]:

    

nbrFinder = NearestNeighbors(n_neighbors=1,algorithm='auto',metric='euclidean').fit(scaled_features)


    

In [56]:

    

dists, inds = nbrFinder.kneighbors(scaled_test)


    

In [57]:

    

print dists, inds


    

    




[[ 0.19256446]] [[4247]]

In [59]:

    

positions[4247,:]


    

    
Out[59]:





array([ 34.79139747,  -9.97309596])

In [61]:

    

type(scaler)


    

    
Out[61]:





sklearn.preprocessing.data.StandardScaler

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