In [49]:

    

from sklearn import preprocessing
from sklearn.kernel_approximation import Nystroem
from sklearn.metrics.pairwise import pairwise_kernels
import numpy as np

import random
from collections import Counter

def fast_cov(X):
    # assume...correlation and covariant are interchangeable...
    X_scaled = preprocessing.scale(X)
    #X_approx_cor = pairwise_kernels(X_scaled.T, metric='cosine')
    X_approx_cor = Nystroem("cosine").fit_transform(X_scaled.T)
    X_cor = X_approx_cor.dot(X_approx_cor.T)
    return X_cor


    

In [50]:

    

b = np.random.normal(size=(110, 1100))


    

In [51]:

    

XX = fast_cov(b)


    

In [52]:

    

XX1 = pairwise_kernels(b.T, metric='cosine')


    

In [53]:

    

XX1.shape


    

    
Out[53]:





(1100, 1100)

In [54]:

    

XX.shape


    

    
Out[54]:





(1100, 1100)

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