WIP: Refactor euclidean_distance, add optional argument for preallocated output array. #483
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Explicit is better than implicit!
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| X : ndarray, float32, shape = [n_samples_a, n_features] | ||
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| Y : ndarray, float32, shape = [n_sample_b, n_features] |
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It seems that under certain circumstances, using BLAS might be faster, but loses precision in certain cases. @jakevdp has done some benchmarking. In an ideal world, these Cython functions should try and guess whether the BLAS call is worth it and do it. |
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Code to bench (create a '.ipy' file and run it in IPython): import timeit
from sklearn.metrics.pairwise import euclidean_distances
import numpy as np
np.random.seed(0)
for n_features in (10, 100, 1000):
for n_samples in (10, 100, 1000):
Y = np.random.random(size=(n_features, n_samples))
X = np.random.random(size=(n_features, n_samples))
print 'n_features: % 2i, n_samples % 2i' % (n_features, n_samples)
%timeit euclidean_distances(X, Y)
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another implementation I found in milk: https://github.com/luispedro/milk/blob/master/milk/unsupervised/pdist.py |
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This pull request adds
euclidean_distance.euclidean_distanceis operating on floating point arrays (general consensus in the sprint room is that it doesn't usually make much sense in the integer case and if you really want that, you can cast).outargument foreuclidean_distance(only in the dense-dense case; raises an error otherwise).