Added pairwise_distances_argmin()

Similar to euclidean_distances_argmin(), but compute distances
with any metric from scikit-learn or scipy.
Optimized euclidean case in pairwise_distances_argmin
Removed euclidean_distances_argmin

This specialized case is useless since pairwise_distances_argmin has been
optimized for the euclidean case.
Minor code cleanup
Added gen_batches() to utils

This generator is similar to gen_even_slices(), but takes a slice length
as input instead of a number of slices.
Changed pairwise_distances_argmin API

- Chunking is now performed passing chunk size instead of chunk number.
  Default value has been set to 500 after some benchmarking.
- Dropped the "return_distances" keyword (useless)

Tests have been adapted.
Code cleanup in pairwise_distances_argmin
[DOC] Fixed pairwise_distances_argmin docstring
[API] Renamed pairwise_distances_argmin

The function is now called pairwise_distances_argmin_min.

One of the "batch_size" arguments has been dropped.
[DOC] updated pairwise_distances_argmin_min doc

The updated docstring is slightly ahead of the code. Will be valid
in the next commit.
Performance enhancement for non-euclidean metrics

Non-euclidean metrics passed as a string are now computed more
efficiently by calling pairwise_distances() instead of loop on
every elements in the chunks.

Some docstring updates.
Small fixes
DOC ASCII only in docstrings

As discussed on the mailing list.
ENH speed up NMF (about 30% off topic extraction runtime)
ENH export randomized_svd publicly

... and don't use an import from utils in an example script!
CSR matrix support in pairwise_distances_argmin_min

Updated tests as well.
Added argument metric_kwargs
Merge pull request #2210 from pgervais/distances_argmin

[MRG] Euclidean pairwise distances argmin
Revert "CSR matrix support in pairwise_distances_argmin_min"

This partially reverts commit 6a885cca75f27d53967f3fde87f998cb00926dbf.

Sparse matrix support doesn't actually work for Euclidean distance.
Use pairwise_distances_argmin_min function.

Thanks @pgervais.