ENH support multiclass targets of string objects
BUG don't densify sparse matrix in BernoulliRBM.score_samples

Also documented the non-determinism of this method.
ENH speed up progress reporting in RBM

Previously took up to 45% (!) of the time in training, not the 10% promised
in the docstring, for the digits example. Now down to at most 3%.

Pseudo-likelihood is now reported for the entire X, rather than batch-by-batch,
which will take more memory.
TST disable non-doctests in Comp. Perf. docs

Btw., why is this in the modules/ directory?
DOC {min,max}_df look at document freq, not term freq
Merge pull request #2626 from jnothman/string_objects

ENH support multiclass targets of dtype=object strings
DOC Add what's new entry for #2626
DOC fix BernoulliRBM._fit docstring

Return value is gone now. Thanks @jnothman.
Merge pull request #2642 from larsmans/rbm-speedup

[MRG] faster convergence reporting in RBMs
ENH: faster cartesian product in make_classification and document complexity

This version
* is 4-6x faster
* takes less memory, and
* (on my box) is more likely to give a decent MemoryError instead of
  triggering the OOM killer, because it allocates in one go.

See #2534.
Revert "ENH: faster cartesian product in make_classification and document complexity"

This reverts commit 03352bf5f96e01487fd7bdcb49fb8416cabcf6ea.

Apparently doesn't work with older NumPy.