ENH speed up RBM training with scipy.special.expit

SciPy 0.10 already has an implementation of the logistic function called
scipy.special.expit.

Using this makes RBM training 12% faster, as measured by observing the time
per iteration as reported by the plot_rbm_logistic_classification.py example,
disregarding the first iteration as an outlier (it's consistently faster than
the rest, not sure why). The speedup is in the expit function, not the
inplace operations; those are there to make benchmarking easier, but they
certainly won't hurt.
ENH re-instate extmath.logistic_sigmoid

For backward compat only, so immediately deprecated.
FIX DictVectorizer handling of empty inputs

Fixes #2883.
TST older nosetests compat in DictVectorizer test
putting cluster  on new line to solve sphinx documentation formatting issue
Merge pull request #2882 from larsmans/expit

Speed up RBM training with scipy.special.expit
Merge pull request #2884 from dsullivan7/clusterdoc

Sphinx formatting issue for Coclustering and Biclustering
[TYPO] s/migh/might/
Update supervised.py
Update supervised.py
Merge pull request #2895 from kaushik94/master

 supervised.py with updated documentation
Update plot_forest_importances.py

import statement has moved to the top of the file
Merge pull request #2897 from ugurthemaster/patch-1

Update plot_forest_importances.py