Accelerate AffinityPropagation.

Accelerate AffinityPropagation for large inputs using standard tricks
(in-place operations and out parameters) to avoid extra allocations.

Basic tests on datasets for 1000 points in 2D show ~10-20% increase in
speed.
Add random forests to document classification example.
Minor typo in plot_dict_face_patches.py
FIX: Raise error when patch width/height is greater than image width/height
Update documentation.rst

Update documentation header. See #3827.
AffinityPropagation: save memory by reusing tmp.
added n_init check for k_means()
Merge pull request #3871 from anntzer/fast-affinity-propagation

[MRG+2] Accelerate AffinityPropagation.
DOC fix what's new for sparse trees
Merge pull request #3858 from banilo/km_init

k-means: add n_init argument check
MAINT bump joblib to 0.8.4

This includes a fix for #3835 (under Python 3.3+ only).
DOC add documentation improvements to what's new
DOC update whats_new.rst for AffinityPropagation optim
ENH add coverage multilabel ranking metric
Remove copy paste mistake
DOC improve documentation of coverage
TST make separate tests for coverage + remove redundant tests with commons