Add SVD-based solver to ridge regression.

I added a solver based on the thin SVD of X to compute the ridge
coefficients. This is much more stable than the cholesky decomposition
for singular matrices.

While in the Ridge regression the Gram matrix becomes nonsingular
because of the regularization, I've come across problems when the
regularization parameter is very small. This pull request remedies,
making the algorithm defined and stable even for the border case
alpha=0.

I took the liberty to change the default algorithm of to this one
(when there are no sample weights).
Remove unnecessary code in ridge svd
BUG: solver was not passed to computational method in Ridge object
Use Cholesky solver by default, but use SVD as fallback

when the cholesky fails.
Use ValueError for non-existant solvers
add optional banner to index page to advertise code sprints
link updated
Merge pull request #1996 from jaquesgrobler/DOC_sprint_sponser_banner

add optional banner to index page to advertise code sprints
ENH: avoid MemoryError on manhattan_distances

ENH: avoid MemoryError on manhattan_distances

Very big (large p, large n) problems in manhattan_distances
create large temporaries. We work around this problem with a chunking
approach

MISC: remove debugging print

COSMIT: fix typo

ENH: chunk_manhattan data size in bytes
BUG: old versions of numpy
BUG: old versions of numpy
Corrected a few things on the Mutual Information doc pages.
TST Fixed test_pipeline_methods_preprocessing_svm: pca was unused
Merge pull request #2005 from kmike/test_pipeline_methods_preprocessing_svm

Fixed test_pipeline_methods_preprocessing_svm: pca was unused
Fixed typo in metrics.py
ENH faster unique_labels for big sequences of sequences
Fix L2 regularization order in sgd_fast
DOC explain labels parameter to confusion_matrix
corrected doctests after moving L2 penalty application in SGD
ENH speed up sklearn.feature_selection.chi2

New version is ~4× faster and reuses allocated arrays.