[WIP] Ridge path

Adressing https://github.com/scikit-learn/scikit-learn/issues/3373

A first implementation of a ridge path using `'eigen'` solver. This solver is now available in `ridge_regression`, which calls upon a reduced functionality of `ridge_path`.

Next steps:

- The same path approach can be straightforwardly used with SVD as well: Write svd path and replace in ridge_regression
- Attack `RidgeCV`: Replace the `GridSearchCV` by `ridge_path` if `eigen` or `svd` are selected.
- Incorporate `ridge_path_looe` from the gist https://gist.github.com/eickenberg/e9eb106f12e40d017fd0
- Adjust `RidgeCV` to use the looe functionalities


Indeed this is always negative. The `1. / alphas` outside the singular values comes from the fact that `VT` was created with `full_matrices=False` and we need to take into account the nullspace of the design. This is shown in the chapter on "The SVD method" in the "Notes on regularized least squares".