[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


Turns out that if I crop singular values and don't add the penalty to those dimensions, then I would have to take those dimensions into account outside this eigenvalue inverse as in the SVDgcv method. So I think it should stay this way.