[MRG+1] LDA refactoring

OK, here is a new version of the LDA class with (optional) shrinkage. The one in #3105 was already very messy, so I thought I'd start from scratch. By default, nothing has changed and the old SVD-based code is used.

When using the shrinkage approach based on Ledoit Wolf shrinkage, it is important that the data are standardized (centered and scaled to unit standard deviation).

TODO list:

- [x] `svd` solver
- [x] `lsqr` solver
- [x] `eigen` solver
- [x]  Ledoit Wolf shrinkage (`lsqr` and `eigen` solvers only)
- [x] support for numerical value for `alpha`
- [x] unit tests
  - [x] test that solvers return the same results (up to numerical errors)
  - [x] test the transform method
The default is actually `False` because the old SVD implementation set it to `False` by default. This makes sense, because that's the main advantage of SVD (you don't need to compute the covariance matrix). I had to change the API here slightly and move the parameter `store_covariance` from the `fit()` method to `__init__()`.