[MRG] 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
Please follow PEP 257 for docstrings:

```python
def generate_data(n_samples, n_features, random_state=0):
    """Generate random blob-ish data with.
    
    This returns an array of input data with shape `(n_samples, n_features)` and
     an array of `n_samples` target labels.

    Only one feature contains discriminative information, the other features
    contain only noise.
    """
    ...
```