[MRG] Generic multi layer perceptron

Currently I am implementing `layers_coef_` to allow for any number of hidden layers.

This pull request is to implement the generic Multi-layer perceptron as part of the GSoC 2014 proposal. 

The expected time to finish this pull request is June 15

The goal is to extend Multi-layer Perceptron to support more than one hidden layer and to support having a pre-training phase (initializing weights through Restricted Boltzmann Machines) and a fine-tuning phase; and write its documentation.

This directly follows from this pull-request: https://github.com/scikit-learn/scikit-learn/pull/2120
You should use the ellipsis feature of doctestst to have this test pass on all the travis platforms:

```

>>> clf.decision_function([[2., 2.], [1., 2.]])  # doctest: +ELLIPSIS
array([ 11.55..., 11.55...])
```