Multiple inputs for MLP

A few people have asked me how to use the new `CompositeLayer` functionality to feed multiple inputs to an MLP (e.g. @axeldavy).

This wasn't as straightforward as it should have been because the `MLP` class doesn't allow `input_source` to be passed (it is hardcoded to be `'features'` in the `Model` base class). Also, the (very badly named) `VectorSpacesDataset` was very outdated, calling some methods that don't exist anymore (e.g. `Space.validate`), and not implementing other methods that were required by the `FiniteDatasetIterator` (`get_data_specs` and `get_num_examples`) or the SGD algorithm (`return_tuple` argument to `iterator` method).

This PR should allow `input_source` to be passed to an MLP, and make the `VectorSpacesDataset` functional again (enough for basic use at least). I added a unit test as well.
Nested MLPs don't have an `input_space` or `nvis`, so https://github.com/bartvm/pylearn2/blob/multiple_inputs/pylearn2/models/mlp.py#L454 is `False` and this entire part gets skipped. But you are right though, I could just set `input_source='features'` in the call signature, because the class attribute `input_source` never gets set at all for a nested MLP.