Feature: loss weights

This feature implements the possibility to use custom weighting on the loss for supervised methods (for unsupervised methods it can be added analogously). In order to use it for Rnns, just specify `weights=True` in the constructor, and of course, instead of providing the fit/evaluation data as (input, target), (input, target, weights) should be provided.

**Bonus**: there is also added a `OneLinePrinter` class, a reporter used in order to have a trainer output in the same fashion as the notebook examples (which don't use a reporter).