* Switching to having the extractor pass whole feature sequences, instead of just the hashes
* Switching extractor to use trie
* Still updating learner to be compatible with trie-based feature extractor
* Allow learner to accept variable-sized feature vectors, by having it resize its internal weight_lines variable as appropriate.
* Upd test
* Use weight_t definition from typedefs
* Use float-valued weights, instead of integer weights
* Switch to using AdaDelta weight updates, from http://www.matthewzeiler.com/pubs/googleTR2012/googleTR2012.pdf
* Change Instance to Brain, a sort of integration-point of a model and extractor
* Rewrite beam class, while doing NER parsing stuff.
* Fix initialization callback in beam.
* Bugfixes to beam. Also add scores, is_valid and costs matrices onto beam, to prevent clients from having to allocate these.
* Don't import Instance
* Fix setup.py
* Tmp work on integration class
* Bug fix to ADADELTA: on first iteration, RMS of the update should be eta, not zero.
* Add new beam tests
* Add new tests
Merge branch 'master' into tmp