Don't save entire list of ranked cells long-term for each test doc
Add output for error investigation of reranking
Reorder Evaluation.scala, grouping related classes together
Use indexed sequence (Vector) instead of Seq (List) in many cases for memory/etc. efficiency
Add various word-matching feature variants for reranking
Clean up and generalize memoization code, remove duplication
Cleanup of feature-vector code and potential bug fix (no intercept term previously)
Fix bug in getting correct feature-vector lengths
Various stylistic changes to code, iterate_* -> iter_*
Clean up MeteredTask code
Style cleanups in closures
Fix bug with test-document words unseen in training
Add task progress messages to perceptron iterations
Use toIndexedSeq not toIterable to generate training data as Vector, not lazy Stream (which causes problems with mutable state, e.g. memoization)
Add score to end of features, not beginning, to keep original implementation of feature Iterable
Add logging of feature-vector size, some cleanup of rerank/perceptron code, removal of old code
Add resource usage results at end of MeteredTask, incorporate maxitems into MeteredTask, clean up Evaluation handling of printing results
Implement compressed (and semi-compressed) feature vectors, use by default; around 10x speed/memory improvement
Augment set of top answers used in generating feature vectors for given document with correct one, if needed
Add some info for debugging generation of rerank training instances