Reduce creating std::string objects, too. In both ScoreArray
and FeatureArray classes, the private members to track sentence
indices (namely, "m_index") were unnecessarily declared as
std::string, but it's better to directly declare them as 'int'.
Regression tests all check out, and kbmira seems to work fine
on a Hansard French->English task.
HypPackEnumerator class may be of interest to pro.cpp and future
optimizers, as it abstracts a lot of the boilerplate involved in
enumerating multiple k-best lists.
MiraWeightVector is not really mira-specific - just a weight vector
that enables efficient averaging. Could be useful to a perceptron
as well. Same goes for MiraFeatureVector.
Interaction with sparse features is written, but untested.
- Use the function in Data::InitFeatureMap().
- Add an unit test for InitFeatureMap().
- Move helper functions for Data::loadnbest() to public for unit testing.
