MLPACK 2.0.0 发布，C++ 的机器学习库
* Parallelization: the DET (density estimation trees) code is nowparallelized with OpenMP. As time goes on, parallelization will be
added to other algorithms, but note that you can also use Armadillo
with OpenBLAS, which will parallelize all the linear algebra calls.
* Model saving and loading: where appropriate, all of the command-line
programs now support loading and saving models. So you can train,
say, a logistic regression model, and save it for later use. This is
also possible with techniques like all-k-nearest-neighbor search,
which allow you to save the tree built on the points. Model
serialization support is also available from C++, too, of course.
* Significant refactoring: most machine learning algorithms now follow
the same API, and documentation has been improved.
* Tree-based algorithms now support multiple types of trees in a far
easier manner.
* The k-means code now supports five different algorithms, many of them
far faster than the original implementation.
* Add streaming decision trees (Hoeffding trees) for fast classifiers
on huge datasets. This supports both categorical and numeric
features.
* No more dependence on libxml2; boost::serialization is used instead.
* Armadillo minimum version bump to 4.100.0.
* All mlpack programs are now prefixed with 'mlpack_', so for instance
'allknn' is now 'mlpack_allknn'.
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