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* add quantile metric.

* first draft.

* add "sqrt" transform in regression objective function.

* fix a bug.

* update parameter doc

* fix doc

* Fix coding style (#969)

Function names must be in the "Pascal Case" style.

* check_elements_interval_closed to CheckElementsIntervalClosed

* obtain_min_max_sum to ObtainMinMaxSum

* Change functions in common.h into template functions (#969)

* CheckElementsIntervalClosed

* ObtainMinMaxSum

These two functions were changed into template functions.

* Remove an unpreferable overload

* remove an overload of the function ObtainMinMaxSum

* Use stringstream to format T type

Function names must be in the "Pascal Case" style.

* check_elements_interval_closed to CheckElementsIntervalClosed

* obtain_min_max_sum to ObtainMinMaxSum

* Attempt to address issue #807 Poisson regression objective.

* Fix coding style.

* Added label non-negative safety check.

* Revert "python-package: support valid_names in scikit-learn API (#420)"

This reverts commit de39dbcf.

* Revert "faster histogram sum up (#418)"

This reverts commit 98c7c2a3.

* some refactor.

* two stage sum up to reduce sum up error.

* add more two-stage sumup.

* some refactor.

* add alignment.

* change name to aligned_allocator.

* remove some useless sumup.

* fix a warning.

* add -march=native .

* remove the padding of gradients.

* no alignment.

* fix test.

* change KNumSumupGroup to 32768.

* change gcc flags.

* many refactors.

* remove multi_loglossova.

* fix tests.

* avoid using lambda function.

* fix some format.

* reduce branching.

* reduce the sumup cost of constant hessians.

* fix test.

* fix bug when have weights.

* fix a comment.

* reduce branching.

* Support Poisson regression

Close #233

* update docs

http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node24.html

* fix typo

* fix hessians to approximate hessians with Gaussian function

* fix ApproximateHessianWithGaussian

* take fabs of gradient

* use atan(1) to calculate pi

* fix pi

* Add L1 objective function

* fix hessians

* update

* Add Huber loss

https://en.wikipedia.org/wiki/Huber_loss

* update

* update docs/Parameters.md

* update

* RAII for utils, application and c_api(partical)

* raii for class in include folder

* raii for application and boosting

* raii for dataset and dataset loader

* raii for dense bin and parser

* RAII refactor for almost all classes

* RAII for c_api

* clean code

* refine repeated code

* Decouple the "sigmoid" between objective and boosting.

* change std::vector<bool> back to std::vector<char> due to concurrence problem

* slight reduce some memory cost

Support multiclass classification (#53)