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* avoid threshold inf

* use __save_model_to_string for feature importance

* Revert "use __save_model_to_string for feature importance"

This reverts commit dca6a85fb3d89866eb56eb0c9ca103ada4d92b53.

* translate model to if-else

* support multiclass and predictleaf

* remove java option for now

* support multi-thread

* add task:convert_model

* refine prediction logic.

* fix test.

* fix out_len in training score of Dart.

* improve predict speed for high dimension data.

* add feature importances in python; add pandas support

* solve best_iteration issue

Categorical feature support (#108)

1. merge python-package
2. add dump model to json
3. fix bugs
4. clean code with pylint
5. update python examples

* 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

add internal_value_ in tree and output to txt model, so we can use for calculating feature importance when predicting (#80)

Packages that parse LightGBM’s logs will require minor changes to
parsing logic to work correctly.