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guolinke committed Aug 5, 2016
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269 changes: 269 additions & 0 deletions .gitignore
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## Ignore Visual Studio temporary files, build results, and
## files generated by popular Visual Studio add-ons.

# User-specific files
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26 changes: 26 additions & 0 deletions CMakeLists.txt
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cmake_minimum_required(VERSION 2.8)

PROJECT(LightGBM)

OPTION(USE_MPI "MPI based parallel learning" OFF)

if(USE_MPI)
find_package(MPI REQUIRED)
ADD_DEFINITIONS(-DUSE_MPI)
MESSAGE(${MPI_LIBRARIES})
MESSAGE(${MPI_CXX_LIBRARIES})
else()
ADD_DEFINITIONS(-DUSE_SOCKET)
endif()

if(UNIX)
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -fopenmp -pthread -O2 -std=c++11")
endif()

SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -fopenmp -O2 -std=c++11")

SET(LightGBM_HEADER_DIR ${PROJECT_SOURCE_DIR}/include)
SET(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR})

ADD_SUBDIRECTORY(src)

23 changes: 23 additions & 0 deletions README.md
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LightGBM, Light Gradient Boosting
==========




LightGBM is a gradient boosting framework that using histogram based tree learning algorithm. It can outperform existing boosting tools on both learning speed and accuracy. Our [experiments](https://github.com/Microsoft/LightGBM/wiki/Experiments#comparison-experiment) shows it is about 6x faster than xgboost with much better prediction accuracy.

LightGBM can be run on multiple machines, Our [experiments](https://github.com/Microsoft/LightGBM/wiki/Experiments#parallel-experiment) shows it can perform linear speed up in parallel learning.

* [**Wiki**](https://github.com/Microsoft/LightGBM/wiki)
* [**Installation**](https://github.com/Microsoft/LightGBM/wiki/Installation-Guide)
* [**Quick Start**](https://github.com/Microsoft/LightGBM/wiki/Quick-Start)
* [**Feature Highlight**](https://github.com/Microsoft/LightGBM/wiki/Feature-Highlight)
* [**Parallel Learning Guide**](https://github.com/Microsoft/LightGBM/wiki/Parallel-Learning-Guide)
* [**Parameters**](https://github.com/Microsoft/LightGBM/wiki/Parameters)
* [**Examples**](https://github.com/Microsoft/LightGBM/tree/master/examples)






5 changes: 5 additions & 0 deletions examples/README.md
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Examples
=====================

You can learn how to use LightGBM by these examples. If you have any questions, please refer to our [wiki](https://github.com/Microsoft/LightGBM/wiki).

34 changes: 34 additions & 0 deletions examples/binary_classification/README.md
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Binary Classification Example
=====================
Here is an example for LightGBM to run binary classification task.

***You should copy executable file to this folder first.***

#### Training

For windows, by running following command in this folder:
```
LightGBM.exe config=train.conf
```


For linux, by running following command in this folder:
```
./LightGBM config=train.conf
```

#### Prediction

You should finish training first.

For windows, by running following command in this folder:
```
LightGBM.exe config=predict.conf
```

For linux, by running following command in this folder:
```
./LightGBM config=predict.conf
```


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