<commit_before>mlpack: a scalable C++ machine learning library
===============================================

mlpack is an intuitive, fast, scalable C++ machine learning library, meant to be
a machine learning analog to LAPACK. It aims to implement a wide array of
machine learning methods and functions as a "swiss army knife" for machine
learning researchers.

[![Build Status](http://big.cc.gt.atl.ga.us/job/mlpack%20-%20svn%20checkin%20test/badge/icon)](http://big.cc.gt.atl.ga.us/job/mlpack%20-%20svn%20checkin%20test/)

0. Contents
-----------

  1. [Introduction](#1-introduction)
  2. [Citation details](#2-citation-details)
  3. [Dependencies](#3-dependencies)
  4. [Building mlpack from source](#4-building-mlpack-from-source)
  5. [Running mlpack programs](#5-running-mlpack-programs)
  6. [Further documentation](#6-further-documentation)
  7. [Bug reporting](#7-bug-reporting)

1. Introduction
---------------

The mlpack website can be found at http://mlpack.org and contains numerous
tutorials and extensive documentation.  This README serves as a guide for what
mlpack is, how to install it, how to run it, and where to find more
documentation. The website should be consulted for further information:

  - [mlpack homepage](http://www.mlpack.org/)
  - [Tutorials](http://www.mlpack.org/tutorial.html)
  - [Development Site (Trac)](http://www.mlpack.org/trac/)
  - [API documentation](http://www.mlpack.org/doxygen.php)

2. Citation details
-------------------

If you use mlpack in your research or software, please cite mlpack using the
citation below (given in BiBTeX format):

    @article{mlpack2013,
      title     = {{MLPACK}: A Scalable {C++} Machine Learning Library},
      author    = {Curtin, <NAME>. and Cline, <NAME>. and Slagle, <NAME>. and
                   <NAME>. and <NAME>, <NAME>. and Gray,
                   <NAME>.},
      journal   = {Journal of Machine Learning Research},
      volume    = {14},
      pages     = {801--805},
      year      = {2013}
    }

Citations are beneficial for the growth and improvement of mlpack.

3. Dependencies
---------------

mlpack has the following dependencies:

      Armadillo     >= 3.6.0
      LibXml2       >= 2.6.0
      Boost (program_options, math_c99, unit_test_framework, random)
      CMake         >= 2.8.5

All of those should be available in your distribution's package manager.  If
not, you will have to compile each of them by hand.  See the documentation for
each of those packages for more information.

If you are compiling Armadillo by hand, ensure that LAPACK and BLAS are enabled.

4. Building mlpack from source
------------------------------

(see also [Building mlpack From Source](http://www.mlpack.org/doxygen.php?doc=build.html) )

mlpack uses CMake as a build system and allows several flexible build
configuration options. One can consult any of numerous CMake tutorials for
further documentation, but this tutorial should be enough to get mlpack built
and installed.

First, unpack the mlpack source and change into the unpacked directory.  Here we
use mlpack-x.y.z where x.y.z is the version.

    $ tar -xzf mlpack-x.y.z.tar.gz
    $ cd mlpack-x.y.z

Then, make a build directory.  The directory can have any name, not just
'build', but 'build' is sufficient.

    $ mkdir build
    $ cd build

The next step is to run CMake to configure the project.  Running CMake is the
equivalent to running `./configure` with autotools. If you run CMake with no
options, it will configure the project to build with no debugging symbols and no
profiling information:

    $ cmake ../

You can specify options to compile with debugging information and profiling
information:

    $ cmake -D DEBUG=ON -D PROFILE=ON ../<commit_msg>Add download link, for people who come across mlpack via Github but might be better served by a stable version.<commit_after>mlpack: a scalable C++ machine learning library
===============================================

mlpack is an intuitive, fast, scalable C++ machine learning library, meant to be
a machine learning analog to LAPACK. It aims to implement a wide array of
machine learning methods and functions as a "swiss army knife" for machine
learning researchers.

*Download [current stable version (1.0.11)](http://www.mlpack.org/files/mlpack-1.0.11.tar.gz)*

[![Build Status](http://big.cc.gt.atl.ga.us/job/mlpack%20-%20svn%20checkin%20test/badge/icon)](http://big.cc.gt.atl.ga.us/job/mlpack%20-%20svn%20checkin%20test/)

0. Contents
-----------

  1. [Introduction](#1-introduction)
  2. [Citation details](#2-citation-details)
  3. [Dependencies](#3-dependencies)
  4. [Building mlpack from source](#4-building-mlpack-from-source)
  5. [Running mlpack programs](#5-running-mlpack-programs)
  6. [Further documentation](#6-further-documentation)
  7. [Bug reporting](#7-bug-reporting)

1. Introduction
---------------

The mlpack website can be found at http://www.mlpack.org and contains numerous
tutorials and extensive documentation.  This README serves as a guide for what
mlpack is, how to install it, how to run it, and where to find more
documentation. The website should be consulted for further information:

  - [mlpack homepage](http://www.mlpack.org/)
  - [Tutorials](http://www.mlpack.org/tutorial.html)
  - [Development Site (Trac)](http://www.mlpack.org/trac/)
  - [API documentation](http://www.mlpack.org/doxygen.php)

2. Citation details
-------------------

If you use mlpack in your research or software, please cite mlpack using the
citation below (given in BiBTeX format):

    @article{mlpack2013,
      title     = {{MLPACK}: A Scalable {C++} Machine Learning Library},
      author    = {Curtin, <NAME>. and Cline, <NAME>. and Slagle, <NAME>. and
                   March, <NAME>. and <NAME>. and Mehta, <NAME>. and Gray,
                   <NAME>.},
      journal   = {Journal of Machine Learning Research},
      volume    = {14},
      pages     = {801--805},
      year      = {2013}
    }

Citations are beneficial for the growth and improvement of mlpack.

3. Dependencies
---------------

mlpack has the following dependencies:

      Armadillo     >= 3.6.0
      LibXml2       >= 2.6.0
      Boost (program_options, math_c99, unit_test_framework, random)
      CMake         >= 2.8.5

All of those should be available in your distribution's package manager.  If
not, you will have to compile each of them by hand.  See the documentation for
each of those packages for more information.

If you are compiling Armadillo by hand, ensure that LAPACK and BLAS are enabled.

4. Building mlpack from source
------------------------------

(see also [Building mlpack From Source](http://www.mlpack.org/doxygen.php?doc=build.html))

mlpack uses CMake as a build system and allows several flexible build
configuration options. One can consult any of numerous CMake tutorials for
further documentation, but this tutorial should be enough to get mlpack built
and installed.

First, unpack the mlpack source and change into the unpacked directory.  Here we
use mlpack-x.y.z where x.y.z is the version.

    $ tar -xzf mlpack-x.y.z.tar.gz
    $ cd mlpack-x.y.z

Then, make a build directory.  The directory can have any name, not just
'build', but 'build' is sufficient.

    $ mkdir build
    $ cd build

The next step is to run CMake to configure the project.  Running CMake is the
equivalent to running `./configure` with autotools. If you run CMake with no
options, it will configure the project to build with no debugging symbols and no
profiling information:

    $ cmake ../

You can specify options to compile with debugging information and profiling
information:

    $ cmake -D DEBUG=ON -D PROFILE=ON ../
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