The mlpack examples repository contains simple example usages of mlpack. You can take the code here and adapt it into your application, or compile it and see what it does and play with it.
Each of the examples are meant to be as simple as possible, and they are extensively documented.
(This README is still a work in progress as this repository is transitioned. Any comments like this one indicate that something still needs to be done. So, as we work on it, we can remove these comments, and once all the comments are removed, we are done. :))
0. Contents
1. Overview
This repository contains examples of mlpack usage that can be easily adapted to various applications. If you're looking to figure out how to get mlpack working for your machine learning task, this repository would definitely be a good place to look, along with the mlpack documentation.
mlpack is a C++ library that provides machine learning support, but it also provides bindings to other languages, including Python and Julia, and it also provides command-line programs.
Therefore, this repository contains examples not just in C++ but also in other
languages. C++ mlpack usage examples are contained in the c++/ directory;
Python examples in the python/ directory, command-line examples in the
command-line/ directory, and so forth.
2. Building the examples and usage
(This section is still in the process of being implemented!)
In order to keep this repository as simple as possible, there is no build
system, and all examples are minimal. For the C++ examples, there is a Makefile
in each example's directory; if you have mlpack installed on your system,
running make should work fine. Some other examples may also use other
libraries, and the Makefile expects those dependencies to also be available.
See the README in each directory for more information, and see the main mlpack
repository and mlpack
website for more information on how to install mlpack.
For Python examples and other-language examples, it's expected that you have mlpack and its dependencies installed.
Each example should be easily runnable and should perform a simple machine learning task on a dataset. You might need to download the dataset first---so be sure to check any README for the example.