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.
All the notebooks in this repository can be easily run on https://lab.mlpack.org/.
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, see the main mlpack repository and mlpack website for more information on how to install mlpack.
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.
Therefore, this repository contains examples that are using common datasets. This repository is organized per language and per method that is used.
cpp/: various mlpack C++ examples showing different machine learning algorithms.jupyter_notebook/: mlpack examples C++ or Python written in jupyter notebook format.embedded/: directory contains mlpack C++ examples with more focus on embedded system in the case of compilation and optimized binary and sensor input.cli/directory contains mlpack methods executed directly from the terminal command line, suitable if you have a ready to use dataset and you do not want to jump to the code.
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. Rarely, 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, 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.
All the required dataset needed by the examples can be downloaded using the
provided script in the scripts directory. You will have to execute
download_data_set.py from the scripts/ directory and it will download and
extract all the necessary datasets inside the data/ directory in order for
examples to work perfectly:
cd scripts/
./download_data_set.py
