Please make a small and self-contained program that exposes the bug, and then open an issue at the Github repo. Issue templates are available to help with the bug report process.
Please cite the following paper if you use mlpack in your work. Citations are useful for the continued development of the library.
R.R. Curtin, M. Edel, M. Lozhnikov, Y. Mentekidis, S. Ghaisas, S. Zhang. “mlpack 3: a fast, flexible C++ machine learning library.” Journal of Open Source Software 3(26): pp. 726, 2018.
mlpack is a community-led effort. mlpack uses an open governance model and is fiscally sponsored by NumFOCUS. Consider making a tax-deductible donation to help the project pay for developer time, professional services, travel, workshops, and a variety of other needs. See the community page for more details and a list of contributors.
See the vision document, which lays out a number of development goals and directions for mlpack in the short- to medium-term future.
mlpack development is done on GitHub and the source code is
available there. There are a few related repositories that might be worth checking out also:
• ensmallen: numerical optimization library
• examples: simple examples of mlpack usage
• models: additional implementations of machine learning models
Contributions are absolutely welcome for mlpack, and anyone is welcome to participate and contribute. See the community page for more details.