• C++ API Documentation

  • Quickstart
  • Matrices and data
  • Loading and saving
  • Citation
  • Core functionality

    • Core math utilities
    • Distances
    • Distributions
    • Kernels
  • Trees

    • KDTree
    • MeanSplitKDTree
    • BallTree
    • MeanSplitBallTree
    • VPTree
    • RPTree
    • MaxRPTree
    • UBTree
    • BinarySpaceTree
  • Classification

    • AdaBoost
    • DecisionTree
    • HoeffdingTree
    • LinearSVM
    • LogisticRegression
    • NaiveBayesClassifier
    • Perceptron
    • RandomForest
    • SoftmaxRegression
  • Regression

    • BayesianLinearRegression
    • DecisionTreeRegressor
    • LARS
    • LinearRegression
  • Clustering

    • MeanShift
  • Geometry
  • Preprocessing

    • Normalizing labels
    • Dataset splitting
  • Transformations

    • AMF
    • LocalCoordinateCoding
    • LMNN
    • NCA
    • NMF
    • PCA
    • RADICAL
    • SparseCoding
  • Modeling

    • Cross-validation
    • Hyperparameter tuning
  • Embedded Applications

    • cross-compile and run k-NN on a Raspberry Pi 2 (armv7)
    • Setting up an mlpack cross-compilation environment
  • Examples
  • Developers

• Binding API

  • Python quickstart
  • Python binding documentation
  • Julia quickstart
  • Julia binding documentation
  • R quickstart
  • R binding documentation
  • CLI quickstart
  • CLI binding documentation
  • Go quickstart
  • Go binding documentation

• Community [top]
  • Real-time chat
  • Video meetup
  • Getting involved

Community

mlpack is a community-led effort, and so the code is not possible without the community. Since mlpack is an open-source project, anyone is welcome to become a part of the community and contribute. There is no need to be a a machine learning expert to participate; often, there are many tasks to be done that don’t require in-depth knowledge.

Over the past several years, mlpack has participated in Google Summer of Code. For more information, see this page.

All mlpack development is done on GitHub. Commits and issue comments can be tracked via the mlpack-git list (graciously hosted by FreeLists. Communication is generally either via issues on GitHub, or via chat:

🔗 Real-time chat

• mlpack:matrix.org on Matrix

• mlpack Slack workspace
  • You will need to request an invite from the auto-inviter.
  • The Slack workspace is a bridged version of the Matrix room and it is generally a better experience to use Matrix directly.

🔗 Video meetup

On the first and third Friday of every month, at 1700 UTC on Fridays, we have casual video meetups with no particular agenda. Feel free to join up! We often talk about code changes that we are working on, issues that people are having with mlpack, general design direction, and whatever else might be on our mind.

We use this Zoom room. For security, we use a password for the meeting to keep malicious bots out. The password is simple: it’s just the name of the library (in all lowercase).

🔗 Getting involved

Everyone is welcome to contribute to mlpack. But before becoming a contributor, it’s often useful to understand mlpack as a user. So, a good place to start is to:

• download mlpack
• use it in C++ (see the documentation)
• use the bindings to other languages to perform machine learning tasks: Python, Julia, R, Go, and the command-line programs

There is also the examples repository that contains many examples you can build and play around with.

Once you have an idea of what’s included in mlpack and how a user might use it, then a good next step would be to set up a development environment. Once you have that set up, you can build mlpack from source, and explore the codebase to see how it’s organized.

Try making small changes to the code, or adding new tests to the test suite, and then rebuild to see how your changes work.

Now you’re set up to contribute! There are lots of ways you can contribute. Here are a couple ideas:

• Help others figure out their mlpack issues and questions. Here is a list of GitHub issues tagged question. Helping others figure out their problems is really one of the best ways to learn about the library.

• Read through the vision document to learn about the development goals of the mlpack community and see the high-level tasks that need to be done to accomplish that vision.

• Find an issue that needs implementation help; here is the list of issues.

• Find an abandoned pull request; here is a list of pull requests that were closed for inactivity. Often these have comments that need to be addressed, but the original author didn’t have time to finish the work. So, you can pick up where they left off!

• Implement a new machine learning algorithm that mlpack doesn’t currently have.

• Take a look at the ideas on the Google Summer of Code Ideas List and see if you find any of them interesting or exciting. Even if you’re not planning to apply for Summer of Code, it’s okay to take these ideas and implement them separately.