• 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

• Documentation for mlpack [top]
  • A fast, flexible machine learning library
  • mlpack basics
  • mlpack algorithm documentation
  • Bindings to other languages
  • mlpack on embedded systems
  • Examples and further documentation
  • Developer documentation
  • Changelog

Documentation for mlpack

🔗 A fast, flexible machine learning library

mlpack is an intuitive, fast, and flexible header-only C++ machine learning library with bindings to other languages. It aims to provide fast, lightweight implementations of both common and cutting-edge machine learning algorithms.

mlpack’s lightweight C++ implementation makes it ideal for deployment, and it can also be used for interactive prototyping via C++ notebooks (these can be seen in action on mlpack’s homepage).

In addition to its powerful C++ interface, mlpack also provides command-line programs, and bindings to the Python, R, Julia, and Go languages.

If you use mlpack, please cite the software.

🔗 mlpack basics

Installing mlpack can be done using the instructions in the README; or the Windows build guide. The following basic guides are highly recommended before using mlpack.

• First steps:
  • mlpack C++ quickstart: create a couple simple C++ programs that use mlpack
  • Sample Windows mlpack C++ application: create a working mlpack Windows program using Visual Studio
• Basics of matrices and data in mlpack:
  • Matrices and data in mlpack
  • Loading and saving mlpack objects
• Reference for mlpack core classes:
  • Core mlpack documentation
    • Core math utilities
    • Distances
    • Distributions
    • Kernels
• Using mlpack natively with our extensions in Python, R, CLI, Julia, and Go:
  • Links to quickstarts and references

🔗 mlpack algorithm documentation

Documentation for each machine learning algorithm that mlpack implements is detailed in the sections below.

• Classification algorithms: classify points as discrete labels (0, 1, 2, …).
• Regression algorithms: predict continuous values.
• Clustering algorithms: group points into clusters.
• Geometric algorithms: computations based on distance metrics (nearest neighbors, kernel density estimation, etc.).
• Preprocessing utilities: prepare data for machine learning algorithms.
• Transformations: transform data from one space to another (principal components analysis, etc.).
• Modeling utilities: cross-validation, hyperparameter tuning, etc.

🔗 Classification algorithms

Classify points as discrete labels (0, 1, 2, …).

• AdaBoost: Adaptive Boosting
• DecisionTree: ID3-style decision tree classifier
• HoeffdingTree: streaming/incremental decision tree classifier
• LinearSVM: simple linear support vector machine classifier
• LogisticRegression: L2-regularized logistic regression (two-class only)
• NaiveBayesClassifier: simple multi-class naive Bayes classifier
• Perceptron: simple Perceptron classifier
• RandomForest: parallelized random forest classifier
• SoftmaxRegression: L2-regularized softmax regression (i.e. multi-class logistic regression)

🔗 Regression algorithms

Predict continuous values.

• BayesianLinearRegression: Bayesian L2-penalized linear regression
• DecisionTreeRegressor: ID3-style decision tree regressor
• LARS: Least Angle Regression (LARS), L1-regularized and L2-regularized
• LinearRegression: L2-regularized linear regression (ridge regression)

🔗 Clustering algorithms

NOTE: this documentation is still under construction and so some algorithms that mlpack implements are not yet listed here. For now, see the mlpack/methods directory for a full list of algorithms.

Group points into clusters.

• MeanShift: clustering with the density-based mean shift algorithm

🔗 Geometric algorithms

NOTE: this documentation is still under construction and so no geometric algorithms in mlpack are documented yet. For now, see the mlpack/methods directory for a full list of algorithms.

Computations based on distance metrics.

🔗 Preprocessing utilities

Prepare data for machine learning algorithms.

• Normalizing labels: map labels to and from the range [0, numClasses - 1].
• Dataset splitting: split a dataset into a training set and a test set.

NOTE: this documentation is still under construction and so not all preprocessing utilities in mlpack are documented yet. See also the mlpack/methods/preprocess directory for a full list of algorithms.

🔗 Transformations

NOTE: this documentation is still under construction and so some algorithms that mlpack implements are not yet listed here. For now, see the mlpack/methods directory for a full list of algorithms.

Transform data from one space to another.

• AMF: alternating matrix factorization
• LocalCoordinateCoding: local coordinate coding with dictionary learning
• LMNN: large margin nearest neighbor (distance metric learning)
• NCA: neighborhood components analysis (distance metric learning)
• NMF: non-negative matrix factorization
• PCA: principal components analysis
• RADICAL: robust, accurate, direct independent components analysis (ICA) algorithm
• SparseCoding: sparse coding with dictionary learning

🔗 Modeling utilities

Tools for assembling a full data science pipeline.

• Cross-validation: k-fold cross-validation tools for any mlpack algorithm
• Hyperparameter tuning: generic hyperparameter tuner to find good hyperparameters for any mlpack algorithm

🔗 Bindings to other languages

mlpack’s bindings to other languages have less complete functionality than mlpack in C++, but almost all the same algorithms are available.

Python	–	quickstart	–	reference
Julia	–	quickstart	–	reference
R	–	quickstart	–	reference
Command-line programs	–	quickstart	–	reference
Go	–	quickstart	–	reference

🔗 mlpack on embedded systems

mlpack is well suited for embedded systems due to the fact that it is written in C++ and it is header-only with minimal dependencies. In the following, we are adding a set of tutorials to allow you to experiment mlpack on various types of these systems.

• cross-compile and run k-NN on a Raspberry Pi 2 (armv7)

🔗 Examples and further documentation

• mlpack examples repository: numerous fully-working example applications of mlpack, in C++ and other languages.
• mlpack models repository: complex models in C++ built with mlpack

For additional documentation beyond what is covered in all the resources above, the source code should be consulted. Each method is fully documented.

🔗 Developer documentation

The following general documentation can be useful if you are interested in contributing to mlpack:

• The mlpack community
• mlpack and Google Summer of Code

Throughout the codebase, mlpack uses some common template parameter policies. These are documented below.

• The ElemType policy: element types for data
• The DistanceType policy: distance metrics
• The KernelType policy: kernel functions
• The TreeType policy: space trees (ball trees, KD-trees, etc.)

In addition, the following documentation may be useful when developing bindings for other languages:

• Timers: timing parts of bindings
• Writing an mlpack binding: simple examples of mlpack bindings
• Automatic bindings: details on mlpack’s automatic binding generator system.

🔗 Changelog

For a list of changes in each version of mlpack, see the changelog.