Mlpack

mlpack

Initial release	February 1, 2008; 16 years ago (2008-02-01)^[1]

Stable release	4.4.0^[2] / 28 May 2024; 26 days ago (28 May 2024)

Repository	github.com/mlpack/mlpack
Written in	C++, Python, Julia, Go
Operating system	Cross-platform
Available in	English
Type	Software library Machine learning
License	Open source (BSD)
Website	mlpack.org

mlpack is a machine learning software library for C++, built on top of the Armadillo library and the ensmallen numerical optimization library.^[3] mlpack has an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users.^[4] Its intended target users are scientists and engineers.

It is open-source software distributed under the BSD license, making it useful for developing both open source and proprietary software. Releases 1.0.11 and before were released under the LGPL license. The project is supported by the Georgia Institute of Technology and contributions from around the world.

Miscellaneous features

Class templates for GRU, LSTM structures are available, thus the library also supports Recurrent Neural Networks.^{[citation needed]}

There are bindings to R, Go, Julia,^[5] and Python. Its binding system is extensible to other languages.

Supported algorithms

Currently mlpack supports the following algorithms and models:

Collaborative Filtering
Decision stumps (one-level decision trees)
Density Estimation Trees
Euclidean minimum spanning trees
Gaussian Mixture Models (GMMs)
Hidden Markov Models (HMMs)
Kernel density estimation (KDE)
Kernel Principal Component Analysis (KPCA)
K-Means Clustering
Least-Angle Regression (LARS/LASSO)
Linear Regression
Bayesian Linear Regression
Local Coordinate Coding
Locality-Sensitive Hashing (LSH)
Logistic regression
Max-Kernel Search
Naive Bayes Classifier
Nearest neighbor search with dual-tree algorithms
Neighbourhood Components Analysis (NCA)
Non-negative Matrix Factorization (NMF)
Principal Components Analysis (PCA)
Independent component analysis (ICA)
Rank-Approximate Nearest Neighbor (RANN)
Simple Least-Squares Linear Regression (and Ridge Regression)
Sparse Coding, Sparse dictionary learning
Tree-based Neighbor Search (all-k-nearest-neighbors, all-k-furthest-neighbors), using either kd-trees or cover trees
Tree-based Range Search

References

^ "Initial checkin of the regression package to be released · mlpack/mlpack". February 8, 2008. Retrieved May 24, 2020.
^ "Release 4.4.0". 28 May 2024. Retrieved 22 June 2024.
^ Ryan Curtin; et al. (2021). "The ensmallen library for flexible numerical optimization". Journal of Machine Learning Research. 22 (166): 1–6. arXiv:2108.12981. Bibcode:2021arXiv210812981C.
^ Ryan Curtin; et al. (2023). "mlpack 4: a fast, header-only C++ machine learning library". Journal of Open Source Software. 8 (82): 5026. arXiv:2302.00820.
^ "Mlpack/Mlpack.jl". 10 June 2021.