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Shogun machine learning toolbox | |
|---|---|
| Original authors | Gunnar Rätsch Soeren Sonnenburg |
| Developers | Soeren Sonnenburg Sergey Lisitsyn Heiko Strathmann Fernando Iglesias Viktor Gal |
| Stable release | 6.1.4 / July 5, 2019 (2019-07-05) |
| Written in | C++ |
| Operating system | Cross-platform |
| Type | Software library |
| License | BSD3 with optional GNU GPLv3 |
| Website | www |
| Repository | github |
Shogun is afree,open-sourcemachine learning software library written inC++. It offers numerous algorithms and data structures formachine learning problems. It offers interfaces forOctave,Python,R,Java,Lua,Ruby andC# usingSWIG.
It is licensed under the terms of theGNU General Public License version 3 or later.
The focus ofShogun is on kernel machines such assupport vector machines forregression andclassification problems.Shogun also offers a full implementation ofHidden Markov models.The core ofShogun is written in C++ and offers interfaces forMATLAB,Octave,Python,R,Java,Lua,Ruby andC#.Shogun has been under active development since 1999. Today there is a vibrant user community all over the world usingShogun as a base for research and education, and contributing to the core package.[citation needed]
CurrentlyShogun supports the following algorithms:
Many different kernels are implemented, ranging from kernels for numerical data (such as gaussian or linear kernels) to kernels on special data (such as strings over certain alphabets). The currently implemented kernels for numeric data include:
The supported kernels for special data include:
The latter group of kernels allows processing of arbitrary sequences over fixed alphabets such asDNA sequences as well as whole e-mail texts.
AsShogun was developed withbioinformatics applications in mind it is capable of processing huge datasets consisting of up to 10 million samples.Shogun supports the use of pre-calculated kernels. It is also possible to use a combined kernel i.e. a kernel consisting of a linear combination of arbitrary kernels over different domains. The coefficients or weights of the linear combination can be learned as well. For this purposeShogun offers amultiple kernel learning functionality.[citation needed]
