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The Shogun Machine Learning Toolbox

Project Description

# The SHOGUN machine learning toolbox

Unified and efficient Machine Learning since 1999.

Develop branch build status:


Quick links for this file: * Introduction * Interfaces * Platforms * License

## Interfaces

Shogun is implemented in C++ and interfaces to Python, octave, java, ruby, C#, R, Lua, Perl. JavaScript and Matlab are planned to be (re-)introduced soon.

| Interface | Status | |:—————-:|———————————————————–| |python | mature (no known problems) | |octave | mature (no known problems) | |java | stable (no known problems) | |ruby | stable (no known problems) | |csharp | stable (no known problems) | |r | beta (most examples work, static calls unavailable | |lua | alpha (many examples work, string typemaps are unstable, overloaded methods unavailable) | |perl | pre-alpha (work in progress quality) | |js | pre-alpha (work in progress quality) |

See our website for examples in all languages.

## Platforms

Shogun is supported under GNU/Linux, MacOSX, FreeBSD, and Windows. See our buildfarm

## Directory Contents

The following directories are found in the source distribution. Note that some folders are submodules that can be checked out with git submodule update --init.

  • src - source code.
  • doc - readmes (doc/reamde, submodule), ipython notebooks, cookbook (api examples), licenses
  • examples - example files for all interfaces.
  • data - data sets (submodule required for some examples / applications)
  • tests - unit and integration tests.
  • applications - applications of SHOGUN.
  • benchmarks - speed benchmarks.
  • cmake - cmake build scripts

## License

Shogun is generally licensed under the GPL3, with code borrowed from various external libraries, and optional parts that are neither compatible with GPL nor BSD. It is possible to compile a BSD3 compatible build of Shogun.

See doc/licenses for details.

Release History

Release History

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