# shogun-toolbox/shogun

Shōgun

Repository: https://github.com/shogun-toolbox/shogun
Canonical: https://ross.abutalabs.com/products/shogun
Homepage: http://shogun-toolbox.org
Language: C++
License: BSD-3-Clause
License Family: permissive
Topics: machine-learning, data-science, artificial-intelligence, swig, c-plus-plus, cmake
Last push: 2023-12-19T18:37:18+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 5633, "days_push": 988, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3079, forks 1034 (observed 2026-08-28T04:07:41.844688+00:00)

## What it is
Shogun is a C++ machine learning toolbox offering unified, efficient implementations of many ML algorithms with automatically generated bindings for Python, Java, Ruby, R, C#, Octave, Lua, and more. It has been developed since 1999 and is a NumFOCUS-affiliated project released under a BSD-3-Clause license.

## Use cases
- train machine learning models from python using a fast c++ backend
- use the same ml library across multiple programming languages
- kernel methods and svm classification in c++
- run machine learning on linux macos and windows
- benchmark ml algorithms against a unified api

## When to choose
- you need a mature multi-language ML library with a unified API
- you want efficient C++ implementations callable from Python, R, Java, or Ruby
- you work with kernel methods, SVMs, or classical ML algorithms

## When to avoid
- you need deep learning or GPU-accelerated training
- you want a rapidly evolving ecosystem with frequent releases
- you need modern Python-native tooling and integration with the PyData stack

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, data-science
- domain: machine-learning, data-science, artificial-intelligence
- platform: windows, bsd, python, cpp, cross-platform
- tags: swig-bindings, multi-language, kernel-methods, cmake, scientific-computing, linux, macos

## Member repositories
- shogun-toolbox/shogun (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:41.844688+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:27:51.088557+00:00, confidence not recorded.
  - readme: https://github.com/shogun-toolbox/shogun (fetched 2026-08-28T04:07:41.844688+00:00, sha 8a5483101f3a)
  - homepage: http://shogun-toolbox.org (fetched 2026-08-29T09:42:59.884953+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.