shogun
A unified and efficient machine learning toolbox with C++ core and multi-language interfaces, developed since 1999.
What is shogun?
SHOGUN is a comprehensive, open-source machine learning toolbox that provides a unified framework for efficient algorithm implementation and experimentation. It offers a stable and mature platform, actively developed since 1999, for tackling diverse machine learning tasks with high-performance C++ implementations accessible through multiple programming languages.
Machine learning researchers and practitioners who need a cross-platform, high-performance toolbox with multi-language support for algorithm development and experimentation. It is particularly suited for those working in environments requiring integration across programming ecosystems like Python, R, Java, or C#.
Developers choose SHOGUN for its long-term stability, extensive algorithm library optimized in C++, and automatically generated bindings for numerous languages, enabling seamless use across different programming environments without sacrificing performance. Its emphasis on unification and efficiency bridges cutting-edge research with practical implementation.
Overview
Shōgun
Use Cases
Best For
- Implementing machine learning algorithms in high-performance C++ with bindings for multiple languages like Python, R, or Java.
- Cross-platform machine learning development on GNU/Linux, macOS, FreeBSD, or Windows.
- Researchers needing a stable, mature toolbox for reproducible experiments with comprehensive documentation and examples.
- Integrating machine learning into applications across diverse programming ecosystems without rewriting code for each language.
- Educational use through Jupyter notebooks, cookbooks, and API examples for learning machine learning concepts.
- Projects requiring reliable, tested algorithms with continuous integration and unit testing for stability.
Not Ideal For
- Projects exclusively focused on deep learning with frameworks like TensorFlow or PyTorch
- Teams needing lightweight, single-language libraries for rapid prototyping or deployment
- Environments where R or JavaScript is the primary language, as bindings are in beta or pre-alpha
- Applications requiring drag-and-drop GUI tools or simplified APIs for non-technical users
Pros & Cons
Pros
Automatically generated bindings for Python, Octave, Java, and more, enabling seamless integration across programming ecosystems as highlighted in the README.
Runs on GNU/Linux, macOS, FreeBSD, and Windows, ensuring broad accessibility and deployment flexibility for diverse environments.
Implements a wide range of machine learning methods in C++ for high performance and computational efficiency, supporting diverse tasks.
Actively developed since 1999 with comprehensive testing and continuous integration, providing a reliable platform for research and practice.
Cons
Interfaces for R, Perl, and JavaScript are marked as beta or pre-alpha, which can lead to instability or missing features for production use in those languages.
Requires building from C++ source with dependencies, making setup more involved compared to pip-installable Python libraries, as noted in the installation instructions.
Emphasizes traditional ML algorithms; may lack extensive support for contemporary deep learning architectures compared to specialized frameworks.
Frequently Asked Questions
Related Projects
PyTorch - Tensors and Dynamic neural networks in Python with strong GPU accelerationTensors and Dynamic neural networks in Python with strong GPU acceleration
OpenCVOpen Source Computer Vision Library
kerasDeep Learning for humans
streamlitStreamlit — A faster way to build and share data apps.
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.