SHOGUN alternatives and similar libraries
Based on the "Machine Learning" category.
Alternatively, view SHOGUN alternatives based on common mentions on social networks and blogs.
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xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow -
mxnet
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more -
Dlib
A toolkit for making real world machine learning and data analysis applications in C++ -
Caffe2
A lightweight, modular, and scalable deep learning framework. [Apache2] website -
vowpal_wabbit
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning. -
CCV
C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library -
catboost
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU. -
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RNNLIB
RNNLIB is a recurrent neural network library for sequence learning problems. Forked from Alex Graves work http://sourceforge.net/projects/rnnl/ -
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Fido
A lightweight C++ machine learning library for embedded electronics and robotics. -
Recommender
A C library for product recommendations/suggestions using collaborative filtering (CF) -
NN++
A small and easy to use neural net implementation for C++. Just download and #include! -
OpenHotspot
OpenHotspot is a machine learning, crime analysis framework written in C++11. -
sofia-ml
The suite of fast incremental algorithms for machine learning. [Apache2]
Updating dependencies is time-consuming.
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
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README
The SHOGUN machine learning toolbox
Unified and efficient Machine Learning since 1999.
Latest release:
Cite Shogun:
Develop branch build status:
Donate to Shogun via NumFocus:
Buildbot: https://buildbot.shogun.ml.
- See [doc/readme/ABOUT.md](doc/readme/ABOUT.md) for a project description.
- See [doc/readme/INSTALL.md](doc/readme/INSTALL.md) for installation instructions.
- See [doc/readme/INTERFACES.md](doc/readme/INTERFACES.md) for calling Shogun from its interfaces.
- See [doc/readme/EXAMPLES.md](doc/readme/EXAMPLES.md) for details on creating API examples.
See [doc/readme/DEVELOPING.md](doc/readme/DEVELOPING.md) for how to hack Shogun.
See API examples for all interfaces.
See the wiki for extended developer information.
Interfaces
Shogun is implemented in C++ and offers automatically generated, unified interfaces to Python, Octave, Java / Scala, Ruby, C#, R, Lua. We are currently working on adding more languages including JavaScript, D, and Matlab.
| Interface | Status |
|---|---|
| Python | mature (no known problems) |
| Octave | mature (no known problems) |
| Java/Scala | stable (no known problems) |
| Ruby | stable (no known problems) |
| C# | stable (no known problems) |
| R | beta (most examples work, static calls 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.
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, separated into C++ source and interfaces
- doc - readmes (doc/readme, submodule), Jupyter notebooks, cookbook (API examples), licenses
- examples - example files for all interfaces
- data - data sets (submodule, required for examples)
- tests - unit tests and continuous integration of interface examples
- applications - applications of SHOGUN (outdated)
- benchmarks - speed benchmarks
- cmake - cmake build scripts
License
Shogun is distributed under [BSD 3-clause license](doc/license/LICENSE.md), with optional GPL3 components. See [doc/licenses](doc/license) for details.
*Note that all licence references and agreements mentioned in the SHOGUN README section above
are relevant to that project's source code only.