Incremental learning written in C++ exposed in Python
Project description
ml-rapids: Incremental learning written in C++ exposed in Python and NodeJS
ml-rapids
implements incremental learning methods in C++ and exposes them via SWIG in Python and NodeJS.
Incremental learning methods:
- Classification
- Majority Class
- Naive Bayes
- Logistic Regression
- Perceptron
- VFDT (Very Fast Decision Trees) aka Hoeffding Trees
- HAT (Hoeffding Adaptive Trees)
- Bagging
- Regression
- /
All the methods implement sklearn
incremantal learner interface (includes fit
, partial_fit
and predict
methods).
Future plans
Streaming random forest on top of Hoeffding trees will be implemented.
The library will be exposed via pypi
and npm
packages.
Python:
pip install ml-rapids
NodeJS:
npm install ml-rapids
Development
Development notes can be read here.
Acknowledgements
ml-rapids
is developed by AILab at Jozef Stefan Institute.
This repository is based strongly on streamDM-cpp.
Project has received funding from European Union's Horizon 2020 Research and Innovation Programme under the Grant Agreement 776115 (PerceptiveSentinel).
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