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Naive Bayes classifier for embedded systems

Project description

# embayes Bayesian machine learning classifiers for embedded systems. Train in Python, deploy on microcontroller.

## Key features

Embedded-friendly Classifier

  • Portable C99 code
  • No stdlib required
  • No dynamic allocations
  • Integer/fixed-point math only
  • Single header file include
  • Fast, sub-millisecond classification

Convenient Training

  • API-compatible with [scikit-learn](
  • Implemented in Python 3
  • C classifier accessible in Python using pybind11

[MIT licensed](./

## Status Minimally useful

  • Gaussian Naive Bayes classifier implemented
  • Tested running on ESP8266 and Linux.
  • On ESP8266, 2 classes and 30 features classify in under 0.5ms

## Installing

Install from git

git clone python3 install –user

## Usage

See [examples/](./examples/ and [embayes.ino](./embayes.ino)



  • Make estimator a wrapper around sklearn.naivebayes.GaussianNB
  • Make estimator work in sklearn pipeline
  • Make pdf approximation configurable as parameter


  • Support generating inline C code, not needing model coefficients in RAM
  • Support de/serializing coefficients at runtime
  • Support training on microcontroller

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