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Tree-based machine learning for embedded system

Project description


Tree-based machine learning classifiers for embedded systems. Train in Python, deploy on microcontroller.

Want Naive Bayes instead? Go to embayes

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


Minimally useful

  • Random Forests and ExtraTrees classifiers implemented
  • Tested running on AVR, ESP8266 and Linux.
  • On ESP8266, 8x8 digits classify in under 0.3ms with 95%+ accuracy
  • On Linux, is approx 2x faster than sklearn


Install from PyPI

pip install emtrees --user


  1. Train your model in Python
import emtrees
estimator = emtrees.RandomForest(n_estimators=10, max_depth=10), Y_train)
  1. Generate C code
code = estimator.output_c('sonar')
with open('sonar.h', 'w') as f:
  1. Use the C code
#include <emtrees.h>
#include "sonar.h"

const int32_t length = 60;
int32_t values[length] = { ... };
const int32_t predicted_class = sonar_predict(values, length):

For full example code, see examples/ and emtrees.ino



  • Standalone example application on microcontroller


  • Support returning probabilities
  • Support serializing/deserializing trees


  • Support weighted voting
  • Support GradientBoostingClassifier
  • Implement a Very Fast Decision Tree (VFDT) learning algorithm
  • Support XGBoost learning of trees
  • Support LightGBM learning of trees
  • Implement multithreading when used in Python bindings, using OpenMP
  • Support regression trees

Project details

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Filename, size & hash SHA256 hash help File type Python version Upload date
emtrees-0.2.3.tar.gz (5.6 kB) Copy SHA256 hash SHA256 Source None May 26, 2018

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