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        "description": "# embayes\nBayesian machine learning classifiers for embedded systems.\nTrain in Python, deploy on microcontroller.\n\n## Key features\n\nEmbedded-friendly Classifier\n\n* Portable C99 code\n* No stdlib required\n* No dynamic allocations\n* Integer/fixed-point math only\n* Single header file include\n* Fast, sub-millisecond classification\n\nConvenient Training\n\n* API-compatible with [scikit-learn](http://scikit-learn.org)\n* Implemented in Python 3\n* C classifier accessible in Python using pybind11\n\n[MIT licensed](./LICENSE.md)\n\n## Status\n**Minimally useful**\n\n* Gaussian Naive Bayes classifier implemented\n* Tested running on ESP8266 and Linux.\n* On ESP8266, 2 classes and 30 features classify in under 0.5ms\n\n## Installing\n\nInstall from git\n\n    git clone https://github.com/jonnor/embayes\n    python3 setup.py install --user\n\n## Usage\n\nSee [examples/cancer.py](./examples/cancer.py) and [embayes.ino](./embayes.ino)\n\n## TODO\n\n0.2\n\n* Make estimator a wrapper around `sklearn.naivebayes.GaussianNB`\n* Make estimator work in sklearn pipeline\n* Make `pdf` approximation configurable as parameter\n\n1.0\n\n* Support generating inline C code, not needing model coefficients in RAM\n* Support de/serializing coefficients at runtime\n* Support training on microcontroller",
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