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        "description": "[![Travis CI Build Status](https://travis-ci.org/jonnor/emlearn.svg?branch=master)](https://travis-ci.org/jonnor/emlearn)\n[![Appveyor Build status](https://ci.appveyor.com/api/projects/status/myb325oc06w89flc?svg=true)](https://ci.appveyor.com/project/jonnor/emlearn)\n\n# emlearn\n\nMachine learning for microcontroller and embedded systems.\nTrain in Python, then do inference on any device with a C99 compiler.\n\n## Key features\n\nEmbedded-friendly Inference\n\n* Portable C99 code\n* No libc required\n* No dynamic allocations\n* Support integer/fixed-point math\n* Single header file include\n\nConvenient Training\n\n* Using Python with [scikit-learn](http://scikit-learn.org) or [Keras](https://keras.io/)\n* The generated C classifier is also accessible in Python\n\n[MIT licensed](./LICENSE.md)\n\nCan be used as an open source alternative to MATLAB Classification Trees,\nDecision Trees using MATLAB Coder for C/C++ code generation.\n`fitctree`, `fitcensemble`, `TreeBagger`, `ClassificationEnsemble`, `CompactTreeBagger`\n\n## Status\n**Minimally useful**\n\nClassifiers:\n\n* `eml_trees`: sklearn.RandomForestClassifier, sklearn.ExtraTreesClassifier, sklearn.DecisionTreeClassifier\n* `eml_net`: sklearn.MultiLayerPerceptron, Keras.Sequential with fully-connected layers\n* `eml_bayes`: sklearn.GaussianNaiveBayes\n\nFeature extraction:\n\n* `eml_audio`: Melspectrogram\n\nTested running on AVR Atmega, ESP8266 and Linux.\n\n## Installing\n\nInstall from PyPI\n\n    pip install --user emlearn\n\n## Usage\n\n1. Train your model in Python\n\n```python\nfrom sklearn.ensemble import RandomForestClassifier\nestimator = RandomForestClassifier(n_estimators=10, max_depth=10)\nestimator.fit(X_train, Y_train)\n...\n```\n\n2. Convert it to C code\n```python\nimport emlearn\ncmodel = emlearn.convert(estimator, method='inline')\ncmodel.save(file='sonar.h')\n```\n\n3. Use the C code\n\n```c\n#include \"sonar.h\"\n\nconst int32_t length = 60;\nint32_t values[length] = { ... };\nconst int32_t predicted_class = sonar_predict(values, length):\n```\n\n\nFor full example code, see [examples/digits.py](./examples/digits.py)\nand [emlearn.ino](./emlearn.ino)",
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