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# ProtoTorch
ProtoTorch is a PyTorch-based Python toolbox for bleeding-edge research in
prototype-based machine learning algorithms.
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[](https://travis-ci.org/si-cim/prototorch)
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[](https://github.com/si-cim/prototorch/releases)
[](https://pypi.org/project/prototorch/)
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[](https://codecov.io/gh/si-cim/prototorch)
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[](https://www.codacy.com/gh/si-cim/prototorch?utm_source=github.com& utm_medium=referral& utm_content=si-cim/prototorch& utm_campaign=Badge_Grade)
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[](https://github.com/si-cim/prototorch/blob/master/LICENSE)
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## Description
This is a Python toolbox brewed at the Mittweida University of Applied Sciences
in Germany for bleeding-edge research in Learning Vector Quantization (LVQ)
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and potentially other prototype-based methods. Although, there are
other (perhaps more extensive) LVQ toolboxes available out there, the focus of
ProtoTorch is ease-of-use, extensibility and speed.
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Many popular prototype-based Machine Learning (ML) algorithms like K-Nearest
Neighbors (KNN), Generalized Learning Vector Quantization (GLVQ) and Generalized
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Matrix Learning Vector Quantization (GMLVQ) are implemented using the "nn" API
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provided by PyTorch.
## Installation
ProtoTorch can be installed using `pip` .
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```bash
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pip install -U prototorch
```
To also install the extras, use
```bash
pip install -U prototorch[datasets,examples,tests]
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```
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To install the bleeding-edge features and improvements:
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```bash
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git clone https://github.com/si-cim/prototorch.git
git checkout dev
cd prototorch
pip install -e .
```
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## Usage
ProtoTorch is modular. It is very easy to use the modular pieces provided by
ProtoTorch, like the layers, losses, callbacks and metrics to build your own
prototype-based(instance-based) models. These pieces blend-in seamlessly with
numpy and PyTorch to allow you mix and match the modules from ProtoTorch with
other PyTorch modules.
ProtoTorch comes prepackaged with many popular LVQ algorithms in a convenient
API, with more algorithms and techniques coming soon. If you would simply like
to be able to use those algorithms to train large ML models on a GPU, ProtoTorch
lets you do this without requiring a black-belt in high-performance Tensor
computation.
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## Bibtex
If you would like to cite the package, please use this:
```bibtex
@misc {Ravichandran2020,
author = {Ravichandran, J},
title = {ProtoTorch},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/si-cim/prototorch}}
}
