prototorch_models/README.md

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# ProtoTorch Models
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[![Build Status](https://travis-ci.org/si-cim/prototorch_models.svg?branch=main)](https://travis-ci.org/si-cim/prototorch_models)
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[![PyPI](https://img.shields.io/pypi/v/prototorch_models)](https://pypi.org/project/prototorch_models/)
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Pre-packaged prototype-based machine learning models using ProtoTorch and
PyTorch-Lightning.
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## Installation
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To install this plugin, simply run the following command:
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```sh
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pip install prototorch_models
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```
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**Installing the models plugin should automatically install a suitable version
of** [ProtoTorch](https://github.com/si-cim/prototorch). The plugin should then
be available for use in your Python environment as `prototorch.models`.
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## Available models
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### LVQ Family
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- Learning Vector Quantization 1 (LVQ1)
- Generalized Learning Vector Quantization (GLVQ)
- Generalized Relevance Learning Vector Quantization (GRLVQ)
- Generalized Matrix Learning Vector Quantization (GMLVQ)
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- Localized and Generalized Matrix Learning Vector Quantization (LGMLVQ)
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- Limited-Rank Matrix Learning Vector Quantization (LiRaMLVQ)
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- Learning Vector Quantization Multi-Layer Network (LVQMLN)
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- Siamese GLVQ
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- Cross-Entropy Learning Vector Quantization (CELVQ)
- Robust Soft Learning Vector Quantization (RSLVQ)
### Other
- k-Nearest Neighbors (KNN)
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- Neural Gas (NG)
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- Growing Neural Gas (GNG)
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## Work in Progress
- Classification-By-Components Network (CBC)
- Learning Vector Quantization 2.1 (LVQ2.1)
## Planned models
- Median-LVQ
- Generalized Tangent Learning Vector Quantization (GTLVQ)
- Probabilistic Learning Vector Quantization (PLVQ)
- Self-Incremental Learning Vector Quantization (SILVQ)
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## Development setup
It is recommended that you use a virtual environment for development. If you do
not use `conda`, the easiest way to work with virtual environments is by using
[virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/). Once
you've installed it with `pip install virtualenvwrapper`, you can do the
following:
```sh
export WORKON_HOME=~/pyenvs
mkdir -p $WORKON_HOME
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source /usr/local/bin/virtualenvwrapper.sh # location may vary
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mkvirtualenv pt
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```
Once you have a virtual environment setup, you can start install the `models`
plugin with:
```sh
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workon pt
git clone git@github.com:si-cim/prototorch_models.git
cd prototorch_models
git checkout dev
pip install -e .[all] # \[all\] if you are using zsh or MacOS
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```
To assist in the development process, you may also find it useful to install
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`yapf`, `isort` and `autoflake`. You can install them easily with `pip`. **Also,
please avoid installing Tensorflow in this environment. It is known to cause
problems with PyTorch-Lightning.**
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## FAQ
### How do I update the plugin?
If you have already cloned and installed `prototorch` and the
`prototorch_models` plugin with the `-e` flag via `pip`, all you have to do is
navigate to those folders from your terminal and do `git pull` to update.