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README.md
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README.md
@ -5,9 +5,15 @@ PyTorch-Lightning.
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## Installation
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To install this plugin, simple install
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[ProtoTorch](https://github.com/si-cim/prototorch) first by following the
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installation instructions there and then install this plugin by doing:
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To install this plugin, first install
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[ProtoTorch](https://github.com/si-cim/prototorch) with:
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```sh
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git clone https://github.com/si-cim/prototorch.git && cd prototorch
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pip install -e .
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```
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and then install the plugin itself with:
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```sh
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git clone https://github.com/si-cim/prototorch_models.git && cd prototorch_models
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@ -28,9 +34,14 @@ following:
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```sh
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export WORKON_HOME=~/pyenvs
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mkdir -p $WORKON_HOME
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source /usr/local/bin/virtualenvwrapper.sh # might be different
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# source ~/.local/bin/virtualenvwrapper.sh
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source /usr/local/bin/virtualenvwrapper.sh # location may vary
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mkvirtualenv pt
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```
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Once you have a virtual environment setup, you can start install the `models`
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plugin with:
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```sh
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workon pt
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git clone git@github.com:si-cim/prototorch_models.git
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cd prototorch_models
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@ -43,23 +54,31 @@ To assist in the development process, you may also find it useful to install
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## Available models
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- GLVQ
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- Generalized Learning Vector Quantization (GLVQ)
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- Generalized Matrix Learning Vector Quantization (GMLVQ)
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- Limited-Rank Matrix Learning Vector Quantization (LiRaMLVQ)
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- Siamese GLVQ
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- Neural Gas
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- GMLVQ
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- Limited-Rank GMLVQ
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- Neural Gas (NG)
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## Work in Progress
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- CBC
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- LVQMLN
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- Classification-By-Components Network (CBC)
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- Learning Vector Quantization Multi-Layer Network (LVQMLN)
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## Planned models
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- Local-Matrix GMLVQ
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- GTLVQ
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- RSLVQ
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- PLVQ
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- SILVQ
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- KNN
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- LVQ1
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- Generalized Tangent Learning Vector Quantization (GTLVQ)
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- Robust Soft Learning Vector Quantization (RSLVQ)
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- Probabilistic Learning Vector Quantization (PLVQ)
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- Self-Incremental Learning Vector Quantization (SILVQ)
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- K-Nearest Neighbors (KNN)
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- Learning Vector Quantization 1 (LVQ1)
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## FAQ
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### How do I update the plugin?
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If you have already cloned and installed `prototorch` and the
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`prototorch_models` plugin with the `-e` flag via `pip`, all you have to do is
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navigate to those folders from your terminal and do `git pull` to update.
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