Metadata-Version: 2.1
Name: prototorch-models
Version: 0.0.0
Summary: Pre-packaged prototype-based machine learning models using ProtoTorch and PyTorch-Lightning.
Home-page: https://github.com/si-cim/prototorch_models
Author: Alexander Engelsberger
Author-email: engelsbe@hs-mittweida.de
License: MIT
Download-URL: https://github.com/si-cim/prototorch_models.git
Description: # ProtoTorch Models
        
        Pre-packaged prototype-based machine learning models using ProtoTorch and
        PyTorch-Lightning.
        
        ## Installation
        
        To install this plugin, first install
        [ProtoTorch](https://github.com/si-cim/prototorch) with:
        
        ```sh
        git clone https://github.com/si-cim/prototorch.git && cd prototorch
        pip install -e .
        ```
        
        and then install the plugin itself with:
        
        ```sh
        git clone https://github.com/si-cim/prototorch_models.git && cd prototorch_models
        pip install -e .
        ```
        
        The plugin should then be available for use in your Python environment as
        `prototorch.models`.
        
        ## 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
        source /usr/local/bin/virtualenvwrapper.sh  # location may vary
        mkvirtualenv pt
        ```
        
        Once you have a virtual environment setup, you can start install the `models`
        plugin with:
        
        ```sh
        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
        ```
        
        To assist in the development process, you may also find it useful to install
        `yapf`, `isort` and `autoflake`. You can install them easily with `pip`.
        
        ## Available models
        
        - Generalized Learning Vector Quantization (GLVQ)
        - Generalized Relevance Learning Vector Quantization (GRLVQ)
        - Generalized Matrix Learning Vector Quantization (GMLVQ)
        - Limited-Rank Matrix Learning Vector Quantization (LiRaMLVQ)
        - Siamese GLVQ
        - Neural Gas (NG)
        
        ## Work in Progress
        
        - Classification-By-Components Network (CBC)
        - Learning Vector Quantization Multi-Layer Network (LVQMLN)
        
        ## Planned models
        
        - Local-Matrix GMLVQ
        - Generalized Tangent Learning Vector Quantization (GTLVQ)
        - Robust Soft Learning Vector Quantization (RSLVQ)
        - Probabilistic Learning Vector Quantization (PLVQ)
        - Self-Incremental Learning Vector Quantization (SILVQ)
        - K-Nearest Neighbors (KNN)
        - Learning Vector Quantization 1 (LVQ1)
        
        ## 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.
        
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Environment :: Plugins
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Provides-Extra: dev
Provides-Extra: examples
Provides-Extra: tests
Provides-Extra: all
