Metadata-Version: 2.1
Name: prototorch
Version: 0.1.0rc0
Summary: Highly extensible, GPU-supported Learning Vector Quantization (LVQ) toolbox built using PyTorch and its nn API.
Home-page: https://github.com/si-cim/prototorch
Author: Jensun Ravichandran
Author-email: jjensun@gmail.com
License: MIT
Download-URL: https://github.com/si-cim/prototorch.git
Description: # ProtoTorch
        
        ProtoTorch is a PyTorch-based Python toolbox for bleeding-edge research in
        prototype-based machine learning algorithms.
        
        [![Build Status](https://travis-ci.org/si-cim/prototorch.svg?branch=master)](https://travis-ci.org/si-cim/prototorch)
        [![GitHub version](https://badge.fury.io/gh/si-cim%2Fprototorch.svg)](https://badge.fury.io/gh/si-cim%2Fprototorch)
        [![PyPI version](https://badge.fury.io/py/prototorch.svg)](https://badge.fury.io/py/prototorch)
        ![Tests](https://github.com/si-cim/prototorch/workflows/Tests/badge.svg)
        [![codecov](https://codecov.io/gh/si-cim/prototorch/branch/master/graph/badge.svg)](https://codecov.io/gh/si-cim/prototorch)
        [![Downloads](https://pepy.tech/badge/prototorch)](https://pepy.tech/project/prototorch)
        [![GitHub license](https://img.shields.io/github/license/si-cim/prototorch)](https://github.com/si-cim/prototorch/blob/master/LICENSE)
        
        ## 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)
        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.
        
        Many popular prototype-based Machine Learning (ML) algorithms like K-Nearest
        Neighbors (KNN), Generalized Learning Vector Quantization (GLVQ) and Generalized
        Matrix Learning Vector Quantization (GMLVQ) are implemented using the "nn" API
        provided by PyTorch.
        
        ## Installation
        
        ProtoTorch can be installed using `pip`.
        ```
        pip install prototorch
        ```
        
        To install the bleeding-edge features and improvements:
        ```
        git clone https://github.com/si-cim/prototorch.git
        git checkout dev
        cd prototorch
        pip install -e .
        ```
        
        ## 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.
        
        ## 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}}
        }
        
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Provides-Extra: examples
Provides-Extra: tests
