Metadata-Version: 2.1
Name: protoflow
Version: 0.2.1
Summary: Highly extensible, GPU-supported Learning Vector Quantization (LVQ) toolbox built using Tensorflow 2.x and its Keras API.
Home-page: https://github.com/si-cim/protoflow
Author: Jensun Ravichandran
Author-email: jjensun@gmail.com
License: MIT
Download-URL: https://github.com/si-cim/protoflow.git
Description: # ProtoFlow
        
        ProtoFlow is a TensorFlow-based Python toolbox for bleeding-edge research in prototype-based machine learning algorithms.
        
        ![tests](https://github.com/si-cim/protoflow/workflows/tests/badge.svg)
        [![docs](https://readthedocs.org/projects/protoflow/badge/?version=latest)](https://protoflow.readthedocs.io/en/latest/?badge=latest)
        [![PyPI](https://img.shields.io/pypi/v/protoflow)](https://pypi.org/project/protoflow/)
        ![PyPI - Downloads](https://img.shields.io/pypi/dm/protoflow?color=blue)
        [![GitHub license](https://img.shields.io/github/license/si-cim/protoflow)](https://github.com/si-cim/protoflow/blob/master/LICENSE)
        
        *PyTorch users, please see:* [ProtoTorch](https://github.com/si-cim/prototorch)
        
        ## 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)
        methods. Although, there are other (perhaps more extensive) LVQ toolboxes
        available out there, the focus of ProtoFlow 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) including the recent Learning Vector
        Quantization Multi-Layer Network (LVQMLN) are implemented as Tensorflow models
        using the Keras API.
        
        ## Installation
        
        ProtoFlow can be easily installed using `pip`.
        ```
        pip install -U protoflow
        ```
        To also install the extras, use
        ```bash
        pip install -U protoflow[examples,other,tests]
        ```
        To install the bleeding-edge features and improvements:
        ```bash
        git clone https://github.com/si-cim/prototorch.git
        git checkout dev
        cd prototorch
        pip install -e .
        ```
        
        ## Documentation
        
        The documentation is available at https://protoflow.readthedocs.io/en/latest/
        
        ## Usage
        
        ProtoFlow is modular. It is very easy to use the modular pieces provided by
        ProtoFlow, like the layers, losses, callbacks and metrics to build your own
        prototype-based(instance-based) models. These pieces blend-in seamlessly with
        Keras allowing you to mix and match the modules from ProtoFlow with other Keras
        modules.
        
        ProtoFlow 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, ProtoFlow 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{Ravichandran2020a,
          author = {Ravichandran, J},
          title = {ProtoFlow},
          year = {2020},
          publisher = {GitHub},
          journal = {GitHub repository},
          howpublished = {\url{https://github.com/si-cim/protoflow}}
        }
        
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: other
Provides-Extra: tests
Provides-Extra: docs
