Metadata-Version: 2.1
Name: dualing
Version: 1.0.2
Summary: Dual-based Neural Learning
Home-page: https://github.com/gugarosa/dualing
Author: Gustavo Rosa
Author-email: gustavo.rosa@unesp.br
License: MIT
Description: # Dualing: Dual-based Neural Learning
        
        [![Latest release](https://img.shields.io/github/release/gugarosa/dualing.svg)](https://github.com/gugarosa/dualing/releases)
        [![Build status](https://img.shields.io/travis/com/gugarosa/dualing/master.svg)](https://github.com/gugarosa/dualing/releases)
        [![Open issues](https://img.shields.io/github/issues/gugarosa/dualing.svg)](https://github.com/gugarosa/dualing/issues)
        [![License](https://img.shields.io/github/license/gugarosa/dualing.svg)](https://github.com/gugarosa/dualing/blob/master/LICENSE)
        
        ## Welcome to Dualing.
        
        Have you ever wanted to find if there is any similarity between your data? If yes, Dualing is the right package! We implement state-of-the-art dual-based neural networks, such as Siamese Networks, to cope with learning similarity functions between sets of data. Such a strategy helps in providing clearer manifolds and better-embedded data for a wide range of applications.
        
        Use Dualing if you need a library or wish to:
        
        * Create similarity measures;
        * Design or use pre-implement state-of-the-art Siamese Networks;
        * Mix-and-match a new approach to solve your problem;
        * Because it is fun to find resemblances;
        
        Read the docs at [dualing.readthedocs.io](https://dualing.readthedocs.io).
        
        Dualing is compatible with: **Python 3.6+**.
        
        ---
        
        ## Package guidelines
        
        1. The very first information you need is in the very **next** section.
        2. **Installing** is also easy if you wish to read the code and bump yourself into, follow along.
        3. Note that there might be some **additional** steps in order to use our solutions.
        4. If there is a problem, please do not **hesitate**. Call us.
        
        ---
        
        ## Getting started: 60 seconds with Dualing
        
        First of all. We have examples. Yes, they are commented. Just browse to `examples/`, choose your subpackage, and follow the example. We have high-level examples for most of the tasks we could think.
        
        Alternatively, if you wish to learn even more, please take a minute:
        
        Dualing is based on the following structure, and you should pay attention to its tree:
        
        ```yaml
        - dualing
            - core
                - dataset
                - loss
                - model
            - datasets
                - batch
                - pair
            - models
                - base
                    - cnn
                    - gru
                    - lstm
                    - mlp
                    - rnn
                - contrastive
                - cross_entropy
                - triplet
            - utils
                - constants
                - exception
                - logging
                - projector
        ```
        
        ### Core
        
        Core is the core. Essentially, it is the parent of everything. You should find parent classes defining the basis of our structure. They should provide variables and methods that will help to construct other modules.
        
        ### Datasets
        
        Because we need data, right? Datasets are composed of classes and methods that allow preparing data for further application in dual-based learning.
        
        ### Models
        
        This is the heart. All models are declared and implemented here. We will offer you the most fantastic implementation of everything we are working with. Please take a closer look at this package.
        
        ### Utils
        
        This is a utility package. Common things shared across the application should be implemented here. It is better to implement once and use it as you wish than re-implementing the same thing repeatedly.
        
        ---
        
        ## Installation
        
        We believe that everything has to be easy. Not tricky or daunting, Dualing will be the one-to-go package that you will need, from the very first installation to the daily-tasks implementing needs. If you may just run the following under your most preferred Python environment (raw, conda, virtualenv, whatever):
        
        ```bash
        pip install dualing
        ```
        
        Alternatively, if you prefer to install the bleeding-edge version, please clone this repository and use:
        
        ```bash
        pip install -e .
        ```
        
        ---
        
        ## Environment configuration
        
        Note that sometimes, there is a need for additional implementation. If needed, from here, you will be the one to know all of its details.
        
        ### Ubuntu
        
        No specific additional commands needed.
        
        ### Windows
        
        No specific additional commands needed.
        
        ### MacOS
        
        No specific additional commands needed.
        
        ---
        
        ## Support
        
        We know that we do our best, but it is inevitable to acknowledge that we make mistakes. If you ever need to report a bug, report a problem, talk to us, please do so! We will be available at our bests at this repository or gustavo.rosa@unesp.br.
        
        ---
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
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: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Provides-Extra: tests
