Metadata-Version: 1.2
Name: tfgraph
Version: 0.2
Summary: Python's Tensorflow Graph Library
Home-page: http://tfgraph.readthedocs.io/en/latest/
Author: garciparedes
Author-email: sergio@garciparedes.me
License: UNKNOWN
Download-URL: https://github.com/tfgraph/tfgraph
Description: TFGraph: Python's Tensorflow Graph Library
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        .. |travisci| image:: https://img.shields.io/travis/tfgraph/tfgraph/master.svg?style=flat-square
           :target: https://travis-ci.org/tfgraph/tfgraph
        
        .. |codecov| image:: https://img.shields.io/codecov/c/github/tfgraph/tfgraph.svg?style=flat-square
           :target: https://codecov.io/gh/tfgraph/tfgraph?branch=master
        
        .. |docs| image:: https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat-square
           :target: http://tfgraph.readthedocs.io/en/latest/?badge=latest
        
        .. |gitter| image:: https://img.shields.io/gitter/room/tfgraph/tfgraph.svg?style=flat-square
           :target: https://gitter.im/tfgraph/tfgraph?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge
        
        .. |license| image:: https://img.shields.io/github/license/tfgraph/tfgraph.svg?style=flat-square
            :target: https://github.com/tfgraph/tfgraph
        
        .. |release| image:: https://img.shields.io/github/release/tfgraph/tfgraph.svg?style=flat-square
            :target: https://github.com/tfgraph/tfgraph
        
        |travisci| |codecov| |docs| |gitter| |license| |release|
        
        Description
        -----------
        This work consists of a study of a set of techniques and strategies related with algorithm's design, whose purpose is the resolution of problems on massive data sets, in an efficient way. This field is known as Algorithms for Big Data. In particular, this work has studied the Streaming Algorithms, which represents the basis of the data structures of sublinear order o(n) in space, known as Sketches. In addition, it has deepened in the study of problems applied to Graphs on the Semi-Streaming model. Next, the PageRank algorithm was analyzed as a concrete case study. Finally, the development of a library for the resolution of graph problems, implemented on the top of the intensive mathematical computation platform known as TensorFlow has been started.
        
        Content
        -------
        * `Source Code <https://github.com/tfgraph/tfgraph/blob/master/tfgraph>`__
        * `API Documentation <http://tfgraph.readthedocs.io/>`__
        * `Code Examples <https://github.com/tfgraph/tfgraph/blob/master/examples>`__
        * `Tests <https://github.com/tfgraph/tfgraph/blob/master/tests>`__
        
        
        How to install
        --------------
        
        If you have git installed, you can try::
        
            $ pip install git+https://github.com/tfgraph/tfgraph.git
        
        If you get any installation or compilation errors, make sure you have the latest pip and setuptools::
        
            $ pip install --upgrade pip setuptools
        
        How to run the tests
        --------------------
        
        Install in editable mode and call `pytest`::
        
            $ pip install -e .
            $ pytest
        
Keywords: tfg,bigdata,tensorflow,graph theory,pagerank,university of valladolid
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.5
