Metadata-Version: 2.1
Name: nemoa
Version: 0.5.582
Summary: Enterprise Machine-Learning and Predictive Analytics
Home-page: https://www.frootlab.org/nemoa
Author: Frootlab Developers
Author-email: contact@frootlab.org
License: GPLv3
Description: <div align="center">
          <a href="https://github.com/frootlab/nemoa">
            <img src="https://bit.ly/2VRCy9t">
          </a>
          <br>
        </div>
        
        Nemoa
        =====
        
        [![Building Status](https://travis-ci.org/frootlab/nemoa.svg?branch=master)](https://travis-ci.org/frootlab/nemoa)
        [![Documentation Status](https://readthedocs.org/projects/nemoa/badge/?version=latest)](https://nemoa.readthedocs.io/en/latest/?badge=latest)
        [![PIP Version](https://badge.fury.io/py/nemoa.svg)](https://badge.fury.io/py/nemoa)
        
        *Nemoa* is a machine learning- and data analysis framework, that implements the
        **Cloud-Assisted Meta Programming** (CAMP) paradigm.
        
        The key goal of Nemoa is to provide a long-term data analysis framework, which
        seemingly integrates into existing enterprise data environments and thereby
        supports collaborative data science. To achieve this goal Nemoa orchestrates
        established Python frameworks like [TensorFlow®](https://www.tensorflow.org/)
        and [SQLAlchemy](https://www.sqlalchemy.org/) and dynamically extends their
        capabilities by community driven algorithms (e.g. for [probabilistic graphical
        modeling](https://en.wikipedia.org/wiki/Graphical_model), [machine
        learning](https://en.wikipedia.org/wiki/Machine_learning) and [structured
        data-analysis](https://en.wikipedia.org/wiki/Structured_data_analysis_(statistics))).
        
        Thereby Nemoa allows client-side implementations to use abstract **currently
        best fitting** (CBF) algorithms. During runtime the concrete implementation of
        CBF algorithms are chosen server-sided by category and metric. An example for
        such a metric would be the average prediction accuracy within a fixed set of
        gold standard samples of the respective domain of application (e.g. latin
        handwriting samples, spoken word samples, TCGA gene expression data, etc.).
        
        Nemoa is [open source](https://github.com/frootlab/pandora), based on the
        [Python](https://www.python.org/) programming language and actively developed as
        part of the [Liquid ML](https://github.com/orgs/frootlab/projects) framework
        at [Frootlab](https://github.com/frootlab).
        
        Current Development Status
        --------------------------
        
        Nemoa currently is in *Pre-Alpha* development stage, which immediately follows
        the *Planning* stage. This means, that at least some essential requirements of
        Nemoa are not yet implemented.
        
        Installation
        ------------
        
        Comprehensive information and installation support is provided within the
        [online manual](http://docs.frootlab.org/nemoa). If you already have a
        Python environment configured on your computer, you can install the latest
        distributed version by using pip:
        
            $ pip install nemoa
        
        Documentation
        -------------
        
        The documentation of the latest distributed version is available as an [online
        manual](http://docs.frootlab.org/nemoa) and for download, given in the
        formats [PDF](https://readthedocs.org/projects/nemoa/downloads/pdf/latest/),
        [EPUB](https://readthedocs.org/projects/nemoa/downloads/epub/latest/) and
        [HTML](https://readthedocs.org/projects/nemoa/downloads/htmlzip/latest/).
        
        Contribute
        ----------
        
        Contributors are very welcome! Feel free to report bugs and feature requests to
        the [issue tracker](https://github.com/frootlab/nemoa/issues) provided by
        GitHub. Currently, as the Frootlab Developers team still is growing, we do not
        provide any Contribution Guide Lines to collaboration partners. However, if you
        are interested to join the team, we would be glad, to receive an informal
        [application](mailto:application@frootlab.org).
        
        License
        -------
        
        Nemoa is [open source](https://github.com/frootlab/pandora) and available free
        for any use under the [GPLv3 license](https://www.gnu.org/licenses/gpl.html):
        
            © 2019 Frootlab Developers:
              Patrick Michl <patrick.michl@frootlab.org>
            © 2013-2019 Patrick Michl
        
Keywords: data-analysis enterprise-data-analysis data-science collaborative-data-science data-visualization machine-learning artificial-intelligence deep-learning probabilistic-graphical-model
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Database :: Database Engines/Servers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: gui
Provides-Extra: gene
