{ "info": { "author": "Frootlab Developers", "author_email": "contact@frootlab.org", "bugtrack_url": null, "classifiers": [ "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Operating System :: OS Independent", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.7", "Topic :: Software Development :: Libraries :: Python Modules" ], "description": "
\n \n
\n\nVivid Code\n==========\n\n[![Building Status](https://travis-ci.org/frootlab/vivid.svg?branch=master)](https://travis-ci.org/frootlab/rian)\n[![Documentation Status](https://readthedocs.org/projects/vivid/badge/?version=latest)](https://vivid.readthedocs.io/en/latest/?badge=latest)\n[![PIP Version](https://badge.fury.io/py/vivid.svg)](https://badge.fury.io/py/vivid)\n\n**Vivid Code** is a pioneering software framework for next generation data\nanalysis applications, that interconnects collaborative data science with\nautomated machine learning.\n\nBased on the **Cloud-Assisted Meta programming** (CAMP) paradigm, the framework\nallows the usage of **Currently Best Fitting** (CBF) algorithms. Before code\ninterpretation / compilation the concrete algorithms, that implement the CBF\nspecifications, are automatically chosen from local and public catalog servers,\nthat host and deploy the concrete algorithms. Thereby the specification is\nconstituted by a unique algorithm category, a data domain and a metric, which\nsubstantiates the meaning of *Best Fitting* within the respective algorithm- and\ndata context. An example is the average prediction accuracy within a fixed set\nof gold standard samples of the data domain (e.g. latin handwriting samples,\nspoken word samples, TCGA gene expression data, etc.).\n\nThe Vivid Code framework allows the implementation of cutting edge enterprise\nanalytical applications, that are automatically kept up-to-date and therefore\n**minimize their maintenance costs**. Also the Vivid Code framework facilitates\nthe publication, application, sharing and comparison of algorithms, within and\nbetween workgroups.\n\nAll components of the Vivid Code framework are open source and based on the\n[Python](https://www.python.org/) programming language.\n\nCurrent Development Status\n--------------------------\n\nThe individual components of the Vivid Code frame work are in different\ndevelopment stages.\nRian currently is in *Pre-Alpha* development stage, which immediately follows\nthe *Planning* stage. This means, that at least some essential requirements of\nRian are not yet implemented.\n\nInstallation\n------------\n\nComprehensive information and installation support is provided within the\n[online manual](http://docs.frootlab.org/vivid). If you already have a\nPython environment configured on your computer, you can install the latest\ndistributed version by using pip:\n\n $ pip install vivid\n\nDocumentation\n-------------\n\nThe documentation of the latest distributed version is available as an [online\nmanual](http://docs.frootlab.org/vivid) and for download, given in the\nformats [PDF](https://readthedocs.org/projects/vivid/downloads/pdf/latest/),\n[EPUB](https://readthedocs.org/projects/vivid/downloads/epub/latest/) and\n[HTML](https://readthedocs.org/projects/vivid/downloads/htmlzip/latest/).\n\nContributions\n-------------\n\nContributors are very welcome! Feel free to report bugs, ideas and feature\nrequests to the [issue tracker](https://github.com/frootlab/vivid/issues),\nprovided by GitHub. Currently, as our team still is growing, we do not provide\nany Contribution Guide Lines. So, if you are interested to help or to join the\nteam, we would be glad, to [hear about you](mailto:application@frootlab.org).\n\nLicense\n-------\n\nAll components of the Vivid Code frame work are open source software and\navailable free for any use under the\n[GPLv3 license](https://www.gnu.org/licenses/gpl.html):\n\n \u00c2\u00a9 2019 Frootlab Developers:\n Patrick Michl \n \u00c2\u00a9 2013-2019 Patrick Michl\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://www.frootlab.org/vivid", "keywords": "collaborative-research data-science machine-learning framework artificial-intelligence artificial-neural-networks platform python python-library collaborative-data-science automated-machine-learning", "license": "GPLv3", "maintainer": "", "maintainer_email": "", "name": "vivid", "package_url": "https://pypi.org/project/vivid/", "platform": "", "project_url": 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