Skip to main content
Join the official 2019 Python Developers SurveyStart the survey!

Enterprise Machine-Learning and Predictive Analytics

Project description

Vivid Code

Building Status Documentation Status PIP Version

Vivid Code is a pioneering software framework for next generation data analysis applications, that interconnects collaborative data science with automated machine learning.

Based on the Cloud-Assisted Meta programming (CAMP) paradigm, the framework allows the usage of Currently Best Fitting (CBF) algorithms. Before code interpretation / compilation the concrete algorithms, that implement the CBF specifications, are automatically chosen from local and public catalog servers, that host and deploy the concrete algorithms. Thereby the specification is constituted by a unique algorithm category, a data domain and a metric, which substantiates the meaning of Best Fitting within the respective algorithm- and data context. An example is the average prediction accuracy within a fixed set of gold standard samples of the data domain (e.g. latin handwriting samples, spoken word samples, TCGA gene expression data, etc.).

The Vivid Code framework allows the implementation of cutting edge enterprise analytical applications, that are automatically kept up-to-date and therefore minimize their maintenance costs. Also the Vivid Code framework facilitates the publication, application, sharing and comparison of algorithms, within and between workgroups.

All components of the Vivid Code framework are open source and based on the Python programming language.

Current Development Status

The individual components of the Vivid Code frame work are in different development stages. Rian currently is in Pre-Alpha development stage, which immediately follows the Planning stage. This means, that at least some essential requirements of Rian are not yet implemented.

Installation

Comprehensive information and installation support is provided within the online manual. If you already have a Python environment configured on your computer, you can install the latest distributed version by using pip:

$ pip install vivid

Documentation

The documentation of the latest distributed version is available as an online manual and for download, given in the formats PDF, EPUB and HTML.

Contributions

Contributors are very welcome! Feel free to report bugs, ideas and feature requests to the issue tracker, provided by GitHub. Currently, as our team still is growing, we do not provide any Contribution Guide Lines. So, if you are interested to help or to join the team, we would be glad, to hear about you.

License

All components of the Vivid Code frame work are open source software and available free for any use under the GPLv3 license:

© 2019 Frootlab Developers:
  Patrick Michl <patrick.michl@frootlab.org>
© 2013-2019 Patrick Michl

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for vivid, version 19.9.post3
Filename, size File type Python version Upload date Hashes
Filename, size vivid-19.9.post3-py3-none-any.whl (15.5 kB) File type Wheel Python version py3 Upload date Hashes View hashes
Filename, size vivid-19.9.post3.tar.gz (3.8 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page