ykankaya / pyuds

pyuds

pyuds

Introduction

pyuds is a Python library for measuring uncertainty in Dempster-Shafer theory of evidence. The functionals supported are Generalized Hartley (GH) uncertainty functional, Generalized Shannon (GS) uncertainty functional, and Aggregate Uncertainty (AU) functional. The library can be utilized either through its API, or through a user-friendly web interface.

Ingredients

Anti Inference Hub uses the following software components:

1. Python 3.0 or higher

2. Mod_python

Documentation

Refer to the User's Guide.

Screenshots

pyuds Web Interface

Q&A

Post your questions to pyuds mailing list.

Licence

Copyright © Sari Haj Hussein.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Code Disclaimer

The author of this software code has used his best efforts in preparing the code. These efforts include the development, research, testing, and optimization of the theories and programs to determine their effectiveness. This software code is not designed or intended for use in the design, construction, operation or maintenance of any nuclear facility. Author disclaims any express or implied warranty of fitness for such uses. The author makes no warranty of any kind, expressed or implied, with regard to this software code or to the documentation accompanying it. In no event shall the author be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption whatsoever) arising out of, the furnishing, performance, or use of this software code, even if advised of the possibilities of such damages.