Skip to main content

A collection of aggregations, such as OWAs, Choquet and Sugeno integrals, etc.

Project description

Fancy Aggregations DOI

Fanzy Aggregations is a package written in python that implements modern functions to aggregate data using Choquet integral, CF12 generalization, Sugeno, etc. More are to come. Our target is to give a wide range of functions to work with and to generate/use different fuzzy measures.

Implemented functions

  • Choquet Integral
  • Choquet Integral CF and CF1,2
  • Sugeno integral and generalizations.
  • Wide range of T-norms (and T-conorms).
  • Implication operators.
  • OWA operators.
  • Penalty functions.
  • MD deviations.
  • N-Overlap functions.

Citation

Fancy Aggregations has its own DOI. If you want to cite it, you can use it. In case you prefer to cite a published paper, you can find a comprehensive list of aggregations, implemented with this library in:

Fumanal-Idocin, J., Wang, Y. K., Lin, C. T., Fernández, J., Sanz, J. A., & Bustince, H. (2021). Motor-Imagery-Based Brain-Computer Interface Using Signal Derivation and Aggregation Functions. IEEE Transactions on Cybernetics.

Reference papers

Each file contains the correspondent paper in its header. Here it is the whole list:

[1] A.H. Altalhi, J.I. Forcén, M. Pagola, E. Barrenechea, H. Bustince, Zdenko Takáč, Moderate deviation and restricted equivalence functions for measuring similarity between data, Information Sciences,Volume 501, 2019, Pages 19-29, ISSN 0020-0255, https://doi.org/10.1016/j.ins.2019.05.078. (http://www.sciencedirect.com/science/article/pii/S0020025519305031)

[2] Bustince, H., Beliakov, G., Dimuro, G. P., Bedregal, B., & Mesiar, R. (2017). On the definition of penalty functions in data aggregation. Fuzzy Sets and Systems, 323, 1-18.

[3] A. Jurio, M. Pagola, R. Mesiar, G. Beliakov and H. Bustince, "Image Magnification Using Interval Information," in IEEE Transactions on Image Processing, vol. 20, no. 11, pp. 3112-3123, Nov. 2011. doi: 10.1109/TIP.2011.2158227 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5782984&isnumber=6045652

[4] Graçaliz Pereira Dimuro, Giancarlo Lucca, Benjamín Bedregal, Radko Mesiar, José Antonio Sanz, Chin-Teng Lin, Humberto Bustince, Generalized CF1F2-integrals: From Choquet-like aggregation to ordered directionally monotone functions, Fuzzy Sets and Systems, Volume 378, 2020, Pages 44-67, ISSN 0165-0114, https://doi.org/10.1016/j.fss.2019.01.009. (http://www.sciencedirect.com/science/article/pii/S0165011418305451)

[5] Beliakov, G., Sola, H. B., & Sánchez, T. C. (2016). A practical guide to averaging functions (Vol. 329). Heidelberg: Springer.

Mandatory

  • Numpy

Project details


Download files

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

Source Distribution

Fancy_aggregations-1.5.1.tar.gz (17.5 kB view details)

Uploaded Source

File details

Details for the file Fancy_aggregations-1.5.1.tar.gz.

File metadata

  • Download URL: Fancy_aggregations-1.5.1.tar.gz
  • Upload date:
  • Size: 17.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/34.0 requests/2.26.0 requests-toolbelt/0.9.1 urllib3/1.26.7 tqdm/4.62.3 importlib-metadata/4.8.1 keyring/23.1.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.9.7

File hashes

Hashes for Fancy_aggregations-1.5.1.tar.gz
Algorithm Hash digest
SHA256 3b79e5a72f448f9d2524e4182f549971a8bbc9345b4d7c39fbbe8df87f415fb6
MD5 d68528596b9381ea23b59d6394a58cec
BLAKE2b-256 4f1fb36c72a37b9f19151a95323b63c5bb509dce198241ab02ba03782ce20bfd

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page