Skip to main content

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

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.

Release history Release notifications | RSS feed

This release

1.5.1 This release

1 file

1.5.0

1 file

1.4.1

1 file

1.3.2

1 file

1.3.0

2 files

1.2.1

1 file

1.0

1 file

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page