convolutionalfixedsum

ConvolutionalFixedSum Algorithm

https://github.com/dgdguk/convolutionalfixedsum

Science Score: 31.0%

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Low similarity (11.1%) to scientific vocabulary

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Repository

ConvolutionalFixedSum Algorithm

Basic Info

Host: GitHub
Owner: dgdguk
License: bsd-3-clause
Language: Python
Default Branch: main
Size: 65.4 KB

Statistics

Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Releases: 1

Created over 1 year ago · Last pushed about 1 year ago

Metadata Files

Readme License Citation

ConvolutionalFixedSum

ConvolutionalFixedSum is an algorithm for generating vectors of random numbers such that:

The values of the vector sum to a given total U
Given a vector of upper constraints, each element of the returned vector is less than or equal to its corresponding upper bound
Given a vector of lower constraints, each element of the returned vector is greater or equal to than its corresponding lower bound
The distribution of the vectors in the space defined by the constraints is uniform.

This algorithm was developed when the authors found that their prior work, the Dirichlet-Rescale Algorithm (DRS), did not, in fact generate values uniformly. As such, ConvolutionalFixedSum supercedes the DRS algorithm.

Usage

Two implementations of ConvolutionalFixedSum are provided: an analytical method, cfsa, which scales exponentially with the length of the vector $n$ and is subject to floating point error, and the recommended numerical approximation cfsn which scales polynomially with $n$. cfsa can be useful for $n \leq 15$, while cfs should work well for larger n. See the paper for a full discussion on this aspect.

Below are some examples on how to use the library.

```python from convolutionalfixedsum import cfsa, cfsn

Generate 3 random values which sum to 2.0, are bounded below by 0 and above by 1

cfsn(3, total=2.0)

Generate 3 random values which sum to 1.0, and whose upper constraints are [1.0, 0.5, 0.1] respectively

cfsn(3, upper_constraints=[1.0, 0.5, 0.1])

Same as before, but the middle value can not be lower than 0.3

cfsn(3, lowerconstraints=[0.0, 0.3, 0.0], upperconstraints=[1.0, 0.5, 0.1])

cfsa has basically the same function signature for most uses

cfsa(3, lowerconstraints=[0.0, 0.3, 0.0], upperconstraints=[1.0, 0.5, 0.1]) ```

Citation

If you wish to cite this software package, use the cite this repository feature of Github. It can give citation data in a variety of formats.

If you wish to cite the underlying research, please cite the following paper (Bibtex only, DOI after proceedings are published).

bibtex @inproceedings{Griffin2025, title={ConvolutionalFixedSum: Uniformly Generating Random Values with a Fixed Sum Subject to Arbitrary Constraints}, author={Griffin, David and Davis, Robert I.}, booktitle={31st {IEEE} Real-Time and Embedded Technology and Applications Symposium}, year={2025}, }

Owner

Login: dgdguk
Kind: user

Repositories: 2
Profile: https://github.com/dgdguk

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Griffin"
  given-names: "David"
  orcid: "https://orcid.org/0000-0002-4077-0005"
- family-names: "Davis"
  given-names: "Robert I."
  orcid: "https://orcid.org/0000-0002-5772-0928"
title: "ConvolutionalFixedSum Software"
version: 0.0.1
doi: 10.5281/zenodo.15107012
date-released: 2025-03-29
url: "https://github.com/dgdguk/convolutionalfixedsum/"

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Average time to close issues: about 14 hours
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Packages

Total packages: 1
Total downloads:
- pypi 314 last-month

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 1
Total maintainers: 1

pypi.org: convolutionalfixedsum

Algorithm to generate uniformly sampled random vectors which sum to a given value, with constraints on each variate

Homepage: https://github.com/dgdguk/convolutionalfixedsum
Documentation: https://convolutionalfixedsum.readthedocs.io/
License: bsd-3-clause
Latest release: 1.0
published about 1 year ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 314 Last month

Rankings

Dependent packages count: 9.2%

Forks count: 31.5%

Average: 33.4%

Stargazers count: 41.4%

Dependent repos count: 51.7%

Maintainers (1)

dgdguk