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Procentual proximity scoring algorithm implemented #2280

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Merged
merged 3 commits into from
Aug 4, 2020
Merged

Procentual proximity scoring algorithm implemented #2280

merged 3 commits into from
Aug 4, 2020

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markmelnic
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@markmelnic markmelnic commented Aug 4, 2020

  • Add an algorithm?

This is an algorithm which works based on a range based percentual proximity scoring principle. Initially I developed it for a personal project, however later I found out it is a form of Newton's method used in statistics to solve maximum likelihood equations numerically.

Changes from last pull request:

  • code pythonization
  • multiple improvements
  • passed doctest
  • passed flake8 test

Checklist:

  • I have read CONTRIBUTING.md.
  • This pull request is all my own work -- I have not plagiarized.
  • I know that pull requests will not be merged if they fail the automated tests.
  • This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
  • All new Python files are placed inside an existing directory.
  • All filenames are in all lowercase characters with no spaces or dashes.
  • All functions and variable names follow Python naming conventions.
  • All function parameters and return values are annotated with Python type hints.
  • All functions have doctests that pass the automated testing.
  • All new algorithms have a URL in its comments that points to Wikipedia or other similar explanation.

- added requested changes
- passed doctest

- passed flake8 test
@markmelnic markmelnic requested a review from cclauss August 4, 2020 20:07
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Thanks for doing this!

@cclauss cclauss merged commit a891f68 into TheAlgorithms:master Aug 4, 2020
stokhos pushed a commit to stokhos/Python that referenced this pull request Jan 3, 2021
* Procentual proximity scoring algorithm implemented

- added requested changes
- passed doctest

- passed flake8 test

* Apply suggestions from code review

Co-authored-by: Christian Clauss <[email protected]>

* Function rename

Co-authored-by: Christian Clauss <[email protected]>
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2 participants