algorithm/Python
1
0
Fork 0
mirror of https://github.com/TheAlgorithms/Python.git synced 2025-07-05 01:09:40 +08:00
Code Issues Packages Projects Releases Wiki Activity

Fix sphinx/build_docs warnings for other (#12482)

Browse Source 
* Fix sphinx/build_docs warnings for other

* Fix

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Fix

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Maxim Smolskiy
2024-12-29 23:31:53 +03:00
committed by GitHub
parent ce036db213
commit 3622e940c9
3 changed files with 77 additions and 63 deletions
Download Patch File Download Diff File Expand all files Collapse all files

30
other/scoring_algorithm.py
View File

@ -1,25 +1,26 @@
"""
developed by: markmelnic
original repo: https://github.com/markmelnic/Scoring-Algorithm
| developed by: markmelnic
| original repo: https://github.com/markmelnic/Scoring-Algorithm
Analyse data using a range based percentual proximity algorithm
and calculate the linear maximum likelihood estimation.
The basic principle is that all values supplied will be broken
down to a range from 0 to 1 and each column's score will be added
down to a range from ``0`` to ``1`` and each column's score will be added
up to get the total score.
==========
Example for data of vehicles
price|mileage|registration_year
20k |60k |2012
22k |50k |2011
23k |90k |2015
16k |210k |2010
::
price|mileage|registration_year
20k |60k |2012
22k |50k |2011
23k |90k |2015
16k |210k |2010
We want the vehicle with the lowest price,
lowest mileage but newest registration year.
Thus the weights for each column are as follows:
[0, 0, 1]
``[0, 0, 1]``
"""
@ -97,10 +98,11 @@ def procentual_proximity(
source_data: list[list[float]], weights: list[int]
) -> list[list[float]]:
"""
weights - int list
possible values - 0 / 1
0 if lower values have higher weight in the data set
1 if higher values have higher weight in the data set
| `weights` - ``int`` list
| possible values - ``0`` / ``1``
* ``0`` if lower values have higher weight in the data set
* ``1`` if higher values have higher weight in the data set
>>> procentual_proximity([[20, 60, 2012],[23, 90, 2015],[22, 50, 2011]], [0, 0, 1])
[[20, 60, 2012, 2.0], [23, 90, 2015, 1.0], [22, 50, 2011, 1.3333333333333335]]