mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-11-23 21:11:08 +00:00
4d0c830d2c
* ci(pre-commit): Add ``flake8-builtins`` additional dependency to ``pre-commit`` (#7104) * refactor: Fix ``flake8-builtins`` (#7104) * fix(lru_cache): Fix naming conventions in docstrings (#7104) * ci(pre-commit): Order additional dependencies alphabetically (#7104) * fix(lfu_cache): Correct function name in docstring (#7104) * Update strings/snake_case_to_camel_pascal_case.py Co-authored-by: Christian Clauss <cclauss@me.com> * Update data_structures/stacks/next_greater_element.py Co-authored-by: Christian Clauss <cclauss@me.com> * Update digital_image_processing/index_calculation.py Co-authored-by: Christian Clauss <cclauss@me.com> * Update graphs/prim.py Co-authored-by: Christian Clauss <cclauss@me.com> * Update hashes/djb2.py Co-authored-by: Christian Clauss <cclauss@me.com> * refactor: Rename `_builtin` to `builtin_` ( #7104) * fix: Rename all instances (#7104) * refactor: Update variable names (#7104) * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * ci: Create ``tox.ini`` and ignore ``A003`` (#7123) * revert: Remove function name changes (#7104) * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Rename tox.ini to .flake8 * Update data_structures/heap/heap.py Co-authored-by: Dhruv Manilawala <dhruvmanila@gmail.com> * refactor: Rename `next_` to `next_item` (#7104) * ci(pre-commit): Add `flake8` plugin `flake8-bugbear` (#7127) * refactor: Follow `flake8-bugbear` plugin (#7127) * fix: Correct `knapsack` code (#7127) * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Co-authored-by: Christian Clauss <cclauss@me.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Dhruv Manilawala <dhruvmanila@gmail.com>
89 lines
2.6 KiB
Python
89 lines
2.6 KiB
Python
"""
|
|
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
|
|
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
|
|
|
|
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]
|
|
"""
|
|
|
|
|
|
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
|
|
|
|
>>> 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]]
|
|
"""
|
|
|
|
# getting data
|
|
data_lists: list[list[float]] = []
|
|
for data in source_data:
|
|
for i, el in enumerate(data):
|
|
if len(data_lists) < i + 1:
|
|
data_lists.append([])
|
|
data_lists[i].append(float(el))
|
|
|
|
score_lists: list[list[float]] = []
|
|
# calculating each score
|
|
for dlist, weight in zip(data_lists, weights):
|
|
mind = min(dlist)
|
|
maxd = max(dlist)
|
|
|
|
score: list[float] = []
|
|
# for weight 0 score is 1 - actual score
|
|
if weight == 0:
|
|
for item in dlist:
|
|
try:
|
|
score.append(1 - ((item - mind) / (maxd - mind)))
|
|
except ZeroDivisionError:
|
|
score.append(1)
|
|
|
|
elif weight == 1:
|
|
for item in dlist:
|
|
try:
|
|
score.append((item - mind) / (maxd - mind))
|
|
except ZeroDivisionError:
|
|
score.append(0)
|
|
|
|
# weight not 0 or 1
|
|
else:
|
|
raise ValueError(f"Invalid weight of {weight:f} provided")
|
|
|
|
score_lists.append(score)
|
|
|
|
# initialize final scores
|
|
final_scores: list[float] = [0 for i in range(len(score_lists[0]))]
|
|
|
|
# generate final scores
|
|
for slist in score_lists:
|
|
for j, ele in enumerate(slist):
|
|
final_scores[j] = final_scores[j] + ele
|
|
|
|
# append scores to source data
|
|
for i, ele in enumerate(final_scores):
|
|
source_data[i].append(ele)
|
|
|
|
return source_data
|