other.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 up to get the total score.

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]

Functions

calculate_each_score(→ list[list[float]])
generate_final_scores(→ list[float])
get_data(→ list[list[float]])
procentual_proximity(→ list[list[float]])	weights - int list

Module Contents

other.scoring_algorithm.calculate_each_score(data_lists: list[list[float]], weights: list[int]) → list[list[float]]

>>> calculate_each_score([[20, 23, 22], [60, 90, 50], [2012, 2015, 2011]],
...                      [0, 0, 1])
[[1.0, 0.0, 0.33333333333333337], [0.75, 0.0, 1.0], [0.25, 1.0, 0.0]]

other.scoring_algorithm.generate_final_scores(score_lists: list[list[float]]) → list[float]

>>> generate_final_scores([[1.0, 0.0, 0.33333333333333337],
...                        [0.75, 0.0, 1.0],
...                        [0.25, 1.0, 0.0]])
[2.0, 1.0, 1.3333333333333335]

other.scoring_algorithm.get_data(source_data: list[list[float]]) → list[list[float]]

>>> get_data([[20, 60, 2012],[23, 90, 2015],[22, 50, 2011]])
[[20.0, 23.0, 22.0], [60.0, 90.0, 50.0], [2012.0, 2015.0, 2011.0]]

other.scoring_algorithm.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]]