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.
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]
Functions¶
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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 -
intlistpossible values -0/10if lower values have higher weight in the data set1if 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]]