other.scoring_algorithm
=======================

.. py:module:: other.scoring_algorithm

.. autoapi-nested-parse::

   | 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
---------

.. autoapisummary::

   other.scoring_algorithm.calculate_each_score
   other.scoring_algorithm.generate_final_scores
   other.scoring_algorithm.get_data
   other.scoring_algorithm.procentual_proximity


Module Contents
---------------

.. py:function:: 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]]


.. py:function:: 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]


.. py:function:: 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]]


.. py:function:: 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]]