scalg 0.1

Analyse data file using a range based procentual proximity algorithm and calculate the linear maximum likelihood estimation.

Scoring-Algorithm

This is an algorithm which works based on a range based procentual proximity principle. Initially it was developed for a personal project, however later I found out it is a form of Newton's method used in statistics to solve maximum likelihood equations numerically.

scalg.score:

Args:
    source_data (list): Data set to process.
    weights (list): Weights corresponding to each column from the data set.
        0 if lower values have higher weight in the data set,
        1 if higher values have higher weight in the data set

Optional args:
    "score_lists" (str): Returns a list with lists of each column scores.
    "scores" (str): Returns only the final scores.

Raises:
    ValueError: Weights can only be either 0 or 1 (int)

Returns:
    list: Source data with the score of the set appended at as the last element.

scalg.score_columns:

Args:
    source_data (list): Data set to process.
    weights (list): Weights corresponding to each column from the data set.
        0 if lower values have higher weight in the data set,
        1 if higher values have higher weight in the data set
    columns (list): Indexes of the source_data columns to be scored.

Optional args:
    "score_lists" (str): Returns a list with lists of each column scores.
    "scores" (str): Returns only the final scores.

Raises:
    ValueError: Weights can only be either 0 or 1 (int)

Returns:
    list: Source data with the score of the set appended at as the last element.