developed by markmelnic original repo https github commarkmelnicscoringalgorithm 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 pricemileageregistrationyear 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 getdata20 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 calculateeachscore20 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 for weight 0 score is 1 actual score weight not 0 or 1 generatefinalscores1 0 0 0 0 33333333333333337 0 75 0 0 1 0 0 25 1 0 0 0 2 0 1 0 1 3333333333333335 initialize final scores 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 procentualproximity20 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 append scores to source data 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 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 for weight 0 score is 1 actual score weight not 0 or 1 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 initialize final scores 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 append scores to source data
def get_data(source_data: list[list[float]]) -> list[list[float]]:
    
    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))
    return data_lists


def calculate_each_score(
    data_lists: list[list[float]], weights: list[int]
) -> list[list[float]]:
    
    score_lists: list[list[float]] = []
    for dlist, weight in zip(data_lists, weights):
        mind = min(dlist)
        maxd = max(dlist)

        score: list[float] = []
        
        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)

        
        else:
            msg = f"Invalid weight of {weight:f} provided"
            raise ValueError(msg)

        score_lists.append(score)

    return score_lists


def generate_final_scores(score_lists: list[list[float]]) -> list[float]:
    
    
    final_scores: list[float] = [0 for i in range(len(score_lists[0]))]

    for slist in score_lists:
        for j, ele in enumerate(slist):
            final_scores[j] = final_scores[j] + ele

    return final_scores


def procentual_proximity(
    source_data: list[list[float]], weights: list[int]
) -> list[list[float]]:
    

    data_lists = get_data(source_data)
    score_lists = calculate_each_score(data_lists, weights)
    final_scores = generate_final_scores(score_lists)

    
    for i, ele in enumerate(final_scores):
        source_data[i].append(ele)

    return source_data