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2d5dd6f132
* MAINT: Used f-string method Updated the code with f-string methods wherever required for a better and cleaner understanding of the code. * Updated files with f-string method * Update rsa_key_generator.py * Update rsa_key_generator.py * Update elgamal_key_generator.py * Update lru_cache.py I don't think this change is efficient but it might tackle the error as the error was due to using long character lines. * Update lru_cache.py * Update lru_cache.py Co-authored-by: cyai <seriesscar@gmail.com> Co-authored-by: Christian Clauss <cclauss@me.com>
89 lines
2.6 KiB
Python
89 lines
2.6 KiB
Python
"""
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developed by: markmelnic
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original repo: https://github.com/markmelnic/Scoring-Algorithm
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Analyse data using a range based percentual proximity algorithm
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and calculate the linear maximum likelihood estimation.
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The basic principle is that all values supplied will be broken
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down to a range from 0 to 1 and each column's score will be added
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up to get the total score.
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==========
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Example for data of vehicles
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price|mileage|registration_year
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20k |60k |2012
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22k |50k |2011
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23k |90k |2015
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16k |210k |2010
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We want the vehicle with the lowest price,
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lowest mileage but newest registration year.
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Thus the weights for each column are as follows:
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[0, 0, 1]
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"""
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def procentual_proximity(
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source_data: list[list[float]], weights: list[int]
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) -> list[list[float]]:
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"""
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weights - int list
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possible values - 0 / 1
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0 if lower values have higher weight in the data set
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1 if higher values have higher weight in the data set
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>>> procentual_proximity([[20, 60, 2012],[23, 90, 2015],[22, 50, 2011]], [0, 0, 1])
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[[20, 60, 2012, 2.0], [23, 90, 2015, 1.0], [22, 50, 2011, 1.3333333333333335]]
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"""
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# getting data
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data_lists: list[list[float]] = []
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for data in source_data:
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for i, el in enumerate(data):
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if len(data_lists) < i + 1:
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data_lists.append([])
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data_lists[i].append(float(el))
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score_lists: list[list[float]] = []
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# calculating each score
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for dlist, weight in zip(data_lists, weights):
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mind = min(dlist)
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maxd = max(dlist)
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score: list[float] = []
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# for weight 0 score is 1 - actual score
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if weight == 0:
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for item in dlist:
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try:
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score.append(1 - ((item - mind) / (maxd - mind)))
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except ZeroDivisionError:
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score.append(1)
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elif weight == 1:
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for item in dlist:
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try:
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score.append((item - mind) / (maxd - mind))
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except ZeroDivisionError:
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score.append(0)
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# weight not 0 or 1
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else:
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raise ValueError(f"Invalid weight of {weight:f} provided")
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score_lists.append(score)
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# initialize final scores
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final_scores: list[float] = [0 for i in range(len(score_lists[0]))]
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# generate final scores
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for i, slist in enumerate(score_lists):
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for j, ele in enumerate(slist):
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final_scores[j] = final_scores[j] + ele
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# append scores to source data
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for i, ele in enumerate(final_scores):
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source_data[i].append(ele)
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return source_data
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