



'''simple docstring'''



def 	_A							( _lowerCAmelCase       ):



							"""simple docstring"""



							__lowercase													=[]
							for data in source_data:
														for i, el in enumerate(_lowerCAmelCase       ):
																					if len(_lowerCAmelCase       ) < i + 1:
																												data_lists.append([]       )
																					data_lists[i].append(float(_lowerCAmelCase       )       )
							return data_lists



def 	_A							( _lowerCAmelCase	, _lowerCAmelCase       ):



							"""simple docstring"""



							__lowercase													=[]
							for dlist, weight in zip(_lowerCAmelCase	, _lowerCAmelCase       ):
														__lowercase													=min(_lowerCAmelCase       )
														__lowercase													=max(_lowerCAmelCase       )

														__lowercase													=[]
														# for weight 0 score is 1 - actual score
														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       )

        # weight not 0 or 1
														else:
																					__lowercase													=f"""Invalid weight of {weight:f} provided"""
																					raise ValueError(_lowerCAmelCase       )

														score_lists.append(_lowerCAmelCase       )

							return score_lists



def 	_A							( _lowerCAmelCase       ):



							"""simple docstring"""



							__lowercase													=[0 for i in range(len(score_lists[0]       )       )]

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

							return final_scores



def 	_A							( _lowerCAmelCase	, _lowerCAmelCase       ):



							"""simple docstring"""



							__lowercase													=get_data(_lowerCAmelCase       )
							__lowercase													=calculate_each_score(_lowerCAmelCase	, _lowerCAmelCase       )
							__lowercase													=generate_final_scores(_lowerCAmelCase       )

							# append scores to source data
							for i, ele in enumerate(_lowerCAmelCase       ):
														source_data[i].append(_lowerCAmelCase       )

							return source_data





"""simple docstring"""

from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

SCREAMING_SNAKE_CASE_            =    datasets.load_iris()

SCREAMING_SNAKE_CASE_            =    np.array(data['''data'''])
SCREAMING_SNAKE_CASE_            =    np.array(data['''target'''])
SCREAMING_SNAKE_CASE_            =    data['''target_names''']

SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_            =    train_test_split(X, y)




def      lowercase__    (       lowerCAmelCase					:       Optional[int]			,					lowerCAmelCase					:       Any			)			->   Optional[Any]:






    """simple docstring"""




    return np.linalg.norm(np.array(lowerCAmelCase			) - np.array(lowerCAmelCase			)			)




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Any			,					lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Tuple			,					lowerCAmelCase					:       Dict=5			)			->   str:






    """simple docstring"""




    UpperCAmelCase          =       zip(lowerCAmelCase			,					lowerCAmelCase			)
    # List of distances of all points from the point to be classified
    UpperCAmelCase          =       []
    for data_point in data:
        UpperCAmelCase          =       euclidean_distance(data_point[0]			,					lowerCAmelCase			)
        distances.append((distance, data_point[1])			)
    # Choosing 'k' points with the least distances.
    UpperCAmelCase          =       [i[1] for i in sorted(lowerCAmelCase			)[:k]]
    # Most commonly occurring class among them
    # is the class into which the point is classified
    UpperCAmelCase          =       Counter(lowerCAmelCase			).most_common(1			)[0][0]
    return classes[result]


if __name__ == "__main__":
   print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



