




from collections import Counter

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

snake_case_ : Optional[Any]    		=       datasets.load_iris()

snake_case_ : str    		=       np.array(data["data"])
snake_case_ : Any    		=       np.array(data["target"])
snake_case_ : Optional[int]    		=       data["target_names"]

snake_case_		,       snake_case_		,       snake_case_		,       snake_case_ : int    		=       train_test_split(X, y)


def    A						(__A   :    Tuple   ,				__A   :    str       )   ->      Any:







     """simple docstring"""


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


def    A						(__A   :    str   ,				__A   :    Any   ,				__A   :    int   ,				__A   :    int   ,				__A   :    List[str]=5       )   ->      Any:







     """simple docstring"""


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












def    A						(__A   :    list   ,				__A   :    int   ,				__A   :    int = 0   ,				__A   :    int = 0       )   ->      int:







     """simple docstring"""


     UpperCAmelCase_          =							right or len(__A       ) - 1
     if left > right:
          return -1
     elif list_data[left] == key:
          return left
     elif list_data[right] == key:
          return right
     else:
          return search(__A   ,				__A   ,				left + 1   ,				right - 1       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()






