


from collections import Counter

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

SCREAMING_SNAKE_CASE :   Tuple							      =				datasets.load_iris()

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

SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				train_test_split(X, y)



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Any:
    return np.linalg.norm(np.array(lowerCamelCase_				) - np.array(lowerCamelCase_				)				)



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_=5				)					->		Tuple:
    _lowercase :							List[Any]   	=	zip(lowerCamelCase_							,				lowerCamelCase_				)
    # List of distances of all points from the point to be classified
    _lowercase :							List[str]   	=	[]
    for data_point in data:
        _lowercase :							int   	=	euclidean_distance(data_point[0]							,				lowerCamelCase_				)
        distances.append((distance, data_point[1])				)
    # Choosing 'k' points with the least distances.
    _lowercase :							Any   	=	[i[1] for i in sorted(lowerCamelCase_				)[:k]]
    # Most commonly occurring class among them
    # is the class into which the point is classified
    _lowercase :							Optional[int]   	=	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]))






def        UpperCamelCase_(   lowerCamelCase_				)					->		int:
    assert (
        isinstance(lowerCamelCase_							,				lowerCamelCase_				) and number_of_steps > 0
    ), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
    if number_of_steps == 1:
        return 1
    _lowercase	,       _lowercase :							Dict   	=	1, 1
    for _ in range(number_of_steps - 1				):
        _lowercase	,       _lowercase :							Tuple   	=	current + previous, current
    return current


if __name__ == "__main__":
 import doctest

 doctest.testmod()


