




'''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							:       int						=			datasets.load_iris()

_SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			np.array(data["data"])
_SCREAMING_SNAKE_CASE							:       Dict						=			np.array(data["target"])
_SCREAMING_SNAKE_CASE							:       List[Any]						=			data["target_names"]

_SCREAMING_SNAKE_CASE							:       int						=			train_test_split(X, y)


def 					UpperCamelCase_(     snake_case							:	Tuple							,    snake_case							:	Tuple					):

							'''simple docstring'''






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


def 					UpperCamelCase_(     snake_case							:	int							,    snake_case							:	List[str]							,    snake_case							:	Tuple							,    snake_case							:	Tuple							,    snake_case							:	List[str]=5					):

							'''simple docstring'''






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










'''simple docstring'''





def 					UpperCamelCase_(     snake_case							:	int = 1_0_0_0					):

							'''simple docstring'''






							snake_case_      =				3
							snake_case_      =				0
							while a < n:
														if a % 3 == 0 or a % 5 == 0:
																					result += a
														elif a % 1_5 == 0:
																					result -= a
														a += 1
							return result


if __name__ == "__main__":
		print(F"{solution() = }")




