"""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 				lowerCAmelCase__      (     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			Tuple   )   ->			Optional[int]:





						"""simple docstring"""






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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[Any]	,     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			int=5   )   ->			List[str]:





						"""simple docstring"""






						snake_case        =     zip(a__	,     a__   )
						# 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]	,     a__   )
												distances.append((distance, data_point[1])   )
						# Choosing 'k' points with the least distances.
						snake_case        =     [i[1] for i in sorted(a__   )[:k]]
						# Most commonly occurring class among them
						# is the class into which the point is classified
						snake_case        =     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]))



"""simple docstring"""
from __future__ import annotations

from typing import Any



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				pass





class 					lowerCAmelCase_      :



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     data
										snake_case        =     None

				def __iter__(			self      ):






										"""simple docstring"""

										snake_case        =     self
										snake_case        =     []
										while node:
																if node in visited:
																						raise ContainsLoopError
																visited.append(lowerCAmelCase      )
																yield node.data
																snake_case        =     node.next_node

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										try:
																list(self      )
																return False
										except ContainsLoopError:
																return True


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					Node(1)
					SCREAMING_SNAKE_CASE__   =					Node(2)
					SCREAMING_SNAKE_CASE__   =					Node(3)
					SCREAMING_SNAKE_CASE__   =					Node(4)
					print(root_node.has_loop)  # False
					SCREAMING_SNAKE_CASE__   =					root_node.next_node
					print(root_node.has_loop)  # True

					SCREAMING_SNAKE_CASE__   =					Node(5)
					SCREAMING_SNAKE_CASE__   =					Node(6)
					SCREAMING_SNAKE_CASE__   =					Node(5)
					SCREAMING_SNAKE_CASE__   =					Node(6)
					print(root_node.has_loop)  # False

					SCREAMING_SNAKE_CASE__   =					Node(1)
					print(root_node.has_loop)  # False


