



from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

_lowerCAmelCase	:   List[str]    =   TypeVar("T")


class 	__snake_case   ( Generic[T] ):
		def __init__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  data
						lowerCAmelCase__		  =  None



		def __str__(							self						):







						"""simple docstring"""
						return f'{self.data}'



class 	__snake_case   ( Generic[T] ):
		def __init__(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  None
		def __iter__(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.top
						while node:
										yield node.data
										lowerCAmelCase__		  =  node.next
		def __str__(							self						):







						"""simple docstring"""
						return "->".join([str(a_						) for item in self]						)
		def __len__(							self						):







						"""simple docstring"""
						return len(tuple(iter(self						)						)						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.top is None
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  Node(a_						)
						if not self.is_empty():
										lowerCAmelCase__		  =  self.top
						lowerCAmelCase__		  =  node
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						if self.is_empty():
										raise IndexError('pop from empty stack'						)
						assert isinstance(self.top    ,a_						)
						lowerCAmelCase__		  =  self.top
						lowerCAmelCase__		  =  self.top.next
						return pop_node.data
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						if self.is_empty():
										raise IndexError('peek from empty stack'						)

						assert self.top is not None
						return self.top.data



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  None


if __name__ == "__main__":
	from doctest import testmod

	testmod()





from collections import Counter

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

_lowerCAmelCase	:   int    =   datasets.load_iris()

_lowerCAmelCase	:   Tuple    =   np.array(data["data"])
_lowerCAmelCase	:   Any    =   np.array(data["target"])
_lowerCAmelCase	:   Union[str, Any]    =   data["target_names"]

_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase	:   Optional[Any]    =   train_test_split(X, y)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Tuple:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__=5					)	->     Dict:



				"""simple docstring"""





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