


from collections import Counter

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

snake_case__			 =						datasets.load_iris()

snake_case__			 =						np.array(data['''data'''])
snake_case__			 =						np.array(data['''target'''])
snake_case__			 =						data['''target_names''']

snake_case__ ,snake_case__ ,snake_case__ ,snake_case__			 =						train_test_split(X, y)


def 				lowerCamelCase__	(      a	:   Any	,  a	:   List[str]				)    ->	Tuple:





			"""simple docstring"""
			return np.linalg.norm(np.array(a				) - np.array(a				)				)


def 				lowerCamelCase__	(      a	:   Tuple	,  a	:   List[str]	,  a	:   Any	,  a	:   List[Any]	,  a	:   List[Any]=5				)    ->	Union[str, Any]:





			"""simple docstring"""
			a__ :int						  =    zip(a	,  a				)
			# List of distances of all points from the point to be classified
			a__ :Tuple						  =    []
			for data_point in data:
						a__ :Any						  =    euclidean_distance(data_point[0]	,  a				)
						distances.append((distance, data_point[1])				)
			# Choosing 'k' points with the least distances.
			a__ :Dict						  =    [i[1] for i in sorted(a				)[:k]]
			# Most commonly occurring class among them
			# is the class into which the point is classified
			a__ :Union[str, Any]						  =    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]))






class 					lowerCAmelCase_ :




						def __init__(	self						:   Optional[int]						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :Tuple						  =    {}




						def  _snake_case					(	self						:   Tuple						)      ->None:






									"""simple docstring"""






									print(self.vertex						)
									for i in self.vertex:
												print(__A   ,     " -> "   ,     " -> ".join([str(__A						) for j in self.vertex[i]]						)						)




						def  _snake_case					(	self						:   Optional[int]   ,     __A						:   int   ,     __A						:   int						)      ->None:






									"""simple docstring"""






									if from_vertex in self.vertex:
												self.vertex[from_vertex].append(__A						)
									else:
												# else make a new vertex
												a__ :Any						  =    [to_vertex]




						def  _snake_case					(	self						:   Optional[int]						)      ->None:






									"""simple docstring"""






									a__ :List[Any]						  =    [False] * len(self.vertex						)

									# call the recursive helper function
									for i in range(len(self.vertex						)						):
												if not visited[i]:
															self.dfs_recursive(__A   ,     __A						)






						def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   int   ,     __A						:   list						)      ->None:






									"""simple docstring"""






									a__ :Dict						  =    True

									print(__A   ,     end=" "						)

									# Recur for all the vertices that are adjacent to this node
									for i in self.vertex:
												if not visited[i]:
															self.dfs_recursive(__A   ,     __A						)


if __name__ == "__main__":
		snake_case__			 =						Graph()
		g.add_edge(0, 1)
		g.add_edge(0, 2)
		g.add_edge(1, 2)
		g.add_edge(2, 0)
		g.add_edge(2, 3)
		g.add_edge(3, 3)

		g.print_graph()
		print('''DFS:''')
		g.dfs()

		# OUTPUT:
		# 0  ->  1 -> 2
		# 1  ->  2
		# 2  ->  0 -> 3
		# 3  ->  3
		# DFS:
		#  0 1 2 3


