


def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]						) ->   List[Any]:





	"""simple docstring"""







	A								=				[0] * len(UpperCamelCase__						)
	A								=				[]
	A								=				[]
	A								=				0

	for values in graph.values():
		for i in values:
			indegree[i] += 1

	for i in range(len(UpperCamelCase__						)						):
		if indegree[i] == 0:
			queue.append(UpperCamelCase__						)

	while queue:
		A								=				queue.pop(0						)
		cnt += 1
		topo.append(UpperCamelCase__						)
		for x in graph[vertex]:
			indegree[x] -= 1
			if indegree[x] == 0:
				queue.append(UpperCamelCase__						)

	if cnt != len(UpperCamelCase__						):
		print("""Cycle exists"""						)
	else:
		print(UpperCamelCase__						)


# Adjacency List of Graph
_lowercase       :      Tuple	=	{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)







from collections import Counter

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

_lowercase       :      Any	=	datasets.load_iris()

_lowercase       :      Tuple	=	np.array(data["data"])
_lowercase       :      int	=	np.array(data["target"])
_lowercase       :      int	=	data["target_names"]

_lowercase	,							_lowercase	,							_lowercase	,							_lowercase       :      Any	=	train_test_split(X, y)





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Any						) ->   List[Any]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     int							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     int=5						) ->   Tuple:





	"""simple docstring"""







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



