






from collections import Counter

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

__A      =				datasets.load_iris()

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

__A ,  __A ,  __A ,  __A      =				train_test_split(X, y)




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







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




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase=5 ):

					'''simple docstring'''







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











def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= [0] * len(_lowercase )
					_A								= []
					_A								= []
					_A								= 0

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

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

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

					if cnt != len(_lowercase ):
										print('''Cycle exists''' )
					else:
										print(_lowercase )


# Adjacency List of Graph
__A      =				{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}


topological_sort(graph)



