
UpperCamelCase_						:     str	       =							[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]



def 		UpperCamelCase     (       _UpperCAmelCase  :   Union[str, Any] ,			_UpperCAmelCase  :   Optional[Any] ,			_UpperCAmelCase  :   List[str] ,			_UpperCAmelCase  :   List[Any]  )   ->			Any:






						'''simple docstring'''

						_lowercase       :	List[str]           =   [False] * len(_UpperCAmelCase  )
						_lowercase       :	int           =   [s]
						_lowercase       :	Dict           =   True

						while queue:
												_lowercase       :	int           =   queue.pop(0  )
												for ind in range(len(graph[u]  )  ):
																		if visited[ind] is False and graph[u][ind] > 0:
																								queue.append(_UpperCAmelCase  )
																								_lowercase       :	int           =   True
																								_lowercase       :	Union[str, Any]           =   u

						return visited[t]



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[str] ,			_UpperCAmelCase  :   int ,			_UpperCAmelCase  :   Tuple  )   ->			Optional[int]:






						'''simple docstring'''

						_lowercase       :	List[Any]           =   [-1] * (len(_UpperCAmelCase  ))
						_lowercase       :	int           =   0
						_lowercase       :	str           =   []
						_lowercase       :	Union[str, Any]           =   [i[:] for i in graph]  # Record original cut, copy.
						while bfs(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  ):
												_lowercase       :	Union[str, Any]           =   float("Inf"  )
												_lowercase       :	List[Any]           =   sink

												while s != source:
																		# Find the minimum value in select path
																		_lowercase       :	str           =   min(_UpperCAmelCase ,			graph[parent[s]][s]  )
																		_lowercase       :	str           =   parent[s]

												max_flow += path_flow
												_lowercase       :	Optional[Any]           =   sink

												while v != source:
																		_lowercase       :	Dict           =   parent[v]
																		graph[u][v] -= path_flow
																		graph[v][u] += path_flow
																		_lowercase       :	Union[str, Any]           =   parent[v]

						for i in range(len(_UpperCAmelCase  )  ):
												for j in range(len(graph[0]  )  ):
																		if graph[i][j] == 0 and temp[i][j] > 0:
																								res.append((i, j)  )

						return res


if __name__ == "__main__":
							print(mincut(test_graph, source=0, sink=5))






import math




class   __lowercase						:






	def       _a(self				: Dict      ,	snake_case				: list[list[float]]      ,	snake_case				: list[int]    ) ->			int:
							_lowercase       :	List[Any]           =   0.0
							_lowercase       :	Any           =   0.0
							for i in range(len(snake_case    )    ):
													da += math.pow((sample[i] - weights[0][i])      ,	2    )
													da += math.pow((sample[i] - weights[1][i])      ,	2    )
													return 0 if da > da else 1
							return 0






	def       _a(self				: Dict      ,	snake_case				: list[list[int | float]]      ,	snake_case				: list[int]      ,	snake_case				: int      ,	snake_case				: float    ) ->			list[list[int | float]]:
							for i in range(len(snake_case    )    ):
													weights[j][i] += alpha * (sample[i] - weights[j][i])
							return weights



def 		UpperCamelCase     (       )   ->			None:






						'''simple docstring'''

						_lowercase       :	Union[str, Any]           =   [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

						# weight initialization ( n, C )
						_lowercase       :	Tuple           =   [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

						# training
						_lowercase       :	List[Any]           =   SelfOrganizingMap()
						_lowercase       :	Any           =   3
						_lowercase       :	Optional[Any]           =   0.5

						for _ in range(_UpperCAmelCase  ):
												for j in range(len(_UpperCAmelCase  )  ):
																		# training sample
																		_lowercase       :	Optional[int]           =   training_samples[j]

																		# Compute the winning vector
																		_lowercase       :	Tuple           =   self_organizing_map.get_winner(_UpperCAmelCase ,			_UpperCAmelCase  )

																		# Update the winning vector
																		_lowercase       :	str           =   self_organizing_map.update(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )

    # classify test sample
						_lowercase       :	Any           =   [0, 0, 0, 1]
						_lowercase       :	Dict           =   self_organizing_map.get_winner(_UpperCAmelCase ,			_UpperCAmelCase  )

						# results
						print(f"""Clusters that the test sample belongs to : {winner}"""  )
						print(f"""Weights that have been trained : {weights}"""  )


# running the main() function
if __name__ == "__main__":
							main()




