


'''simple docstring'''






import math
class 						__UpperCamelCase	:





							def 		a__   (  self		:List[str]  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:int			):
									snake_case_		:							Dict			       =						0.0
									snake_case_		:							str			       =						0.0
									for i in range(len(UpperCAmelCase_			)			):
											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		:List[Any]  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:Any  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]			):
									for i in range(len(UpperCAmelCase_			)			):
											weights[j][i] += alpha * (sample[i] - weights[j][i])
									return weights





def      UpperCAmelCase      (		):







		'''simple docstring'''
		# Training Examples ( m, n )
		snake_case_		:							Optional[Any]			       =						[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

		# weight initialization ( n, C )
		snake_case_		:							Union[str, Any]			       =						[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

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

		for _ in range(_snake_case ):
				for j in range(len(_snake_case ) ):
						# training sample
						snake_case_		:							str			       =						training_samples[j]

						# Compute the winning vector
						snake_case_		:							List[str]			       =						self_organizing_map.get_winner(_snake_case				,      _snake_case )

						# Update the winning vector
						snake_case_		:							int			       =						self_organizing_map.update(_snake_case				,      _snake_case				,      _snake_case				,      _snake_case )

    # classify test sample
		snake_case_		:							int			       =						[0, 0, 0, 1]
		snake_case_		:							Optional[Any]			       =						self_organizing_map.get_winner(_snake_case				,      _snake_case )

		# 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()



'''simple docstring'''






def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :str				,      lowerCamelCase_  :str ):







		'''simple docstring'''
		# Return True if there is node that has not iterated.
		snake_case_		:							Optional[int]			       =						[False] * len(lowerCamelCase_ )
		snake_case_		:							Dict			       =						[]
		queue.append(lowerCamelCase_ )
		snake_case_		:							Tuple			       =						True

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

		return visited[t]





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :Dict				,      lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		# This array is filled by BFS and to store path
		snake_case_		:							Optional[int]			       =						[-1] * (len(lowerCamelCase_ ))
		snake_case_		:							Tuple			       =						0
		while bfs(lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_ ):
				snake_case_		:							int			       =						float("""Inf""" )
				snake_case_		:							Any			       =						sink

				while s != source:
						# Find the minimum value in select path
						snake_case_		:							Optional[Any]			       =						min(lowerCamelCase_				,      graph[parent[s]][s] )
						snake_case_		:							Union[str, Any]			       =						parent[s]

				max_flow += path_flow
				snake_case_		:							Dict			       =						sink

				while v != source:
						snake_case_		:							Optional[int]			       =						parent[v]
						graph[u][v] -= path_flow
						graph[v][u] += path_flow
						snake_case_		:							Any			       =						parent[v]
		return max_flow


__A	:	Union[str, Any]     =     [
    [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],
]

__A,   __A	:	Union[str, Any]     =     0, 5






print(ford_fulkerson(graph, source, sink))