





"""simple docstring"""
import math






class 					_UpperCAmelCase   :


					def 	lowerCamelCase       (					self			:Union[str, Any]     ,       __UpperCamelCase			:list[list[float]]     ,       __UpperCamelCase			:list[int]		):
												A		=  0.0
												A		=  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 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:list[list[int | float]]     ,       __UpperCamelCase			:list[int]     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:float		):
												for i in range(len(__UpperCamelCase		)		):
																			weights[j][i] += alpha * (sample[i] - weights[j][i])
												return weights







def 					A__				(			):
							# Training Examples ( m, n )
							A		=  [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

							# training
							A		=  SelfOrganizingMap()
							A		=  3
							A		=  0.5

							for _ in range(UpperCamelCase       ):
														for j in range(len(UpperCamelCase       )       ):
																					# training sample
																					A		=  training_samples[j]

																					# Compute the winning vector
																					A		=  self_organizing_map.get_winner(UpperCamelCase					,  UpperCamelCase       )

																					# Update the winning vector
																					A		=  self_organizing_map.update(UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       )

    # classify test sample
							A		=  [0, 0, 0, 1]
							A		=  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()









"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def 					A__				(			UpperCamelCase       ):
							A		=  [False] * len(UpperCamelCase       )
							A		=  [-1] * len(UpperCamelCase       )

							def dfs(UpperCamelCase					,  UpperCamelCase       ):
														A		=  True
														A		=  c
														for u in graph[v]:
																					if not visited[u]:
																												dfs(UpperCamelCase					,  1 - c       )

							for i in range(len(UpperCamelCase       )       ):
														if not visited[i]:
																					dfs(UpperCamelCase					,  0       )

							for i in range(len(UpperCamelCase       )       ):
														for j in graph[i]:
																					if color[i] == color[j]:
																												return False

							return True


# Adjacency list of graph
_snake_case     :		str       						= {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))


