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if os.path.isdir("logs"):
shutil.rmtree("logs")
# configure("logs/logs_toy", flush_secs=1)
# clean saving directory
if not os.path.exists("saves"):
os.makedirs("saves")
# Generate Graph
# G = nx.karate_club_graph()
G = nx.LCF_graph(14, [5, -5], 7)
graphdataset = GraphDataset(G, shuffle_neighbour=False)
# run node2vec
for edge in G.edges():
G[edge[0]][edge[1]]['weight'] = 1
embedding = nv.node2vec_main(G, args)
print(embedding.shape)
embedding_size = embedding.shape[0] + 3
embedding = torch.from_numpy(embedding).float().cuda()
# normalize
embedding = embedding / torch.mean(torch.norm(embedding, 2, 1))
print(embedding)
##### parallel
mp.set_start_method('spawn')
decoder = DecoderRNN_step(input_size=input_size, hidden_size=64, embedding_size=embedding_size,
n_layers=n_layers, is_bidirection=False, embedding_init_flag=True, embedding_init=embedding).cuda()
configure("logs/logs_toy", flush_secs=1)
# clean saving directory
if not os.path.exists("saves"):
os.makedirs("saves")
# Generate Graph
G = nx.karate_club_graph()
# G = nx.LCF_graph(14,[5,-5],7)
# G = nx.LCF_graph(20,[-9,-9],10)
graphdataset = GraphDataset(G, shuffle_neighbour = True)
# run node2vec
for edge in G.edges():
G[edge[0]][edge[1]]['weight'] = 1
embedding = nv.node2vec_main(G, args)
# print(embedding)
print('embedding.shape', embedding.shape)
embedding_dist = np.zeros((embedding.shape[0],embedding.shape[0]))
G_adj = np.asarray(nx.to_numpy_matrix(G))
# print(np.std(G_adj,axis=0)/np.mean(G_adj,axis=0))
G_adj_sum = np.repeat(np.sum(G_adj, axis = 1, keepdims=True), G_adj.shape[0] ,axis=1)
alpha = 10
print('alpha', alpha)
for i in range(embedding_dist.shape[0]):
for j in range(embedding_dist.shape[1]):
if i!=j:
embedding_dist[i][j] = np.exp(embedding[i] @ embedding[j].transpose() * alpha)