class kddNShot:
def __init__(self, path, batch_size):
self.data_generator = Data(path, batch_size)
self.norm_adj = self.data_generator.get_adj_mat()
def next(self):
return self.data_generator.sample()
def get_test(self):
return self.data_generator.sample('test')
data_generator = Data(path=args.data_path + args.dataset,
batch_size=args.batch_size)
USR_NUM, ITEM_NUM = data_generator.n_users, data_generator.n_items
N_TRAIN, N_TEST = data_generator.n_train, data_generator.n_test
BATCH_SIZE = args.batch_size
config = dict()
config['n_users'] = data_generator.n_users
config['n_items'] = data_generator.n_items
print(data_generator.train_items[1])
"""
*********************************************************
Generate the Laplacian matrix, where each entry defines the decay factor (e.g., p_ui) between two connected nodes.
"""
plain_adj, norm_adj, mean_adj, pre_adj = data_generator.get_adj_mat()
print("plain_adj")
print(plain_adj)
print("norm_adj")
print(norm_adj)
print("mean_adj")
print(mean_adj)
print("pre_adj")
print(pre_adj)
if args.adj_type == 'plain':
config['norm_adj'] = plain_adj
print('use the plain adjacency matrix')
elif args.adj_type == 'norm':
config['norm_adj'] = norm_adj
print('use the normalized adjacency matrix')
elif args.adj_type == 'gcmc':
