def dataloader_all_sensors_seq2seq(setting):
train, eval, test = dataloader(setting['dataset'])
scaler = utils.Scaler(train)
return dataiter_all_sensors_seq2seq(train, scaler, setting), \
dataiter_all_sensors_seq2seq(eval, scaler, setting, shuffle=False), \
dataiter_all_sensors_seq2seq(test, scaler, setting, shuffle=False), \
scaler
def dataloader_all_sensors_seq2seq(setting):
train, eval, test = dataloader(setting['dataset']) #[T, N, D]
scaler = utils.Scaler(train)
return dataiter_all_sensors_seq2seq(train, scaler, setting), \
dataiter_all_sensors_seq2seq(eval, scaler, setting, shuffle=False, offset=train.shape[0]), \
dataiter_all_sensors_seq2seq(test, scaler, setting, shuffle=False, offset=train.shape[0]+eval.shape[0]), \
scaler
def dataloader_flow(settings):
settings = settings['training']
train, eval, test = load_flow()
scaler = utils.Scaler()
scaler.fit(train)
flow_train = create_dataset_flow(
train,
scaler,
sampler_type='random',
batch_size=settings['flow_batch_size'],
iterations_per_epoch=settings['iterations_per_epoch'])
flow_eval = create_dataset_flow(eval,
scaler,
sampler_type='batch',
batch_size=settings['flow_batch_size'])
flow_test = create_dataset_flow(test,
scaler,
sampler_type='batch',
batch_size=settings['flow_batch_size'])
return flow_train, flow_eval, flow_test, scaler