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