[General]
mode = train
# Specify the absoulute path of training, validation and testing files
train_files = ./data/chengdu/train.npy,./data/porto/train.npy
val_files = ./data/chengdu/val.npy,./data/porto/val.npy
test_files = ./data/chengdu/test.npy,./data/porto/test.npy
# Specify the batch size
batch_size = 32
# Specify the number for GPU
gpu = 7
# Specify the unique label for each experiment
prefix = tte_exp_64_gru

[Model]
# Specify the inner learning rate
learning_rate = 1e-2
# Specify the inner reduce rate of learning rate
lr_reduce = 0.5
# Specify the maximum iteration
epoch = 500000
# Specify the k shot
inner_k = 10
# Specify the outer step size
outer_step_size = 0.1
# Specify the model according to the class name
model = MSMTTEGRUAttModel
# Specify the dataset according to the class name
dataset = MyDifferDatasetWithEmbedding
# Specify the dataloader according to the class name
dataloader = MyDataLoaderWithEmbedding


# mean, standard deviation for latitudes, longitudes and travel time (Chengdu is before the comma while Porto is after the comma)
[Statistics]
lat_means = 30.651168872309235,41.16060653954797
lng_means = 104.06000501543934,-8.61946359614912
lat_stds = 0.039222931811691585,0.02315827641949562
lng_stds = 0.045337940910596744,0.029208656457667292
labels_means = 1088.0075248390972,691.2889878452086
labels_stds = 1315.707363003298,347.4765869900725