### This is an example training of the task Handwriting

####SOFTMAX
python main.py --output_dir output_tst --comment "classification" --name UWaveGestureLibrary_fromScratch \
--records_file Classification_records.xls --data_dir data/Multivariate_ts/UWaveGestureLibrary --data_class tsra \
--pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  \
--task classification --key_metric accuracy --model softmax 

##### ATTENTION-BN
python main.py --output_dir output_tst --comment "classification" --name UWaveGestureLibrary_fromScratch \
--records_file Classification_records.xls --data_dir data/Multivariate_ts/UWaveGestureLibrary --data_class tsra \
--pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  \
--task classification --key_metric accuracy --model softmax --beta 1.


##### ATTENTION-SH
python main.py --output_dir output_tst --comment "classification" --name UWaveGestureLibrary_fromScratch \
--records_file Classification_records.xls --data_dir data/Multivariate_ts/UWaveGestureLibrary --data_class tsra \
--pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  \
--task classification --key_metric accuracy --model shformer
 

#####ATTENTION-BN+SH
python main.py --output_dir output_tst --comment "classification" --name UWaveGestureLibrary_fromScratch \
--records_file Classification_records.xls --data_dir data/Multivariate_ts/UWaveGestureLibrary --data_class tsra \
--pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  \
--task classification --key_metric accuracy --model shformer --beta 1.




## LINEAR BASELINE AND LINEAR VERSIONS OF OUR METHOD
#### LINEAR
python main.py --output_dir output_tst --comment "classification" --name UWaveGestureLibrary_fromScratch \
--records_file Classification_records.xls --data_dir data/Multivariate_ts/UWaveGestureLibrary --data_class tsra \
--pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  \
--task classification --key_metric accuracy --model linearformer --beta 1.


##### ATTENTION-BN
python main.py --output_dir output_tst --comment "classification" --name UWaveGestureLibrary_fromScratch \
--records_file Classification_records.xls --data_dir data/Multivariate_ts/UWaveGestureLibrary --data_class tsra \
--pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  \
--task classification --key_metric accuracy --model linearformer --beta 1.


##### ATTENTION-SH
python main.py --output_dir output_tst --comment "classification" --name UWaveGestureLibrary_fromScratch \
--records_file Classification_records.xls --data_dir data/Multivariate_ts/UWaveGestureLibrary --data_class tsra \
--pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  \
--task classification --key_metric accuracy --model shlinformer 


#####ATTENTION-BN+SH
python main.py --output_dir output_tst --comment "classification" --name UWaveGestureLibrary_fromScratch \
--records_file Classification_records.xls --data_dir data/Multivariate_ts/UWaveGestureLibrary --data_class tsra \
--pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  \
--task classification --key_metric accuracy --model shlinformer --beta 1.

















######### linear
for be in 0. 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95 1.05 1.2
do
    CUDA_VISIBLE_DEVICES=2 python main.py --output_dir /tanData/tam/output_tst --comment "classification for mrlinformer" --name UWaveGestureLibrary_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/UWaveGestureLibrary --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  --task classification --key_metric accuracy --seed 6 --model linearformer --beta $be  &

    CUDA_VISIBLE_DEVICES=2 python main.py --output_dir /tanData/tam/output_tst --comment "classification for mrlinformer" --name UWaveGestureLibrary_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/UWaveGestureLibrary --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  --task classification --key_metric accuracy --seed 666 --model linearformer --beta $be & 

    CUDA_VISIBLE_DEVICES=2 python main.py --output_dir /tanData/tam/output_tst --comment "classification for mrlinformer" --name UWaveGestureLibrary_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/UWaveGestureLibrary --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  --task classification --key_metric accuracy --seed 1608 --model linearformer --beta $be  &



    CUDA_VISIBLE_DEVICES=3 python main.py --output_dir /tanData/tam/output_tst --comment "classification for mrlinformer" --name PEMS-SF_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/PEMS-SF --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  --task classification  --key_metric accuracy --seed 6 --model linearformer --beta $be  &

    CUDA_VISIBLE_DEVICES=3 python main.py --output_dir /tanData/tam/output_tst --comment "classification for mrlinformer" --name PEMS-SF_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/PEMS-SF --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  --task classification  --key_metric accuracy --seed 666 --model linearformer --beta $be  &

    CUDA_VISIBLE_DEVICES=3 python main.py --output_dir /tanData/tam/output_tst --comment "classification for mrlinformer" --name PEMS-SF_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/PEMS-SF --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  --task classification  --key_metric accuracy --seed 1608 --model linearformer --beta $be  &
    wait
    echo "Done"
done 
 

 
for be in 0. 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95 1.05 1.2
do
CUDA_VISIBLE_DEVICES=0 python main.py --output_dir /tanData/tam/output_tst --comment "classification for mrlinformer" --name SelfRegulationSCP2_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/SelfRegulationSCP2 --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  --task classification  --key_metric accuracy --seed 6 --model linearformer --beta $be  &

CUDA_VISIBLE_DEVICES=3 python main.py --output_dir /tanData/tam/output_tst --comment "classification for mrlinformer" --name SelfRegulationSCP2_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/SelfRegulationSCP2 --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  --task classification  --key_metric accuracy --seed 666 --model linearformer --beta $be  &

CUDA_VISIBLE_DEVICES=2 python main.py --output_dir /tanData/tam/output_tst --comment "classification for mrlinformer" --name SelfRegulationSCP2_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/SelfRegulationSCP2 --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  --task classification  --key_metric accuracy --seed 1608 --model linearformer --beta $be &
    wait
    echo "Done"
done


#### MRLINEAR
for be in 0. 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95 1.05 1.2
do
    CUDA_VISIBLE_DEVICES=2 python main.py --output_dir /tanData/tam/output_tst --comment "classification for mrlinformer" --name UWaveGestureLibrary_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/UWaveGestureLibrary --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  --task classification --key_metric accuracy --seed 6 --model mrlinformer --beta $be --mode 'norm_first' &

    CUDA_VISIBLE_DEVICES=2 python main.py --output_dir /tanData/tam/output_tst --comment "classification for mrlinformer" --name UWaveGestureLibrary_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/UWaveGestureLibrary --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  --task classification --key_metric accuracy --seed 666 --model mrlinformer --beta $be --mode 'norm_first' & 

    CUDA_VISIBLE_DEVICES=2 python main.py --output_dir /tanData/tam/output_tst --comment "classification for mrlinformer" --name UWaveGestureLibrary_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/UWaveGestureLibrary --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  --task classification --key_metric accuracy --seed 1608 --model mrlinformer --beta $be --mode 'norm_first' &
    wait
    echo "Done"
done


for be in 0. 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95 1.05 1.2
do
    CUDA_VISIBLE_DEVICES=3 python main.py --output_dir /tanData/tam/output_tst --comment "classification for mrlinformer" --name PEMS-SF_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/PEMS-SF --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  --task classification  --key_metric accuracy --seed 6 --model mrlinformer --beta $be  --mode 'norm_first' &

    CUDA_VISIBLE_DEVICES=3 python main.py --output_dir /tanData/tam/output_tst --comment "classification for mrlinformer" --name PEMS-SF_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/PEMS-SF --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  --task classification  --key_metric accuracy --seed 666 --model mrlinformer --beta $be --mode 'norm_first' &

    CUDA_VISIBLE_DEVICES=3 python main.py --output_dir /tanData/tam/output_tst --comment "classification for mrlinformer" --name PEMS-SF_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/PEMS-SF --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  --task classification  --key_metric accuracy --seed 1608 --model mrlinformer --beta $be --mode 'norm_first' &
    wait
    echo "Done"
done 
 
 
for be in 0. 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95 1.05 1.2
do
CUDA_VISIBLE_DEVICES=0 python main.py --output_dir /tanData/tam/output_tst --comment "classification for mrlinformer" --name SelfRegulationSCP2_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/SelfRegulationSCP2 --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  --task classification  --key_metric accuracy --seed 6 --model mrlinformer --beta $be --mode 'norm_first' &

CUDA_VISIBLE_DEVICES=3 python main.py --output_dir /tanData/tam/output_tst --comment "classification for mrlinformer" --name SelfRegulationSCP2_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/SelfRegulationSCP2 --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  --task classification  --key_metric accuracy --seed 666 --model mrlinformer --beta $be --mode 'norm_first' &

CUDA_VISIBLE_DEVICES=2 python main.py --output_dir /tanData/tam/output_tst --comment "classification for mrlinformer" --name SelfRegulationSCP2_fromScratch --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/SelfRegulationSCP2 --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 100 --lr 0.001 --batch_size 16 --optimizer RAdam  --pos_encoding learnable  --task classification  --key_metric accuracy --seed 1608 --model mrlinformer --beta $be --mode 'norm_first' &
    wait
    echo "Done"
done