# # # ####### HEARTBEAT

# for beta in 0. 0.15 0.25 0.35 0.450.55 0.65 0.75 0.85 0.95 1.05 
# # for beta in 0.
# do
#     CUDA_VISIBLE_DEVICES=2 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/Heartbeat --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name Heartbeat --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed 666 --weight_k $beta &

#     CUDA_VISIBLE_DEVICES=2 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/Heartbeat --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name Heartbeat --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed 1608 --weight_k $beta &

#     CUDA_VISIBLE_DEVICES=2 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/Heartbeat --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name Heartbeat --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed 2010 --weight_k $beta &


#     CUDA_VISIBLE_DEVICES=3 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/Handwriting --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name Handwriting --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 666 --weight_k $beta &

#     CUDA_VISIBLE_DEVICES=3 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/Handwriting --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name Handwriting --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 1608 --weight_k $beta &

#     CUDA_VISIBLE_DEVICES=3 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/Handwriting --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name Handwriting --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 2010 --weight_k $beta &
#     wait
#     echo "continue"
# done



for beta in 0.6
do
  CUDA_VISIBLE_DEVICES=2 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/JapaneseVowels --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name JapaneseVowels --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 2010 --weight_k $beta & 

  CUDA_VISIBLE_DEVICES=3 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/JapaneseVowels --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name JapaneseVowels --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 1608 --weight_k $beta & 

  CUDA_VISIBLE_DEVICES=2 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/JapaneseVowels --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name JapaneseVowels --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 666 --weight_k $beta &
  
    wait
    echo "continue"
done

# # wait 
# # echo "Continue"

# for beta in 0. 0.15 0.25 0.35 0.450.55 0.65 0.75 0.85 0.95 1.05 
# # for beta in 0.
# do
#     CUDA_VISIBLE_DEVICES=2 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/SelfRegulationSCP1 --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed 666 --weight_k $beta & 

#     CUDA_VISIBLE_DEVICES=3 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/SelfRegulationSCP1 --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed 1608 --weight_k $beta &

#     CUDA_VISIBLE_DEVICES=0 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/SelfRegulationSCP1 --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name SelfRegulationSCP1 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed 2010 --weight_k $beta &
#     wait 
#     echo "Continue"
# done


# for beta in 0. 0.15 0.25 0.35 0.450.55 0.65 0.75 0.85 0.95 1.05 
# # for beta in 0.
# do
#   CUDA_VISIBLE_DEVICES=0 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/EthanolConcentration --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name EthanolConcentration --dropout 0.1 --activation gelu --batch_size 128 --subsample_factor 2 --num_layers 1 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed 2010 --weight_k $beta &

#   CUDA_VISIBLE_DEVICES=2 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/EthanolConcentration --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name EthanolConcentration --dropout 0.1 --activation gelu --batch_size 128 --subsample_factor 2 --num_layers 1 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed 1608 --weight_k $beta &

#   CUDA_VISIBLE_DEVICES=3 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/EthanolConcentration --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name EthanolConcentration --dropout 0.1 --activation gelu --batch_size 128 --subsample_factor 2 --num_layers 1 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed 666 --weight_k $beta & 
#     wait 
#     echo "Continue"
# done


for beta in 0.6
do
    CUDA_VISIBLE_DEVICES=3 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/FaceDetection --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name FaceDetection --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 2010 --weight_k $beta &

    CUDA_VISIBLE_DEVICES=3 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/FaceDetection --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name FaceDetection --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 666 --weight_k $beta &

    CUDA_VISIBLE_DEVICES=3 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/FaceDetection --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name FaceDetection --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 1608 --weight_k $beta &

#     wait
#     echo "continue"
# done


# for beta in 0.6
# do
    CUDA_VISIBLE_DEVICES=2 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/SpokenArabicDigits --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name SpokenArabicDigits --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed 666 --weight_k $beta &

    CUDA_VISIBLE_DEVICES=2 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/SpokenArabicDigits --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name SpokenArabicDigits --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed 2010 --weight_k $beta &

    CUDA_VISIBLE_DEVICES=0 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/SpokenArabicDigits --output_dir /tanData/tam/output_tst --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model linearformer --name SpokenArabicDigits --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed 1608 --weight_k $beta &

    wait
    echo "continue"
done







