
for beta in 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
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/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 fourierformer --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 fourierformer --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 fourierformer --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



# 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 fourierformer --name Heartbeat --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed 666 &

# 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 fourierformer --name Heartbeat --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed 1608 &

# 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 fourierformer --name Heartbeat --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed 2010 &



# 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 fourierformer --name Handwriting --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 666 &

# 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 fourierformer --name Handwriting --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 1608 &

# 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 fourierformer --name Handwriting --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed 2010 &



# wait 
# echo "Continue"



# CUDA_VISIBLE_DEVICES=1 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 fourierformer --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 0.6 &

# CUDA_VISIBLE_DEVICES=1 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 fourierformer --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 0.6 &

# CUDA_VISIBLE_DEVICES=1 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 fourierformer --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 0.6 &

# wait 
# echo "Continue"

# 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 fourierformer --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 & 

# 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 fourierformer --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 &

# CUDA_VISIBLE_DEVICES=1 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 fourierformer --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 &

# wait 
# echo "Continue"

# CUDA_VISIBLE_DEVICES=1 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 fourierformer --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 &

# 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 fourierformer --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 &

# 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 fourierformer --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 

# wait 
# echo "Continue"

# 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/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 fourierformer --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 0.6 &

# 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/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 fourierformer --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 0.6 &

# 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/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 fourierformer --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 0.6 &



# 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/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 fourierformer --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 0.6 &

# 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/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 fourierformer --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 0.6 &

# 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/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 fourierformer --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 0.6 




# # # EthanolConcentration
# # 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_ts \
# #     --data_class tsra --pattern TRAIN --val_pattern TEST \
# #     --epochs 400 --lr 0.001 --optimizer RAdam \
# #     --pos_encoding learnable  --task classification \
# #     --key_metric accuracy --model "fishformer" \
# #     --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 \
# #     --n_global_heads 4 --mode "gfish"


# #!/bin/bash
# se=2010
# # EthanolConcentration
# 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_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model fishformer --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 $se --n_global_heads 4 --mode "gfish" & 

# # Handwriting
# CUDA_VISIBLE_DEVICES=1 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_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model fishformer --name Handwriting --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed $se --n_global_heads 4 --mode "gfish" &

# #Heartbeat
# 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_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model fishformer --name Heartbeat --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed $se --n_global_heads 4 --mode "gfish" &

# wait 
# echo "Continue"

# # JapaneseVowels
# 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/JapaneseVowels --output_dir /tanData/tam/output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model fishformer --name JapaneseVowels --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed $se --n_global_heads 4 --mode "gfish" &

# #PEMS-SF
# CUDA_VISIBLE_DEVICES=1 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir /tanData/tam/Multivariate_ts/PEMS-SF --output_dir /tanData/tam/output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model fishformer --name PEMS-SF --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 128 --dim_feedforward 512 --seed $se --n_global_heads 6 --mode "gfish" &

# #SelfRegulationSCP1
# 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_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model fishformer --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 $se --n_global_heads 4 --mode "gfish" &

# wait 
# echo "Continue"

# # SelfRegulationSCP2
# 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/SelfRegulationSCP2 --output_dir /tanData/tam/output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model fishformer --name SelfRegulationSCP2 --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --subsample_factor 2 --seed $se --n_global_heads 4 --mode "gfish" &


# # SpokenArabicDigits
# CUDA_VISIBLE_DEVICES=1 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_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model fishformer --name SpokenArabicDigits --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed $se --n_global_heads 4 --mode "gfish" & 

# #UWaveGestureLibrary
# 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/UWaveGestureLibrary --output_dir /tanData/tam/output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model fishformer --name UWaveGestureLibrary --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 16 --d_model 256 --dim_feedforward 256 --seed $se --n_global_heads 8 --mode "gfish" & 
 

# wait 
# echo "Continue"

# #FaceDetection
# 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/FaceDetection --output_dir /tanData/tam/output_ts --data_class tsra --pattern TRAIN --val_pattern TEST --epochs 400 --lr 0.001 --optimizer RAdam --pos_encoding learnable  --task classification  --key_metric accuracy --model fishformer --name FaceDetection --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed $se --n_global_heads 4 --mode "gfish" &