# Argument -- model
```
MrsFormer: mra_head; Softmax: transformer; MRA2: MRA2_full; MRA-s: MRA2_sparse; Linear Transformer: linear_transformer; MrsLinear Transformer: linear_transformer_mra_head; FMM transformer: fmm_transformer; MrsFMM transformer: fmm_transformer_mra_head; Linformer: linformer, MrsLinformer: linformer_mra_head; Performer: performer; MrsPerformer: performer_mra_head, LUNA: luna, MrsLUNA: luna_mra_head
```
# EthanolConcentration
```
CUDA_VISIBLE_DEVICES=5 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir Multivariate_ts/EthanolConcentration --output_dir 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 mra_head --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
```
# Handwriting
```
CUDA_VISIBLE_DEVICES=5 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir Multivariate_ts/Handwriting --output_dir 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 mra_head --name Handwriting --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed $se
```
# Heartbeat
```
CUDA_VISIBLE_DEVICES=5 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir Multivariate_ts/Heartbeat --output_dir 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 mra_head --name Heartbeat --dropout 0.1 --activation gelu --batch_size 128 --num_layers 1 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed $se
```
# JapaneseVowels
```
CUDA_VISIBLE_DEVICES=5 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir Multivariate_ts/JapaneseVowels --output_dir 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 mra_head --name JapaneseVowels --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed $se
```
# SelfRegulationSCP1
```
CUDA_VISIBLE_DEVICES=5 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir Multivariate_ts/SelfRegulationSCP1 --output_dir 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 mra_head --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
```
# SpokenArabicDigits
```
CUDA_VISIBLE_DEVICES=5 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir Multivariate_ts/SpokenArabicDigits --output_dir 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 mra_head --name SpokenArabicDigits --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 64 --dim_feedforward 256 --seed $se
```
# FaceDetection
```
CUDA_VISIBLE_DEVICES=5 python src/main.py --output_dir experiments --comment "classification from Scratch" --records_file Classification_records.xls --data_dir Multivariate_ts/FaceDetection --output_dir 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 mra_head --name FaceDetection --dropout 0.1 --activation gelu --batch_size 128 --num_layers 3 --num_heads 8 --d_model 128 --dim_feedforward 256 --seed $se
```