export CUDA_VISIBLE_DEVICES=0

model_name=DC_PatchTST



# DC_PatchTST specific parameters
d_model_stage0=64        # Stage 0 dimension (D0)
depth_enc0=1             # Stage 0 Mamba2 encoder depth  
depth_enc1=1             # Stage 1 Mamba2 encoder depth
target_ratio0=0.25       # Target compression ratio for stage 0
target_ratio1=0.25        # Target compression ratio for stage 1

# EthanolConcentration dataset
python -u run.py \
  --task_name classification \
  --is_training 1 \
  --root_path ./dataset/EthanolConcentration/ \
  --model_id EthanolConcentration \
  --model $model_name \
  --data UEA \
  --e_layers 3 \
  --batch_size 8 \
  --d_model 128 \
  --d_ff 256 \
  --n_heads 8 \
  --dropout 0.1 \
  --activation gelu \
  --des 'DC_PatchTST_Exp' \
  --itr 1 \
  --learning_rate 0.0002 \
  --train_epochs 100 \
  --patience 10 \
  --d_model_stage0 $d_model_stage0 \
  --depth_enc0 $depth_enc0 \
  --depth_enc1 $depth_enc1 \
  --target_ratio0 $target_ratio0 \
  --target_ratio1 $target_ratio1

# FaceDetection dataset  
python -u run.py \
  --task_name classification \
  --is_training 1 \
  --root_path ./dataset/FaceDetection/ \
  --model_id FaceDetection \
  --model $model_name \
  --data UEA \
  --e_layers 3 \
  --batch_size 8 \
  --d_model 128 \
  --d_ff 256 \
  --n_heads 8 \
  --dropout 0.1 \
  --activation gelu \
  --des 'DC_PatchTST_Exp' \
  --itr 1 \
  --learning_rate 0.001 \
  --train_epochs 100 \
  --patience 10 \
  --d_model_stage0 $d_model_stage0 \
  --depth_enc0 $depth_enc0 \
  --depth_enc1 $depth_enc1 \
  --target_ratio0 $target_ratio0 \
  --target_ratio1 $target_ratio1

# Handwriting dataset
python -u run.py \
  --task_name classification \
  --is_training 1 \
  --root_path ./dataset/Handwriting/ \
  --model_id Handwriting \
  --model $model_name \
  --data UEA \
  --e_layers 3 \
  --batch_size 8 \
  --d_model 128 \
  --d_ff 256 \
  --n_heads 8 \
  --dropout 0.1 \
  --activation gelu \
  --des 'DC_PatchTST_Exp' \
  --itr 1 \
  --learning_rate 0.001 \
  --train_epochs 100 \
  --patience 10 \
  --d_model_stage0 $d_model_stage0 \
  --depth_enc0 $depth_enc0 \
  --depth_enc1 $depth_enc1 \
  --target_ratio0 $target_ratio0 \
  --target_ratio1 $target_ratio1

# Heartbeat dataset
python -u run.py \
  --task_name classification \
  --is_training 1 \
  --root_path ./dataset/Heartbeat/ \
  --model_id Heartbeat \
  --model $model_name \
  --data UEA \
  --e_layers 3 \
  --batch_size 8 \
  --d_model 128 \
  --d_ff 256 \
  --n_heads 8 \
  --dropout 0.1 \
  --activation gelu \
  --des 'DC_PatchTST_Exp' \
  --itr 1 \
  --learning_rate 0.001 \
  --train_epochs 100 \
  --patience 10 \
  --d_model_stage0 $d_model_stage0 \
  --depth_enc0 $depth_enc0 \
  --depth_enc1 $depth_enc1 \
  --target_ratio0 $target_ratio0 \
  --target_ratio1 $target_ratio1

# JapaneseVowels dataset
python -u run.py \
  --task_name classification \
  --is_training 1 \
  --root_path ./dataset/JapaneseVowels/ \
  --model_id JapaneseVowels \
  --model $model_name \
  --data UEA \
  --e_layers 3 \
  --batch_size 8 \
  --d_model 128 \
  --d_ff 256 \
  --n_heads 8 \
  --dropout 0.1 \
  --activation gelu \
  --des 'DC_PatchTST_Exp' \
  --itr 1 \
  --learning_rate 0.001 \
  --train_epochs 100 \
  --patience 10 \
  --d_model_stage0 $d_model_stage0 \
  --depth_enc0 $depth_enc0 \
  --depth_enc1 $depth_enc1 \
  --target_ratio0 $target_ratio0 \
  --target_ratio1 $target_ratio1