Update baseline.sh
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		| @@ -113,7 +113,7 @@ class SuperAttention(SuperModule): | |||||||
|             .permute(0, 2, 1, 3) |             .permute(0, 2, 1, 3) | ||||||
|         ) |         ) | ||||||
|         attn_v1 = (q_v1 @ k_v1.transpose(-2, -1)) * math.sqrt(head_dim) |         attn_v1 = (q_v1 @ k_v1.transpose(-2, -1)) * math.sqrt(head_dim) | ||||||
|         attn_v1 = attn_v1.softmax(dim=-1) |         attn_v1 = attn_v1.softmax(dim=-1)  # B * #head * N * N | ||||||
|         attn_v1 = self.attn_drop(attn_v1) |         attn_v1 = self.attn_drop(attn_v1) | ||||||
|         feats_v1 = (attn_v1 @ v_v1).permute(0, 2, 1, 3).reshape(B, N, -1) |         feats_v1 = (attn_v1 @ v_v1).permute(0, 2, 1, 3).reshape(B, N, -1) | ||||||
|         if C == head_dim * num_head: |         if C == head_dim * num_head: | ||||||
|   | |||||||
| @@ -16,11 +16,10 @@ fi | |||||||
| gpu=$1 | gpu=$1 | ||||||
| market=$2 | market=$2 | ||||||
|  |  | ||||||
| algorithms="NAIVE-V1 NAIVE-V2 MLP GRU LSTM ALSTM XGBoost LightGBM SFM TabNet DoubleE" | # algorithms="NAIVE-V1 NAIVE-V2 MLP GRU LSTM ALSTM XGBoost LightGBM SFM TabNet DoubleE" | ||||||
|  | algorithms="MLP GRU LSTM ALSTM XGBoost LightGBM SFM TabNet DoubleE" | ||||||
|  |  | ||||||
| for alg in ${algorithms} | for alg in ${algorithms} | ||||||
| do | do | ||||||
|  |  | ||||||
|   python exps/trading/baselines.py --alg ${alg} --gpu ${gpu} --market ${market} |   python exps/trading/baselines.py --alg ${alg} --gpu ${gpu} --market ${market} | ||||||
|  |  | ||||||
| done | done | ||||||
|   | |||||||
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