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