Comments (5)
I found out, that the graph has 1664 edges. That's close to the amount of edges in the METR-LA dataset with 207 instead of 228 nodes. So the weight matrix should be okay.
@Felijasha Hi, I have the same question. the adjacent matrix only has 30+ values greater than 0, means the graph only has 30+ edges, which is too little for 228 nodes. what do you mean by 'the graph has 1664 edges' ?
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@Felijasha Could you please elaborate on this ? I was wondering the same thing
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I found out, that the graph has 1664 edges. That's close to the amount of edges in the METR-LA dataset with 207 instead of 228 nodes. So the weight matrix should be okay.
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@Felijasha In case you're still up-to-date in this, may I ask you about the significance of the lines below ? (lines 71 and 72) in trainer.py, especially when it comes to the significance of the three values 4e1,1e5,1e5 in the inference process. Thanks in advance :)
step_idx = tmp_idx = np.arange(3, n_pred + 1, 3) - 1
min_val = min_va_val = np.array([4e1, 1e5, 1e5] * len(step_idx))
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Sorry in this issue I can't really help you.
I'm writing actual my bachelor thesis and I'm using the STGCN framework for my model in the thesis. I use another implementation as fundamental. I will have a look at it in the following days. Maybe I have an idea.
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Related Issues (20)
- Settings for n_his and Kt
- About first_approx() HOT 4
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- Predict diference data HOT 1
- the sensor id of V_228 HOT 1
- adjacency matrix computation and its heatmap HOT 4
- how to make train dataset HOT 1
- what purpose of this model HOT 1
- How to convert Trajectory datasets into adjacency matrix? HOT 1
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- Way of resuming training with checkpoint
- Hello, can you send me the PEMS07(L) dataset? HOT 1
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- Station Info missing for larger dataset
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