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How to predict a new sample (1 sample) on existing model? #316
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Do you mean prediction with a trained model? |
What does "1 sample" mean? |
I'm using ACFM model for traffic flow prediction task. And I trained it with "NYCTAXI201401-201403_GRID" dataset. I saw the length of 1 input sequence is 6 and the output is 1. How can I predict with another new sequence? I don't know how to preprocessing new raw data to model's input and which code to call and predict this new sequence. |
Modify the parameters |
This is all that is needed to make predictions with the trained model, no data processing is required. |
I keep the input_window and output_window like you. |
This requires processing the data into atomic files before libcity can be used. |
Assume I had atomic files, which code can I use ? |
You can run the model directly with |
test_model.py only takes a batch data for testing, it can't achieve your requirement |
Hello, I have a similar problem. |
In this case, a pre-trained model is needed first. You can use other datasets to train the model in Libcity and generate a |
Thank you for your response. Is the implementation steps as follows. Suppose I have finished training DMVST with NYCTaxi20150103 dataset, then when I want to predict future data, e.g., taxi demand after 2015/3/1. I need to modify NYCTaxi20150103.grid to add the data after 2015/03/01, and set departing_volume and arriving_volume to 0. Finally, use run_model to adjust the test_rate to get the predicted value after multiple steps. |
Yes, after you have trained DMVSTNET, just set |
Hi, thank for your contribution you have made.
I wonder if i could use trained model to predict on a new sample ? Can you give me instructions or code demo?
( Now I'm using ACFM model for traffic flow prediction task. )
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