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more api / examples plz ! #5
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Hi @IncubatorShokuhou,
Thus, first of all, you have to find the way how to read your data to 3D numpy array, and then create a Scaler which fits your data the best (by default you can use our RY_Scaler, or try MinMaxScaler from the sklearn library). As for the choice of different models we recommend you to try all of them on your data :-) For Germany, the Dense model works the best (see our paper for particular evaluation results). At the moment, we focus on our paper revision and have no plans for an additional tutorial. If data you use is open, you can send us the link, and we will try our best to help you with preprocessing. Best, Our paper: |
I already created an example to ilustrate how to use this library. Nevertheless, don't let be fooled by the example. I say this because data corresponds a light drizzly, so it's normal obtain bad results starting from this data. After all, I hope this example serve to you to fammiliarize with Example: |
Hi Elmer, thanks for yout example!! Thanks you Giovanni |
Results are not saved, model.run() returns an array-like that contains the predicted data. About the input file, it's available in the same repository. If you want to run the code, just change the file direction. |
Thanks you @ElmerJeanpierreLopez !! #import Dense
#enter the right path
There are no error, but what do I get? Thank |
Hello again @GiovanniVignoli95 |
hi Georgy:
I feel so excited on your Rainymotion project. I believe that it will give me great help in the field of nowcasting.
However, I find it a little bit difficult in preparing suitable input data (as I am a raw hand of python and hdf5 format). Also, I have no idea in choosing the proper functions (i.e. SparseSD or Sparse?) in different conditions.
Could you give a little bit more introduction in the following update of the upcoming tutorial(if there is a upcoming one)?
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