Open tommylees112 opened 5 years ago
Maybe we can have some sort of 'is dimension all nan/inf' and then just drop that coordinate from the axes:
So we would select x[:, :-1, :-1]
from our x
values if the final dimension of the 2nd and 3rd axis were all inf
i'm sure this is a relatively easy one liner in the src/models/data.py
file? Maybe i'm wrong. Where else are we checking for nans and dropping them?
In the final month and for the final feature (VHI) the values are ALL
nan
(in theDataLoader
these are coded up as-inf
). This is because the feature (VHI) is hidden from the model and so we will have to do some extra preprocessing for this step to avoid errors in fitting the models.So for the linear model:
Creates the error: