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It would be nice to have an API similar to that of scikit-learn, e.g. to perform MVARICA:
model=scot.MVARICA()
model.fit(data)
We'd have to discuss the details though, such as where do we specify certain parameters (e.g. model order, dimensionality - in __init__ or fit). @mbillingr I would be happy if you had some comments on that.
The text was updated successfully, but these errors were encountered:
I agree with putting most parameters into __init__.
I think we definitely need a transform method. If this works like in scikit-learn, the transform method should return the transformed signals and thereby exposing the fitted components. Is this what you meant with your second question?
Regarding the workflows, I think we should develop this new API in parallel to the existing one(s) so that we don't break existing workflows. I'm not particularly concerned with online application, since this is not an explicit goal of SCoT. We should try to fit in as nicely as possible into the existing ecosystem, which is mostly NumPy, SciPy, and MNE in our case. Do we need to get more concrete before starting to work on this new API?
It would be nice to have an API similar to that of
scikit-learn
, e.g. to perform MVARICA:We'd have to discuss the details though, such as where do we specify certain parameters (e.g. model order, dimensionality - in
__init__
orfit
). @mbillingr I would be happy if you had some comments on that.The text was updated successfully, but these errors were encountered: