Comments (2)
Agree with you @slevang , that'd be nice. Currently, the rank is only computed when we fit the Decomposer
xeofs/xeofs/models/decomposer.py
Lines 72 to 74 in a3cb204
The first opportunity to compute the rank is probably already within the Stacker of the Preprocessor
. since we then know the final size of the matrix.
I don't know what you think about the default value - Personally, I mostly prefer to have a fast rather than exact decomposition. If we were going for n_modes="all"
as a default, it would always trigger the full SVD decomposition.
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Yep, agree with all that. Fine to leave the default alone, and I was thinking around the same place in the preprocessors to check the rank. Just a question then of how to propagate the information back to the rest of the class, haven't looked at the details yet.
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Related Issues (20)
- ModuleNotFound Error datatree HOT 5
- `scores()` don't match `transform()` with dask data HOT 4
- Improving release process
- Keep the documentation in sync with the code HOT 3
- Add dependencies section in the documentation HOT 1
- numpy and pandas dependency question
- numpy and pandas dependencies HOT 3
- Migrating repository to xarray-contrib HOT 2
- why the explained_variance_ratio of CCA so small HOT 4
- Serialization fails with `xarray>=2024.1.0` HOT 2
- Single mode data reconstruction fails with normalized scores HOT 9
- Support for complex input data
- MCA incorrect coords alignment in transform method HOT 3
- Is the reconstruction of unseen data possible using EOF? HOT 5
- Model cannot fit DataArray without coordiantes
- Trouble creating the eof object HOT 8
- TypeError: __init__() missing 1 required positional argument: 'X' HOT 1
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- Questions to the REOF power parameter in the examples HOT 4
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