Comments (4)
aiyacharley
commented on May 11, 2024
hope the author can update as soon as possible
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tkonopka
commented on May 11, 2024
Thanks for reaching out and sorry for late reply to this.
The original umap-learn has evolved with several new capabilities and it is not realistic that I can implement all here soon. (It is not clear that it would be desirable to duplicate, anyway.) Is there a feature in particular that you are looking for? You can consider checking out the R package uwot. Or, if your intention is to use the python package umap-learn, consider calling it directly using reticulate.
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tkonopka
commented on May 11, 2024
got around to some edits related to this. Running umap(some_data, method="umap-learn") should now run with umap-learn v0.5.
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tkonopka
commented on May 11, 2024
Now addressed in release "CRAN v0.2.8.0"
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Related Issues (20)
- predict() on a umap object with n_components=1 gets two errors -- Looks like missing drop=F HOT 2
- Failed creating initial embedding; using random embedding instead HOT 3
- Intel MKL FATAL ERROR HOT 3
- Differences with Python version? HOT 4
- Sparse Matrix support HOT 4
- is there any spark version implementations? HOT 2
- missing value where TRUE/FALSE needed HOT 3
- Problem with using custom metric HOT 2
- umap() produces matrix instead of S3 object HOT 2
- method = "python" does not work HOT 1
- when random_state is set automatically in config, it is not sufficient for reproducibility HOT 1
- Citing the package HOT 1
- Type error in optimize_embedding HOT 3
- transforming new data to an embedding HOT 3
- Error with n_components=1 HOT 3
- Number of threads HOT 3
- Allow for supervised/semi-supervised dimension reduction with labels HOT 1
- min_dist not updating with Python backend HOT 3
- predict() generates different predictions if called with multiple points at once versus called with each point individually HOT 7
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