Comments (1)
Hi, thanks for the suggestion! We haven't looked into speeding up the nearest neighbor computation.
For speeding up nndescent in particular, I would suggest moving this discussion to the pynndescent Github repository.
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Related Issues (20)
- Error when embedding new points HOT 2
- Could not find a `llvm-config` binary on Mac M1 HOT 3
- Adding or removing one sample results in absolutely different embeddings HOT 1
- Continuous color bar for categorical variables HOT 4
- SolverError after using Standardized constraint HOT 2
- Implement pymde in Scanpy HOT 3
- Quadratic initialization on GPU HOT 2
- [Question] Possible to invert embedding? HOT 1
- Updating embeddings Notebook - doesnt work HOT 3
- Incorrect conda command on installation page HOT 1
- Implement default embeddings
- Distortion functions on the embedded positions?
- Using pre-computed knn with preserve_neighbors HOT 5
- Conflict with sklearn.manifold.TSNE HOT 2
- scikit-learn compatible API HOT 4
- poetry installation "Unable to find installation candidates for nvidia-cudnn-cu11" HOT 6
- incompatibility with PyTorch 2.0? HOT 2
- advice on scaling
- Is there an easy way to use Standardized() constraint with target std not equal to 1? HOT 1
- Error while plotting graph HOT 1
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