Comments (3)
That should work; I'll see if I can look into it.
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Many thanks, as the ability to use different/custom metrics is why I selected the pynndescent package in the first place.
Also, I cannot pass the additional 'w' metric into a custom metric using metric_kwds={'w' : w.to_dict()} as I expected. Could you provide an example of how to do that as well for a custom function.
Much appreciated.
David
from pynndescent.
Due to how things have to get handled by numba, in practice you need to pass all the required arguments in the correct order to the distance function. That means for weighted minkowski you'll have to pass w
and p
(if you just want to use p
you cna always use minkowski
instead). To pass w
you'll need to pass a vector of length giving the weight for each coordinate.
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Related Issues (20)
- `make_dense_tree()` with `angular=True` can segfault on poorly-behaved datasets HOT 1
- access distances in heap HOT 3
- Sample identifiers for semantic search HOT 2
- uint8 as internal data HOT 1
- Cosine metric - error "Negative values in data passed to precomputed distance matrix" HOT 2
- Question about covariance matrix used when using Mahalanobis distance
- Tests fail: E SystemError: initialization of _internal failed without raising an exception
- Newest version breaks with UMAP HOT 3
- Slice error using mac M1-max ARM HOT 6
- Exceedingly large amount of memory usage
- Very high memory usage HOT 7
- Specifying threshold in distance metrics
- API to save and load index from disk
- Minor bias in split selection? HOT 1
- true_angular is not a distance?
- TSSS missing a factor of 2
- Reverse diversification is actually forward diversification again
- pynndescent might break with next numba release HOT 3
- How to navigate pyinstaller HOT 1
- Querying the training set: runtime tradeoff for large k HOT 2
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