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Many LSH implementations use Jaccard similarity to return matching result above a certain threshold say 80% match.
Is possible to implement the same in this library.
The text was updated successfully, but these errors were encountered:
Same thoughts.
And I think zipping arrays into lower dimensions and using smaller input_dim may help, as smaller dimensions increases the probability of collision, and therefore similar vectors are more likely fall into a same slot.
Same thoughts.
And I think zipping arrays into lower dimensions and using smaller input_dim may help, as smaller dimensions increases the probability of collision, and therefore similar vectors are more likely fall into a same slot.
Many LSH implementations use Jaccard similarity to return matching result above a certain threshold say 80% match. Is possible to implement the same in this library.
How to use threshold with this LSH implementation can you help me I have problem with this issue
Many LSH implementations use Jaccard similarity to return matching result above a certain threshold say 80% match.
Is possible to implement the same in this library.
The text was updated successfully, but these errors were encountered: