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Search nearest neighbour vectors in n-dimensional space with hashes. There are no dependencies in this package.
The algorithm is based on the assumption that two real numbers can be considered equal within certain equality distance. Therefore quantization is applicable for comparison. To make sure points near or at quantization borders are also comparable, a vector can be discretized into more than one hash, as described [here](https://vitali-fedulov.github.io/similar.pictures/algorithm-for-hashing-high-dimensional-float-vectors.html) (also as [PDF](https://github.com/vitali-fedulov/research/blob/main/Algorithm%20for%20hashing%20float%20vectors.pdf)).
The algorithm is based on the assumption that two real numbers can be considered equal within certain equality distance. Then quantization is used for comparison. To make sure points near or at quantization borders are also comparable, a vector can be discretized into more than one hash, as described [here](https://vitali-fedulov.github.io/similar.pictures/algorithm-for-hashing-high-dimensional-float-vectors.html) (also as [PDF](https://github.com/vitali-fedulov/research/blob/main/Algorithm%20for%20hashing%20float%20vectors.pdf)). The method indirectly clusters given vectors by hypercubes.