1.3 KiB
1.3 KiB
Hashing N-dimensional float vectors
Search nearest neighbour vectors in n-dimensional space with hashes. There are no dependencies in this package.
Each vestor is discretized into a set of hashes, as described here (also as PDF).
How to use
- Provided a float vector []float64, use
CubeSetandCentralCubefunctions to generate hypercube coordinates []int. The difference between the two functions is that one corresponds to hash-table record and the other to a query or vice versa, depending on performance/memory preference. HashSetandDecimalHash/FNV1aHashare used to get corresponding hash set and central hash from the hypercube coordinates above. There are 2 alternative hash functions: DecimalHash and FNV1aHash. DecimalHash does not have collisions, but is not suitable for cases with large number of buckets or dimensions. FNV1aHash is applicable for all cases.
Example for similar image search/clustering.
Go doc for full code documentation.