This is an implementation of BK-tree for golang. BK-tree is a tree data structure for similarity search in a metric space. Using BK-tree, you can search neighbors of a data from the metric space efficiently.
Search similar values from 1,000,000 data.
Data is 64 bits integer, and distance function is hamming distance.
(see bktree_test.go for detail)
BenchmarkSearch_ExactForLargeTree-2 1000000 3540 ns/op
BenchmarkSearch_Tolerance1ForLargeTree-2 30000 85227 ns/op
BenchmarkSearch_Tolerance2ForLargeTree-2 3000 907897 ns/op
BenchmarkSearch_Tolerance4ForLargeTree-2 200 17273811 ns/op
BenchmarkSearch_Tolerance8ForLargeTree-2 10 234244862 ns/op
BenchmarkSearch_Tolerance32ForLargeTree-2 3 722881937 ns/op
BenchmarkLinearSearchForLargeSet-2 10 281977270 ns/op
If the tolerance is small enough, BK-tree is much faster than naive linear search.
see _example/ direcotry.
$ go get github.com/agatan/bktree
MIT