annoy gann

gann (go-approximate-nearest-neighbor) is a library for approximate nearest neighbor search purely written in golang.

The implemented algorithm is truly inspired by Annoy (https://github.com/spotify/annoy).

feature

purely written in Go: no dependencies out of Go world.
easy to tune with a bit of parameters

installation

go get github.com/mathetake/gann

parameters

setup phase parameters

name	type	description	run-time complexity	space complexity	accuracy
dim	int	dimension of target vectors	the larger, the more expensive	the larger, the more expensive	N/A
nTree	int	# of trees	the larger, the more expensive	the larger, the more expensive	the larger, the more accurate
k	int	maximum # of items in a single leaf	the larger, the less expensive	N/A	the larger, the less accurate

runtime (search phase) parameters

name	type	description	time complexity	accuracy
searchNum	int	# of requested neighbors	the larger, the more expensive	N/A
bucketScale	float64	affects the size of `bucket`	the larger, the more expensive	the larger, the more accurate

bucketScale affects the size of bucket which consists of items for exact distance calculation. The actual size of the bucket is calculated by int(searchNum * bucketScale).

In the search phase, we traverse index trees and continuously put items on reached leaves to the bucket until the bucket becomes full. Then we calculate the exact distances between a item in the bucket and the query vector to get approximate nearest neighbors.

Therefore, the larger bucketScale, the more computational complexity while the more accurate result to be produced.

example

package main

import (
	"fmt"
	"math/rand"
	"time"

	"github.com/mathetake/gann"
	"github.com/mathetake/gann/metric"
)

var (
	dim    = 3
	nTrees = 2
	k      = 10
	nItem  = 1000
)

func main() {
	rawItems := make([][]float64, 0, nItem)
	rand.Seed(time.Now().UnixNano())

	for i := 0; i < nItem; i++ {
		item := make([]float64, 0, dim)
		for j := 0; j < dim; j++ {
			item = append(item, rand.Float64())
		}
		rawItems = append(rawItems, item)
	}

	m, err := metric.NewCosineMetric(dim)
	if err != nil {
		// err handling
		return
	}

	// create index
	idx, err := gann.CreateNewIndex(rawItems, dim, nTrees, k, m)
	if err != nil {
		// error handling
		return
	}

	// search
	var searchNum = 5
	var bucketScale float64 = 10
	q := []float64{0.1, 0.02, 0.001}
	res, err := idx.GetANNbyVector(q, searchNum, bucketScale)
	if err != nil {
		// error handling
		return
	}

	fmt.Printf("res: %v\n", res)
}

references

License

MIT

marcofaggian/gannoy