countrymaam is a simple implementation of similar vector search with pure Go. This library is inspired by Flann, Annoy, and Faiss.
- Flat search index (
FlatIndex) - Kd-Tree base index (
KdTreeIndexandRandomizedKdTreeIndex) - Random-Projection Tree base index (
RpTreeIndexAndRandomizedRpTreeIndex) - Serialize/Deserialize with gop
$ go get github.com/ar90n/countrymaam
package main
import (
"bytes"
_ "embed"
"encoding/csv"
"fmt"
"io"
"strconv"
"github.com/ar90n/countrymaam"
)
//dim064.csv is derived from http://cs.joensuu.fi/sipu/datasets/
//go:embed dim064.csv
var dim64 []byte
func readFeatures(dim uint) ([][]int, error) {
r := csv.NewReader(bytes.NewReader(dim64))
ret := make([][]int, 0)
for {
record, err := r.Read()
if err == io.EOF {
break
}
if err != nil {
return nil, err
}
feature := make([]int, dim)
for i, text := range record {
val, err := strconv.ParseInt(text, 10, 32)
if err != nil {
return nil, err
}
feature[i] = int(val)
}
ret = append(ret, feature)
}
return ret, nil
}
func main() {
dim := uint(64)
index := countrymaam.NewKdTreeIndex[int, int](dim, 8)
features, err := readFeatures(dim)
if err != nil {
panic(err)
}
for i, f := range features {
index.Add(f, i)
}
index.Build()
query := []int{
177, 73, 110, 135, 85, 153, 143, 73, 210, 208, 148, 50, 39, 165, 51, 201, 47, 102, 198, 55, 192, 42, 89, 189, 104, 86, 183, 162, 60, 145, 122, 104, 133, 200, 167, 51, 147, 167, 191, 220, 85, 75, 57, 72, 43, 150, 155, 53, 163, 171, 106, 115, 99, 78, 88, 48, 81, 214, 114, 126, 196, 214, 220, 75,
}
neighbors, err := index.Search(query, 5, 32)
if err != nil {
panic(err)
}
for i, n := range neighbors {
fmt.Printf("%d: %d, %f\n", i, n.Item, n.Distance)
}
}$ go run ./example/kdtree.go
0: 1023, 0.000000
1: 974, 6.000000
2: 992, 7.000000
3: 1001, 9.000000
4: 975, 9.000000$ cd benchmark
$ make benchmark
$ ls ann-benchmarks/results/fashion-mnist-784-euclidean.png
ann-benchmarks/results/fashion-mnist-784-euclidean.png
The source code is licensed MIT. The website content is licensed CC BY 4.0,see LICENSE.