https://github.com/rikonor/go-ann
Approximate Nearest Neighbor using the MRPT algorithm
https://github.com/rikonor/go-ann
golang mrpt nearest-neighbor-search
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Approximate Nearest Neighbor using the MRPT algorithm
- Host: GitHub
- URL: https://github.com/rikonor/go-ann
- Owner: rikonor
- License: mit
- Created: 2017-07-22T20:25:13.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2018-08-22T12:06:58.000Z (about 7 years ago)
- Last Synced: 2025-04-05T07:41:37.505Z (7 months ago)
- Topics: golang, mrpt, nearest-neighbor-search
- Language: Go
- Size: 16.6 KB
- Stars: 23
- Watchers: 5
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
go-ann
---
[](http://godoc.org/github.com/rikonor/go-ann)
[](https://travis-ci.org/rikonor/go-ann)
Pure Go implementation of Approximate k-Nearest-Neighbor search.
This package exposes:
- A naive exact match implementation which can be used for testing purposes (`ExhaustiveANNer`).
- An Approximate search based on the [MRPT algorithm](https://arxiv.org/pdf/1509.06957.pdf) (`MRPTANNer`).
### Usage
The package exposes an `ANNer` interface:
```go
// ANNer allows you to perform an approximate k-NN search given a query point
type ANNer interface {
// ANN takes a query point and how many nearest neighbors to return
// and returns the indices of the neihgbors
ANN(q []float64, k int) []int
}
```
***Basic example***
```go
// Assuming we have a series of vectors xs
xs := [][]float64{
[]float64{0, 0, 0, 0},
[]float64{1.1, 1.1, 1.1, 1.1},
[]float64{2, 2, 2, 2},
...
}
// Decide on a query point q
q := []float64{1, 1, 1, 1}
// We'd like to find the index of it's nearest neighbor
k := 1
```
Using exact/Exhaustive search
```go
// Create a naive NN index using exact/exhaustive search
nn := NewExhaustiveNNer(xs)
indices := nn.ANN(q, k)
// indices -> [1] for (1.1, 1.1, 1.1, 1.1)
```
Using approximate search
```go
// Notice that the MRPT algorithm has a few tunable parameters: Number of trees and tree depth
// Create a new ANN index using the MRPT algorithm
nn := NewMRPTANNer(
3, // Number of trees
10, // Tree depth
xs, // Vectors
)
indices := nn.ANN(q, k)
// indices -> [1] for (1.1, 1.1, 1.1, 1.1)
```
***Using a MappedANNer***
Wrap an ANNer with a MappedANNer to associate values with vectors.
```go
// vectorIDs are values which are associated with their respective vectors
// e.g We'd like to associate vector #0 with "1", etc
vectorIDs := []string{"1", "2", "3", "4", ...}
mnn := NewMappedANNer(nn, vectorIDs)
q := []float64{1, 2, 3}
k := 2
// Use as usual, however, now the return value is the associated value
// rather then the vector indices
vs := mnn.ANN(q, k)
```
***Very basic benchmark***
```
BenchmarkMRPTANNer-8 10000 138771 ns/op
BenchmarkExhaustiveNNer-8 100 16952058 ns/op
```
### License
MIT