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https://github.com/kyroy/kdtree
A k-d tree implementation in Go.
https://github.com/kyroy/kdtree
go golang kdtree library nearest-neighbor nearest-neighbor-search tree
Last synced: 3 months ago
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A k-d tree implementation in Go.
- Host: GitHub
- URL: https://github.com/kyroy/kdtree
- Owner: kyroy
- License: apache-2.0
- Created: 2018-03-26T14:15:34.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-04-09T13:27:23.000Z (7 months ago)
- Last Synced: 2024-06-18T14:14:00.152Z (5 months ago)
- Topics: go, golang, kdtree, library, nearest-neighbor, nearest-neighbor-search, tree
- Language: Go
- Homepage:
- Size: 36.1 KB
- Stars: 135
- Watchers: 7
- Forks: 28
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-golang-repositories - kdtree - d tree implementation in Go. (Repositories)
README
# kdtree
[](https://godoc.org/github.com/kyroy/kdtree)
[](https://travis-ci.org/kyroy/kdtree)
[](https://codecov.io/gh/kyroy/kdtree)
[](https://goreportcard.com/report/github.com/kyroy/kdtree)
[](https://github.com/kyroy/kdtree/blob/master/LICENSE)
A [k-d tree](https://en.wikipedia.org/wiki/K-d_tree) implementation in Go with:
- n-dimensional points
- k-nearest neighbor search
- range search
- remove without rebuilding the whole subtree
- data attached to the points
- using own structs by implementing a simple 2 function interface
## Usage
```bash
go get github.com/kyroy/kdtree
```
```go
import "github.com/kyroy/kdtree"
````
### Implement the `kdtree.Point` interface
```go
// Point specifies one element of the k-d tree.
type Point interface {
// Dimensions returns the total number of dimensions
Dimensions() int
// Dimension returns the value of the i-th dimension
Dimension(i int) float64
}
```
### `points.Point2d`
```go
type Data struct {
value string
}
func main() {
tree := kdtree.New([]kdtree.Point{
&points.Point2D{X: 3, Y: 1},
&points.Point2D{X: 5, Y: 0},
&points.Point2D{X: 8, Y: 3},
})
// Insert
tree.Insert(&points.Point2D{X: 1, Y: 8})
tree.Insert(&points.Point2D{X: 7, Y: 5})
// KNN (k-nearest neighbor)
fmt.Println(tree.KNN(&points.Point{Coordinates: []float64{1, 1, 1}}, 2))
// [{3.00 1.00} {5.00 0.00}]
// RangeSearch
fmt.Println(tree.RangeSearch(kdrange.New(1, 8, 0, 2)))
// [{5.00 0.00} {3.00 1.00}]
// Points
fmt.Println(tree.Points())
// [{3.00 1.00} {1.00 8.00} {5.00 0.00} {8.00 3.00} {7.00 5.00}]
// Remove
fmt.Println(tree.Remove(&points.Point2D{X: 5, Y: 0}))
// {5.00 0.00}
// String
fmt.Println(tree)
// [[{1.00 8.00} {3.00 1.00} [ {8.00 3.00} {7.00 5.00}]]]
// Balance
tree.Balance()
fmt.Println(tree)
// [[[{3.00 1.00} {1.00 8.00} ] {7.00 5.00} {8.00 3.00}]]
}
```
### n-dimensional Points (`points.Point`)
```go
type Data struct {
value string
}
func main() {
tree := kdtree.New([]kdtree.Point{
points.NewPoint([]float64{7, 2, 3}, Data{value: "first"}),
points.NewPoint([]float64{3, 7, 10}, Data{value: "second"}),
points.NewPoint([]float64{4, 6, 1}, Data{value: "third"}),
})
// Insert
tree.Insert(points.NewPoint([]float64{12, 4, 6}, Data{value: "fourth"}))
tree.Insert(points.NewPoint([]float64{8, 1, 0}, Data{value: "fifth"}))
// KNN (k-nearest neighbor)
fmt.Println(tree.KNN(&points.Point{Coordinates: []float64{1, 1, 1}}, 2))
// [{[4 6 1] {third}} {[7 2 3] {first}}]
// RangeSearch
fmt.Println(tree.RangeSearch(kdrange.New(1, 15, 1, 5, 0, 5)))
// [{[7 2 3] {first}} {[8 1 0] {fifth}}]
// Points
fmt.Println(tree.Points())
// [{[3 7 10] {second}} {[4 6 1] {third}} {[8 1 0] {fifth}} {[7 2 3] {first}} {[12 4 6] {fourth}}]
// Remove
fmt.Println(tree.Remove(points.NewPoint([]float64{3, 7, 10}, nil)))
// {[3 7 10] {second}}
// String
fmt.Println(tree)
// [[ {[4 6 1] {third}} [{[8 1 0] {fifth}} {[7 2 3] {first}} {[12 4 6] {fourth}}]]]
// Balance
tree.Balance()
fmt.Println(tree)
// [[[{[7 2 3] {first}} {[4 6 1] {third}} ] {[8 1 0] {fifth}} {[12 4 6] {fourth}}]]
}
```