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-# KDTree
-Golang implementation of KD tree (https://en.wikipedia.org/wiki/K-d_tree) data structure
-
-## Getting started
-
-Use go tool to install the package in your packages tree:
-```
-go get github.com/hongshibao/go-kdtree
-```
-Then you can use it in import section of your Go programs:
-```go
-import "github.com/hongshibao/go-kdtree"
-```
-The package name is ```kdtree```.
-
-## Basic example
-
-First you need to implement the ```Point``` interface:
-```go
-type Point interface {
-	// Return the total number of dimensions
-	Dim() int
-	// Return the value X_{dim}, dim is started from 0
-	GetValue(dim int) float64
-	// Return the distance between two points
-	Distance(p Point) float64
-	// Return the distance between the point and the plane X_{dim}=val
-	PlaneDistance(val float64, dim int) float64
-}
-```
-Here is an example of implementing ```Point``` interface with square of Euclidean distance as the ```Distance``` definition:
-```go
-type EuclideanPoint struct {
-	Point
-	Vec []float64
-}
-
-func (p *EuclideanPoint) Dim() int {
-	return len(p.Vec)
-}
-
-func (p *EuclideanPoint) GetValue(dim int) float64 {
-	return p.Vec[dim]
-}
-
-func (p *EuclideanPoint) Distance(other Point) float64 {
-	var ret float64
-	for i := 0; i < p.Dim(); i++ {
-		tmp := p.GetValue(i) - other.GetValue(i)
-		ret += tmp * tmp
-	}
-	return ret
-}
-
-func (p *EuclideanPoint) PlaneDistance(val float64, dim int) float64 {
-	tmp := p.GetValue(dim) - val
-	return tmp * tmp
-}
-```
-Now you can create KD-tree from a list of points and get a list of k nearest neighbours for a target point:
-```go
-func NewEuclideanPoint(vals ...float64) *EuclideanPoint {
-	ret := &EuclideanPoint{}
-	for _, val := range vals {
-		ret.Vec = append(ret.Vec, val)
-	}
-	return ret
-}
-
-func main() {
-	p1 := NewEuclideanPoint(0.0, 0.0, 0.0)
-	p2 := NewEuclideanPoint(0.0, 0.0, 1.0)
-	p3 := NewEuclideanPoint(0.0, 1.0, 0.0)
-	p4 := NewEuclideanPoint(1.0, 0.0, 0.0)
-	points := make([]Point, 0)
-	points = append(points, p1)
-	points = append(points, p2)
-	points = append(points, p3)
-	points = append(points, p4)
-	tree := NewKDTree(points)
-	targetPoint := NewEuclideanPoint(0.0, 0.0, 0.1)
-	neighbours := tree.KNN(targetPoint, 2)
-	for idx, p := range neighbours {
-		fmt.Printf("Point %d: (%f", idx, p.GetValue(0))
-		for i := 1; i < p.Dim(); i++ {
-			fmt.Printf(", %f", p.GetValue(i))
-		}
-		fmt.Println(")")
-	}
-}
-```
-The returned k nearest neighbours are sorted by their distance with the target point.