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-rw-r--r--vendor/github.com/hongshibao/go-kdtree/LICENSE.md202
-rw-r--r--vendor/github.com/hongshibao/go-kdtree/README.md92
-rw-r--r--vendor/github.com/hongshibao/go-kdtree/kdtree.go200
3 files changed, 0 insertions, 494 deletions
diff --git a/vendor/github.com/hongshibao/go-kdtree/LICENSE.md b/vendor/github.com/hongshibao/go-kdtree/LICENSE.md
deleted file mode 100644
index bd12e68..0000000
--- a/vendor/github.com/hongshibao/go-kdtree/LICENSE.md
+++ /dev/null
@@ -1,202 +0,0 @@
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diff --git a/vendor/github.com/hongshibao/go-kdtree/README.md b/vendor/github.com/hongshibao/go-kdtree/README.md
deleted file mode 100644
index 85d4550..0000000
--- a/vendor/github.com/hongshibao/go-kdtree/README.md
+++ /dev/null
@@ -1,92 +0,0 @@
-# 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.
diff --git a/vendor/github.com/hongshibao/go-kdtree/kdtree.go b/vendor/github.com/hongshibao/go-kdtree/kdtree.go
deleted file mode 100644
index 64ecd60..0000000
--- a/vendor/github.com/hongshibao/go-kdtree/kdtree.go
+++ /dev/null
@@ -1,200 +0,0 @@
-package kdtree
-
-import (
- "container/heap"
-
- "github.com/hongshibao/go-algo"
-)
-
-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
-}
-
-type PointBase struct {
- Point
- Vec []float64
-}
-
-func (b PointBase) Dim() int {
- return len(b.Vec)
-}
-
-func (b PointBase) GetValue(dim int) float64 {
- return b.Vec[dim]
-}
-
-func NewPointBase(vals []float64) PointBase {
- ret := PointBase{}
- for _, val := range vals {
- ret.Vec = append(ret.Vec, val)
- }
- return ret
-}
-
-type kdTreeNode struct {
- axis int
- splittingPoint Point
- leftChild *kdTreeNode
- rightChild *kdTreeNode
-}
-
-type KDTree struct {
- root *kdTreeNode
- dim int
-}
-
-func (t *KDTree) Dim() int {
- return t.dim
-}
-
-func (t *KDTree) KNN(target Point, k int) []Point {
- hp := &kNNHeapHelper{}
- t.search(t.root, hp, target, k)
- ret := make([]Point, 0, hp.Len())
- for hp.Len() > 0 {
- item := heap.Pop(hp).(*kNNHeapNode)
- ret = append(ret, item.point)
- }
- for i := len(ret)/2 - 1; i >= 0; i-- {
- opp := len(ret) - 1 - i
- ret[i], ret[opp] = ret[opp], ret[i]
- }
- return ret
-}
-
-func (t *KDTree) search(p *kdTreeNode,
- hp *kNNHeapHelper, target Point, k int) {
- stk := make([]*kdTreeNode, 0)
- for p != nil {
- stk = append(stk, p)
- if target.GetValue(p.axis) < p.splittingPoint.GetValue(p.axis) {
- p = p.leftChild
- } else {
- p = p.rightChild
- }
- }
- for i := len(stk) - 1; i >= 0; i-- {
- cur := stk[i]
- dist := target.Distance(cur.splittingPoint)
- if hp.Len() < k || (*hp)[0].distance >= dist {
- heap.Push(hp, &kNNHeapNode{
- point: cur.splittingPoint,
- distance: dist,
- })
- if hp.Len() > k {
- heap.Pop(hp)
- }
- }
- if hp.Len() < k || target.PlaneDistance(
- cur.splittingPoint.GetValue(cur.axis), cur.axis) <=
- (*hp)[0].distance {
- if target.GetValue(cur.axis) < cur.splittingPoint.GetValue(cur.axis) {
- t.search(cur.rightChild, hp, target, k)
- } else {
- t.search(cur.leftChild, hp, target, k)
- }
- }
- }
-}
-
-func NewKDTree(points []Point) *KDTree {
- if len(points) == 0 {
- return nil
- }
- ret := &KDTree{
- dim: points[0].Dim(),
- root: createKDTree(points, 0),
- }
- return ret
-}
-
-func createKDTree(points []Point, depth int) *kdTreeNode {
- if len(points) == 0 {
- return nil
- }
- dim := points[0].Dim()
- ret := &kdTreeNode{
- axis: depth % dim,
- }
- if len(points) == 1 {
- ret.splittingPoint = points[0]
- return ret
- }
- idx := selectSplittingPoint(points, ret.axis)
- if idx == -1 {
- return nil
- }
- ret.splittingPoint = points[idx]
- ret.leftChild = createKDTree(points[0:idx], depth+1)
- ret.rightChild = createKDTree(points[idx+1:len(points)], depth+1)
- return ret
-}
-
-type selectionHelper struct {
- axis int
- points []Point
-}
-
-func (h *selectionHelper) Len() int {
- return len(h.points)
-}
-
-func (h *selectionHelper) Less(i, j int) bool {
- return h.points[i].GetValue(h.axis) < h.points[j].GetValue(h.axis)
-}
-
-func (h *selectionHelper) Swap(i, j int) {
- h.points[i], h.points[j] = h.points[j], h.points[i]
-}
-
-func selectSplittingPoint(points []Point, axis int) int {
- helper := &selectionHelper{
- axis: axis,
- points: points,
- }
- mid := len(points)/2 + 1
- err := algo.QuickSelect(helper, mid)
- if err != nil {
- return -1
- }
- return mid - 1
-}
-
-type kNNHeapNode struct {
- point Point
- distance float64
-}
-
-type kNNHeapHelper []*kNNHeapNode
-
-func (h kNNHeapHelper) Len() int {
- return len(h)
-}
-
-func (h kNNHeapHelper) Less(i, j int) bool {
- return h[i].distance > h[j].distance
-}
-
-func (h kNNHeapHelper) Swap(i, j int) {
- h[i], h[j] = h[j], h[i]
-}
-
-func (h *kNNHeapHelper) Push(x interface{}) {
- item := x.(*kNNHeapNode)
- *h = append(*h, item)
-}
-
-func (h *kNNHeapHelper) Pop() interface{} {
- old := *h
- n := len(old)
- item := old[n-1]
- *h = old[0 : n-1]
- return item
-}