vendor packages

3 files changed, 494 insertions, 0 deletions

diff --git a/vendor/github.com/hongshibao/go-kdtree/LICENSE.md b/vendor/github.com/hongshibao/go-kdtree/LICENSE.md
new file mode 100644
index 0000000..bd12e68
--- /dev/null
+++ b/vendor/github.com/hongshibao/go-kdtree/LICENSE.md
@@ -0,0 +1,202 @@
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diff --git a/vendor/github.com/hongshibao/go-kdtree/README.md b/vendor/github.com/hongshibao/go-kdtree/README.md
new file mode 100644
index 0000000..85d4550
--- /dev/null
+++ b/vendor/github.com/hongshibao/go-kdtree/README.md
@@ -0,0 +1,92 @@
+# 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
new file mode 100644
index 0000000..64ecd60
--- /dev/null
+++ b/vendor/github.com/hongshibao/go-kdtree/kdtree.go
@@ -0,0 +1,200 @@
+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
+}