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Diffstat (limited to 'vendor/github.com/hongshibao/go-kdtree')
| -rw-r--r-- | vendor/github.com/hongshibao/go-kdtree/LICENSE.md | 202 | ||||
| -rw-r--r-- | vendor/github.com/hongshibao/go-kdtree/README.md | 92 | ||||
| -rw-r--r-- | vendor/github.com/hongshibao/go-kdtree/kdtree.go | 200 |
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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 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2015 thinkpoet (shibaohong@outlook.com) + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + 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 +} |