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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 deleted file mode 100644 index bd12e68..0000000 --- a/vendor/github.com/hongshibao/go-kdtree/LICENSE.md +++ /dev/null @@ -1,202 +0,0 @@ - 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 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 -} |