项目需要，需要把MVPtree这种冷门的数据结构写入Java，然网上没有成形的Java实现，虽说C++看惯了不过对C++实现复杂结构也是看得蒙蔽，幸好客户给了个github上job什么的人用Java写的VPtree，大体结构可以嵌入MVPtree。
对于MVPtree的其他信息请左转百度= =本文只讲述算法实现。
点查找树结构主要需解决的问题有2个：如何减少非必要点的搜索，以及如何减少距离计算次数。前者的解决方法比较容易想到，把点集分割为左右对称的两半长方形，或者脑洞大点的，通过距离切分（效率很高，因为所有查询都是基于点距离的）成为圆和圆环。后者适用面不是很广，优化思路通常是预先计算与基准点的距离，查询点时筛点。
VPtree就是使用距离划分点集的例子。每个结点一个点集，随意定个点作为基准点，然后把点集根据与基准点距离分成数量相等的2个子集，这2个子集再分别进入此结点的子结点，用点查找出点集的过程如出一辙，但是没有对第2点进行优化，这个结构适合于距离函数是曼哈顿距离或者欧几里得距离的情况。
MVPtree继承了VPtree用距离划分的特点，只不过一个结点会划分4个点集，同时通过path数组限制距离函数运行次数。划分为4个点集而不是2个点集，可以分割得细一些，减少无效点；使用一定数量的基准点限制，可以在查询频繁的情况下减少距离计算次数，并且这些基准点通常被切分得很散，大片大片的无效区域被排除了，效果拔群。这个结构适合于距离函数是计算次数过高的切比雪夫函数之流。
接下来就是代码的实现了。
MVPtree与VPtree的点有个不同之处，就是MVPtree的点还附上了与基准点的距离数组，这里就需要使用特别的点数据结构：MVPtree用点
核心代码如下：
public class MVPTreePoint<P> {
private ArrayList<Double> path;
private P point;
private final int maxLevel;
public MVPTreePoint(final P point, final int maxLevel) {
this.point = point;
this.maxLevel = maxLevel;
this.path = new ArrayList<>();
}
public void addDistanceToSelf(final MVPTreePoint<P> vantagePointElement, final DistanceFunction<P> distanceFunction) {
if(this.path.size() < this.maxLevel)
this.path.add(distanceFunction.getDistance(this.point, vantagePointElement.point));
}
public void addDistanceToSelf(final P vantagePoint, final DistanceFunction<P> distanceFunction) {
if(this.path.size() < this.maxLevel)
this.path.add(distanceFunction.getDistance(this.point, vantagePoint));
}
public void addDistanceToSelf(final double distance) {
if(this.path.size() < this.maxLevel) {
this.path.add(distance);
}
}
public void removeDistanceToSelf(final int position) {
if(position < this.path.size()) {
this.path.remove(position);
}
}
public double getDistanceToSelf(int i) {
return this.path.get(i);
}
public int size() {
return this.path.size();
}
public void clearPath() {
this.path.clear();
}
public P getPoint() {
return this.point;
}
@SuppressWarnings("unchecked")
public boolean equals(Object o){
MVPTreePoint<P> t = (MVPTreePoint<P>) o;
return this.point.equals(t.point);
}
}
MVPTreePoint
把距离数组写到点类上而不是集成到树结点类上，结构会清晰一些，并且从点里取出距离也方便。
MVPtree与VPtree有好多不同的地方，但是好多都只是改一下类名，把P,E改成MVPTreePoint<P>,MVPTreePoint<E>，这里主讲核心算法——初始化树和点查询。
初始化MVPtree不仅要多选出一个基准点，多切分2次数组，还要把基准点到每个点的距离都分别储存起来。
capacity就是叶子结点的容量，要设中间一些，根据数据规模定吧。
原论文把基准点从点集取出来放到单独的位置上，但是实际编写程序时，把基准点仅仅当作一个基准点，基准点还是作为点集的一部分初始化。这样，数据结构仅仅是多出quantityOfPoint/capacity个点，但是程序编写方便了很多。
public MVPTreeNode(
final Collection<MVPTreePoint<E>> pointNodes,
final DistanceFunction<P> distanceFunction,
final MVPThresholdSelectionStrategy<P, E> thresholdSelectionStrategy,
final int capacity, final int maxLevel) {
if (capacity < 1) {
throw new IllegalArgumentException("Capacity must be positive.");
}
if (pointNodes.isEmpty()) {
throw new IllegalArgumentException(
"Cannot create a MVPTreeNode with an empty list of points.");
}
this.capacity = capacity;
this.maxLevel = maxLevel;
this.distanceFunction = distanceFunction;
this.thresholdSelectionStrategy = thresholdSelectionStrategy;
this.pointNodes = new ArrayList<>(pointNodes);
this.children = new MVPTreeNode[2][2];
this.vantagePoint = (E[]) new Object[2];
this.secondThreshold = new double[2];
this.anneal();
}
protected void anneal() {
if (this.pointNodes == null) {
int childrenSize[][] = new int[2][2];
for (int i = 0; i < 2; i++) {
for (int j = 0; j < 2; j++) {
childrenSize[i][j] = this.children[i][j].size();
}
}
if (childrenSize[0][0] == 0 || childrenSize[0][1] == 0
|| childrenSize[1][0] == 0 || childrenSize[1][1] == 0) {
// One of the child nodes has become empty, and needs to be
// pruned.
this.pointNodes = new ArrayList<>(childrenSize[0][0]
+ childrenSize[0][1] + childrenSize[1][0]
+ childrenSize[1][1]);
this.addAllPointsToCollection(this.pointNodes);
for (MVPTreePoint<E> pointNode : this.pointNodes) {
pointNode.clearPath();
}
for (int i = 0; i < 2; i++) {
for (int j = 0; j < 2; j++) {
this.children[i][j] = null;
}
}
this.anneal();
} else {
for (int i = 0; i < 2; i++) {
for (int j = 0; j < 2; j++) {
this.children[i][j].anneal();
}
}
}
} else {
int firstVantagePointIndex = new Random().nextInt(this.pointNodes
.size());
this.vantagePoint[0] = this.pointNodes.get(firstVantagePointIndex)
.getPoint();
this.firstThreshold = this.thresholdSelectionStrategy
.selectThreshold(this.pointNodes, this.vantagePoint[0],
this.distanceFunction);
int firstIndexPastThreshold;
try {
firstIndexPastThreshold = MVPTreeNode.partitionPoints(
this.pointNodes, this.vantagePoint[0],
this.firstThreshold, this.distanceFunction);
} catch (final PartitionException e) {
this.storeInOneNode();
return;
}
if (this.pointNodes.size() > this.capacity) {
List<MVPTreePoint<E>> subTreeList[] = new List[2];
subTreeList[0] = this.pointNodes.subList(0,
firstIndexPastThreshold);
subTreeList[1] = this.pointNodes.subList(
firstIndexPastThreshold, this.pointNodes.size());
// if points can be divided into 2 parts, find second vantage
// point and try to split point array
int secondVantagePointIndex = new Random()
.nextInt(subTreeList[1].size());
this.vantagePoint[1] = subTreeList[1].get(
secondVantagePointIndex).getPoint();
int splitPosition[] = new int[2];
for (int i = 0; i < 2; i++) {
this.secondThreshold[i] = this.thresholdSelectionStrategy
.selectThreshold(subTreeList[i],
this.vantagePoint[1], this.distanceFunction);
try {
splitPosition[i] = MVPTreeNode.partitionPoints(
subTreeList[i], this.vantagePoint[1],
this.secondThreshold[i], this.distanceFunction);
} catch (final PartitionException e) {
this.storeInOneNode();
return;
}
}
for (MVPTreePoint<E> pointNode : this.pointNodes) {
pointNode.addDistanceToSelf(this.distanceFunction
.getDistance(pointNode.getPoint(),
this.vantagePoint[0]));
pointNode.addDistanceToSelf(this.distanceFunction
.getDistance(pointNode.getPoint(),
this.vantagePoint[1]));
}
for (int i = 0; i < 2; i++) {
this.children[i][0] = new MVPTreeNode<>(
subTreeList[i].subList(0, splitPosition[i]),
this.distanceFunction,
this.thresholdSelectionStrategy, this.capacity,
this.maxLevel);
this.children[i][1] = new MVPTreeNode<>(
subTreeList[i].subList(splitPosition[i],
subTreeList[i].size()),
this.distanceFunction,
this.thresholdSelectionStrategy, this.capacity,
this.maxLevel);
}
this.pointNodes = null;
} else {
this.storeInOneNode();
}
}
}
private void storeInOneNode() {
int maxIndex = 0;
double maxDistance = this.distanceFunction.getDistance(this.pointNodes
.get(0).getPoint(), this.vantagePoint[0]);
for (int i = 1; i < this.pointNodes.size(); i++) {
double curDistance = this.distanceFunction.getDistance(
this.pointNodes.get(i).getPoint(), this.vantagePoint[0]);
if (maxDistance < curDistance) {
maxDistance = curDistance;
maxIndex = i;
}
}
this.vantagePoint[1] = this.pointNodes.get(maxIndex).getPoint();
for (int i = 0; i < 2; i++) {
for (int j = 0; j < 2; j++) {
this.children[i][j] = null;
}
}
}
init MVPtree
原作者给出了2种查询方式：找离查询点前k近点和找离查询点不远于u点。
找离查询点前k点的算法可以沿用查询VPtree时的做法，先查找查询点所在的子结点，再查找其他子结点，注意要先判定收集者是否装满（没装满的话，不管是啥点都直接塞），再判定收集者与查询点的最远距离（对第二种查找方式来说是固定距离）是否小于点/点集与查询点的最近距离（在树结点和叶子结点都有用处）。
public void collectNearestNeighbors(
final NearestNeighborCollector<P, E> collector, int depth) {
if (this.pointNodes == null) {
// O1-Q
final double distanceFromFirstVantagePointToQueryPoint = this.distanceFunction
.getDistance(this.vantagePoint[0],
collector.getQueryPoint().getPoint());
// O2-Q
final double distanceFromSecondVantagePointToQueryPoint = this.distanceFunction
.getDistance(this.vantagePoint[1],
collector.getQueryPoint().getPoint());
collector.getQueryPoint().addDistanceToSelf(
distanceFromFirstVantagePointToQueryPoint);
collector.getQueryPoint().addDistanceToSelf(
distanceFromSecondVantagePointToQueryPoint);
final MVPTreeNode<P, E> index = this
.getChildNodeForPoint(collector.getQueryPoint().getPoint());
index.collectNearestNeighbors(collector, depth + 1);
// O1-Q - O1-S1
double basicDistance = distanceFromFirstVantagePointToQueryPoint
- this.firstThreshold;
for(int i = 0;i < 2;i ++){
if (!collector.isFull() || basicDistance <= collector.getRadius()) {
// O2-Q - O2-S2
double touchDistance = distanceFromSecondVantagePointToQueryPoint
- this.secondThreshold[i];
for(int j = 0;j < 2;j ++){
if (index != this.children[i][j]
&& (!collector.isFull() || touchDistance <= collector.getRadius())) {
this.children[i][j].collectNearestNeighbors(collector, depth + 1);
}
touchDistance *= -1;
}
}
basicDistance *= -1;
}
collector.getQueryPoint().removeDistanceToSelf(depth + depth + 1);
collector.getQueryPoint().removeDistanceToSelf(depth + depth);
} else {
for (final MVPTreePoint<E> pointNode : this.pointNodes) {
if(!collector.isFull() || this.isAbleToInsert(collector.getRadius(),
collector.getQueryPoint(), pointNode)) {
collector.offerPoint(pointNode.getPoint());
}
}
}
}
collectNearestNeighbors
找离查询点不远于u点算法就是论文里讲述的算法，执行步骤与收集第k近有相同之处，不同在于限定距离是固定值，且任何时候都必须判定
public void collectAllWithinDistance(final MVPTreePoint<P> queryPoint,
final double maxDistance, final Collection<E> collection, int depth) {
if (this.pointNodes == null) {
final double distanceFromFirstVantagePointToQueryPoint = this.distanceFunction
.getDistance(this.vantagePoint[0], queryPoint.getPoint());
final double distanceFromSecondVantagePointToQueryPoint = this.distanceFunction
.getDistance(this.vantagePoint[1], queryPoint.getPoint());
queryPoint
.addDistanceToSelf(distanceFromFirstVantagePointToQueryPoint);
queryPoint
.addDistanceToSelf(distanceFromSecondVantagePointToQueryPoint);
// We want to search any of this node‘s children that intersect with
// the query region
if (distanceFromFirstVantagePointToQueryPoint <= this.firstThreshold
+ maxDistance) {
if (distanceFromSecondVantagePointToQueryPoint <= this.secondThreshold[0]
+ maxDistance) {
this.children[0][0].collectAllWithinDistance(queryPoint,
maxDistance, collection, depth + 1);
}
if (distanceFromSecondVantagePointToQueryPoint + maxDistance >= this.secondThreshold[0]) {
this.children[0][1].collectAllWithinDistance(queryPoint,
maxDistance, collection, depth + 1);
}
}
if (distanceFromFirstVantagePointToQueryPoint + maxDistance >= this.firstThreshold) {
if (distanceFromSecondVantagePointToQueryPoint <= this.secondThreshold[1]
+ maxDistance) {
this.children[1][0].collectAllWithinDistance(queryPoint,
maxDistance, collection, depth + 1);
}
if (distanceFromSecondVantagePointToQueryPoint + maxDistance >= this.secondThreshold[1]) {
this.children[1][1].collectAllWithinDistance(queryPoint,
maxDistance, collection, depth + 1);
}
}
queryPoint.removeDistanceToSelf(depth + depth + 1);
queryPoint.removeDistanceToSelf(depth + depth);
} else {
for (MVPTreePoint<E> pointNode : pointNodes) {
if (this.isAbleToInsert(maxDistance, queryPoint, pointNode))
collection.add(pointNode.getPoint());
}
}
}
collectAllWithinDistance
这两种查询方式都需要比较预先计算的距离，把这种计算合为一个函数：
public boolean isAbleToInsert(double limitDistance,
MVPTreePoint<P> queryPoint, MVPTreePoint<E> pointNode) {
for (int i = 0; i < queryPoint.size(); i++) {
double disOffset = queryPoint.getDistanceToSelf(i)
- pointNode.getDistanceToSelf(i);
if (Math.abs(disOffset) > limitDistance) {
return false;
}
}
return this.distanceFunction.getDistance(pointNode.getPoint(),
queryPoint.getPoint()) <= limitDistance;
}
isAbleToInsert
其他函数也需要修改，但是没有像这3个函数一样大幅度的修改结构。
时间： 2024-10-02 03:44:06