Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/1220#discussion_r46121733
--- Diff:
flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/nn/QuadTree.scala ---
@@ -0,0 +1,301 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you 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.
+ */
+
+package org.apache.flink.ml.nn.util
+
+import org.apache.flink.ml.math.{Breeze, Vector}
+import Breeze._
+
+import org.apache.flink.ml.metrics.distances.DistanceMetric
+
+import scala.collection.mutable.ListBuffer
+import scala.collection.mutable.PriorityQueue
+
+/**
+ * n-dimensional QuadTree data structure; partitions
+ * spatial data for faster queries (e.g. KNN query)
+ * The skeleton of the data structure was initially
+ * based off of the 2D Quadtree found here:
+ *
http://www.cs.trinity.edu/~mlewis/CSCI1321-F11/Code/src/util/Quadtree.scala
+ *
+ * Many additional methods were added to the class both for
+ * efficient KNN queries and generalizing to n-dim.
+ *
+ * @param minVec
+ * @param maxVec
+ */
+class QuadTree(minVec:Vector, maxVec:Vector,distMetric:DistanceMetric){
+ var maxPerBox = 20
+
+ class Node(center:Vector,width:Vector, var children:Seq[Node]) {
+
+ var objects = new ListBuffer[Vector]
+
+ /** for testing purposes only; used in QuadTreeSuite.scala
+ *
+ * @return
+ */
+ def getCenterWidth(): (Vector, Vector) = {
+ (center, width)
+ }
+
+ def contains(obj: Vector): Boolean = {
+ overlap(obj, 0.0)
+ }
+
+ /** Tests if obj is within a radius of the node
+ *
+ * @param obj
+ * @param radius
+ * @return
+ */
+ def overlap(obj: Vector, radius: Double): Boolean = {
+ var count = 0
+ for (i <- 0 to obj.size - 1) {
+ if (obj(i) - radius < center(i) + width(i) / 2 &&
+ obj(i) + radius > center(i) - width(i) / 2) {
+ count += 1
+ }
+ }
+
+ if (count == obj.size) {
+ true
+ } else {
+ false
+ }
+ }
+
+ /** Tests if obj is near a node: minDist is defined so that every
point in the box
+ * has distance to obj greater than minDist
+ * (minDist adopted from "Nearest Neighbors Queries" by N.
Roussopoulos et al.)
+ *
+ * @param obj
+ * @param radius
+ * @return
+ */
+ def isNear(obj: Vector, radius: Double): Boolean = {
+ if (minDist(obj) < radius) {
+ true
+ } else {
+ false
+ }
+ }
+
+ def minDist(obj: Vector): Double = {
+ var minDist = 0.0
+ for (i <- 0 to obj.size - 1) {
+ if (obj(i) < center(i) - width(i) / 2) {
+ minDist += math.pow(obj(i) - center(i) + width(i) / 2, 2)
+ } else if (obj(i) > center(i) + width(i) / 2) {
+ minDist += math.pow(obj(i) - center(i) - width(i) / 2, 2)
+ }
+ }
+ minDist
+ }
+
+ def whichChild(obj: Vector): Int = {
+
+ var count = 0
+ for (i <- 0 to obj.size - 1) {
+ if (obj(i) > center(i)) {
+ count += Math.pow(2, obj.size -1 - i).toInt
+ }
+ }
+ count
+ }
+
+ def makeChildren() {
+ val centerClone = center.copy
+ val cPart = partitionBox(centerClone, width)
+ val mappedWidth = 0.5*width.asBreeze
+ children = cPart.map(p => new Node(p, mappedWidth.fromBreeze, null))
+
+ }
+
+ /**
+ * Recursive function that partitions a n-dim box by taking the (n-1)
dimensional
+ * plane through the center of the box keeping the n-th coordinate
fixed,
+ * then shifting it in the n-th direction up and down
+ * and recursively applying partitionBox to the two shifted (n-1)
dimensional planes.
+ *
+ * @param center
+ * @param width
+ * @return
+ *
+ */
+ def partitionBox(center: Vector, width: Vector): Seq[Vector] = {
+
+ def partitionHelper(box: Seq[Vector], dim: Int): Seq[Vector] = {
+ if (dim >= width.size) {
+ box
+ } else {
+ val newBox = box.flatMap {
+ vector =>
+ val (up, down) = (vector.copy, vector)
+ up.update(dim, up(dim) - width(dim) / 4)
+ down.update(dim, down(dim) + width(dim) / 4)
+
+ Seq(up,down)
+ }
+ partitionHelper(newBox, dim + 1)
+ }
+ }
+ partitionHelper(Seq(center), 0)
+ }
+ }
+
+
+ val root = new Node( ((minVec.asBreeze +
maxVec.asBreeze)*0.5).fromBreeze,
+ (maxVec.asBreeze - minVec.asBreeze).fromBreeze, null)
+
+ /**
+ * simple printing of tree for testing/debugging
+ */
+ def printTree(){
+ printTreeRecur(root)
+ }
+
+ def printTreeRecur(n:Node){
+ if(n.children != null) {
+ for (c <- n.children){
+ printTreeRecur(c)
+ }
+ }else{
+ println("printing tree: n.objects " + n.objects)
+ }
+ }
+
+ /**
+ * Recursively adds an object to the tree
+ * @param obj
+ */
+ def insert(obj:Vector){
+ insertRecur(obj,root)
+ }
+
+ private def insertRecur(obj:Vector,n:Node) {
+ if(n.children==null) {
+ if(n.objects.length < maxPerBox )
+ {
+ n.objects += obj
+ }
+
+ else{
+ n.makeChildren() ///make children nodes; place objects into them
and clear node.objects
+ for (o <- n.objects){
+ insertRecur(o, n.children(n.whichChild(o)))
+ }
+ n.objects.clear()
+ insertRecur(obj, n.children(n.whichChild(obj)))
+ }
+ } else{
+ insertRecur(obj, n.children(n.whichChild(obj)))
+ }
+ }
+
+ /** Following methods are used to zoom in on a region near a test point
for a fast KNN query.
+ *
+ * This capability is used in the KNN query to find k "near" neighbors
n_1,...,n_k, from
+ * which one computes the max distance D_s to obj. D_s is then used
during the
+ * kNN query to find all points within a radius D_s of obj using
searchNeighbors.
+ * To find the "near" neighbors, a min-heap is defined on the leaf
nodes of the quadtree.
+ * The priority of a leaf node is an appropriate notion of the distance
between the test
+ * point and the node, which is defined by minDist(obj),
+ *
+ */
+ private def subOne(tuple: (Double,Node)) = tuple._1
+
+ def searchNeighborsSiblingQueue(obj:Vector):ListBuffer[Vector] = {
+ var ret = new ListBuffer[Vector]
+ if (root.children == null) { // edge case when the main box has not
been partitioned at all
+ root.objects
--- End diff --
You could also return a `List[Vector]` and then simply call `toList`. This
will copy the `ListBuffer` instance only when it is modified afterwards.
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