Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/1220#discussion_r63733018
--- Diff:
flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/nn/QuadTree.scala
---
@@ -0,0 +1,352 @@
+/*
+ * 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
+
+import org.apache.flink.ml.math.{Breeze, Vector}
+import Breeze._
+
+import
org.apache.flink.ml.metrics.distances.{SquaredEuclideanDistanceMetric,
+EuclideanDistanceMetric, 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 vector of the corner of the bounding box with smallest
coordinates
+ * @param maxVec vector of the corner of the bounding box with smallest
coordinates
+ * @param distMetric metric, must be Euclidean or squareEuclidean
+ * @param maxPerBox threshold for number of points in each box before
slitting a box
+ */
+class QuadTree(
+ minVec: Vector,
+ maxVec: Vector,
+ distMetric: DistanceMetric,
+ maxPerBox: Int) {
+
+ class Node(
+ center: Vector,
+ width: Vector,
+ var children: Seq[Node]) {
+
+ val nodeElements = new ListBuffer[Vector]
+
+ /** for testing purposes only; used in QuadTreeSuite.scala
+ *
+ * @return center and width of the box
+ */
+ def getCenterWidth(): (Vector, Vector) = {
+ (center, width)
+ }
+
+ /** Tests whether the queryPoint is in the node, or a child of that
node
+ *
+ * @param queryPoint
+ * @return
+ */
+ def contains(queryPoint: Vector): Boolean = {
+ overlap(queryPoint, 0.0)
+ }
+
+ /** Tests if queryPoint is within a radius of the node
+ *
+ * @param queryPoint
+ * @param radius
+ * @return
+ */
+ def overlap(
+ queryPoint: Vector,
+ radius: Double): Boolean = {
+ val count = (0 until queryPoint.size).filter { i =>
+ (queryPoint(i) - radius < center(i) + width(i) / 2) &&
+ (queryPoint(i) + radius > center(i) - width(i) / 2)
+ }.size
+
+ count == queryPoint.size
+ }
+
+ /** Tests if queryPoint is near a node
+ *
+ * @param queryPoint
+ * @param radius
+ * @return
+ */
+ def isNear(
+ queryPoint: Vector,
+ radius: Double): Boolean = {
+ minDist(queryPoint) < radius
+ }
+
+ /**
+ * minDist is defined so that every point in the box
+ * has distance to queryPoint greater than minDist
+ * (minDist adopted from "Nearest Neighbors Queries" by N.
Roussopoulos et al.)
+ *
+ * @param queryPoint
+ * @return
+ */
+ def minDist(queryPoint: Vector): Double = {
+ val minDist = (0 until queryPoint.size).map { i =>
+ if (queryPoint(i) < center(i) - width(i) / 2) {
+ math.pow(queryPoint(i) - center(i) + width(i) / 2, 2)
+ } else if (queryPoint(i) > center(i) + width(i) / 2) {
+ math.pow(queryPoint(i) - center(i) - width(i) / 2, 2)
+ } else {
+ 0
+ }
+ }.sum
+
+ distMetric match {
+ case _: SquaredEuclideanDistanceMetric => minDist
+ case _: EuclideanDistanceMetric => math.sqrt(minDist)
+ case _ => throw new IllegalArgumentException(s" Error: metric must
be" +
+ s" Euclidean or SquaredEuclidean!")
+ }
+ }
+
+ /**
+ * Finds which child queryPoint lies in. node.children is a
Seq[Node], and
+ * whichChild finds the appropriate index of that Seq.
+ * @param queryPoint
+ * @return
+ */
+ def whichChild(queryPoint: Vector): Int = {
+ (0 until queryPoint.size).map { i =>
+ if (queryPoint(i) > center(i)) {
+ Math.pow(2, queryPoint.size - 1 - i).toInt
+ } else {
+ 0
+ }
+ }.sum
+ }
+
+ /** Makes children nodes by partitioning the box into equal sub-boxes
+ * and adding a node for each sub-box
+ */
+ 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 the center of the box
+ * @param width a vector of lengths of each dimension of the box
+ * @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(): Unit = {
+ printTreeRecur(root)
+ }
+
+ def printTreeRecur(node: Node) {
+ if (node.children != null) {
+ for (c <- node.children) {
+ printTreeRecur(c)
+ }
+ } else {
+ println("printing tree: n.nodeElements " + node.nodeElements)
+ }
+ }
+
+ /**
+ * Recursively adds an object to the tree
+ * @param queryPoint
+ */
+ def insert(queryPoint: Vector) {
+ insertRecur(queryPoint, root)
+ }
+
+ private def insertRecur(
+ queryPoint: Vector,
+ node: Node) {
+ if (node.children == null) {
+ if (node.nodeElements.length < maxPerBox) {
+ node.nodeElements += queryPoint
+ } else {
+ node.makeChildren()
+ for (o <- node.nodeElements) {
+ insertRecur(o, node.children(node.whichChild(o)))
+ }
+ node.nodeElements.clear()
+ insertRecur(queryPoint, node.children(node.whichChild(queryPoint)))
+ }
+ } else {
+ insertRecur(queryPoint, node.children(node.whichChild(queryPoint)))
+ }
+ }
+
+ /**
+ * 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 queryPoint. D_s is then
used during the
+ * kNN query to find all points within a radius D_s of queryPoint using
searchNeighbors.
+ * To find the "near" neighbors, a min-heap is defined on the leaf nodes
of the leaf
+ * nodes of the minimal bounding box of the queryPoint. 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(queryPoint),
+ *
+ * @param queryPoint a test point for which the method finds the minimal
bounding
+ * box that queryPoint lies in and returns elements in
that boxes
+ * siblings' leaf nodes
+ * @return
+ */
+ def searchNeighborsSiblingQueue(queryPoint: Vector): ListBuffer[Vector]
= {
+ val ret = new ListBuffer[Vector]
+ // edge case when the main box has not been partitioned at all
+ if (root.children == null) {
+ root.nodeElements.clone()
+ } else {
+ val nodeQueue = new PriorityQueue[(Double, Node)]()(Ordering.by(x =>
x._1))
+ searchRecurSiblingQueue(queryPoint, root, nodeQueue)
+
+ var count = 0
+ while (count < maxPerBox) {
+ val dq = nodeQueue.dequeue()
+ if (dq._2.nodeElements.nonEmpty) {
+ ret ++= dq._2.nodeElements
+ count += dq._2.nodeElements.length
+ }
+ }
+ ret
+ }
+ }
+
+ /**
+ *
+ * @param queryPoint point under consideration
+ * @param node node that queryPoint lies in
+ * @param nodeQueue defined in searchSiblingQueue, this stores nodes
based on their
+ * distance to node as defined by minDist
+ */
+ private def searchRecurSiblingQueue(
+ queryPoint: Vector,
+ node: Node,
+ nodeQueue: PriorityQueue[(Double, Node)]) {
+ if (node.children != null) {
+ for (child <- node.children; if child.contains(queryPoint)) {
+ if (child.children == null) {
+ for (c <- node.children) {
+ minNodes(queryPoint, c, nodeQueue)
+ }
+ } else {
+ searchRecurSiblingQueue(queryPoint, child, nodeQueue)
+ }
+ }
+ }
+ }
+
+ /**
+ * Goes down to minimal bounding box of queryPoint, and add elements to
nodeQueue
+ *
+ * @param queryPoint point under consideration
+ * @param node node that queryPoint lies in
+ * @param nodeQueue PriorityQueue that stores all points in minimal
bounding box of queryPoint
+ */
+ private def minNodes(
+ queryPoint: Vector,
+ node: Node,
+ nodeQueue: PriorityQueue[(Double, Node)]) {
+ if (node.children == null) {
+ nodeQueue += ((-node.minDist(queryPoint), node))
--- End diff --
Ah ok, thanks for the clarification
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