[
https://issues.apache.org/jira/browse/FLINK-1745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15030990#comment-15030990
]
ASF GitHub Bot commented on FLINK-1745:
---------------------------------------
Github user chiwanpark commented on a diff in the pull request:
https://github.com/apache/flink/pull/1220#discussion_r46093270
--- Diff:
flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/nn/QuadTree.scala ---
@@ -0,0 +1,340 @@
+/*
+ * 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.{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)
+ }
+
+ 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 = {
+ var count = 0
+ for (i <- 0 to queryPoint.size - 1) {
+ if (queryPoint(i) - radius < center(i) + width(i) / 2 &&
+ queryPoint(i) + radius > center(i) - width(i) / 2) {
+ count += 1
+ }
+ }
+
+ if (count == queryPoint.size) {
+ true
+ } else {
+ false
+ }
+ }
+
+ /** Tests if queryPoint is near a node
+ *
+ * @param queryPoint
+ * @param radius
+ * @return
+ */
+ def isNear(queryPoint: Vector, radius: Double): Boolean = {
+ if (minDist(queryPoint) < radius) {
+ true
+ } else {
+ false
+ }
+ }
+
+ /**
+ * used in error handling when computing minDist to make sure
+ * distMetric is Euclidean or SquaredEuclidean
+ * @param message
+ */
+ case class metricException(message: String) extends Exception(message)
+
+ /**
+ * 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 = {
+ var minDist = 0.0
+ for (i <- 0 to queryPoint.size - 1) {
+ if (queryPoint(i) < center(i) - width(i) / 2) {
+ minDist += math.pow(queryPoint(i) - center(i) + width(i) / 2, 2)
+ } else if (queryPoint(i) > center(i) + width(i) / 2) {
+ minDist += math.pow(queryPoint(i) - center(i) - width(i) / 2, 2)
+ }
+ }
+
+ if (distMetric.isInstanceOf[SquaredEuclideanDistanceMetric]) {
+ minDist
+ } else if (distMetric.isInstanceOf[EuclideanDistanceMetric]) {
+ math.sqrt(minDist)
+ } else{
+ throw metricException(s" Error: metric must be 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 = {
+ var count = 0
+ for (i <- 0 to queryPoint.size - 1) {
+ if (queryPoint(i) > center(i)) {
+ count += Math.pow(2, queryPoint.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 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
+ * @return
+ */
+ def searchNeighborsSiblingQueue(queryPoint: Vector): ListBuffer[Vector]
= {
+ var 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
+ * @param node
+ * @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 {
--- End diff --
Please merge this line and above line.
> Add exact k-nearest-neighbours algorithm to machine learning library
> --------------------------------------------------------------------
>
> Key: FLINK-1745
> URL: https://issues.apache.org/jira/browse/FLINK-1745
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Daniel Blazevski
> Labels: ML, Starter
>
> Even though the k-nearest-neighbours (kNN) [1,2] algorithm is quite trivial
> it is still used as a mean to classify data and to do regression. This issue
> focuses on the implementation of an exact kNN (H-BNLJ, H-BRJ) algorithm as
> proposed in [2].
> Could be a starter task.
> Resources:
> [1] [http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm]
> [2] [https://www.cs.utah.edu/~lifeifei/papers/mrknnj.pdf]
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