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https://issues.apache.org/jira/browse/FLINK-1745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15024105#comment-15024105
]
ASF GitHub Bot commented on FLINK-1745:
---------------------------------------
Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/1220#discussion_r45713377
--- 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 = {
--- End diff --
What does this method do?
> 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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