[
https://issues.apache.org/jira/browse/FLINK-1745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15289048#comment-15289048
]
ASF GitHub Bot commented on FLINK-1745:
---------------------------------------
Github user danielblazevski commented on a diff in the pull request:
https://github.com/apache/flink/pull/1220#discussion_r63712033
--- 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
--- End diff --
changed to to `scala.math`
> 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]
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
