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`
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