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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_r41526734 --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/nn/QuadTree.scala --- @@ -0,0 +1,305 @@ + +/* + * 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.Vector +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:ListBuffer[Double], maxVec:ListBuffer[Double],distMetric:DistanceMetric){ + var maxPerBox = 20 + + class Node(c:ListBuffer[Double],L:ListBuffer[Double], var children:ListBuffer[Node]) { + + var objects = new ListBuffer[Vector] + + /** for testing purposes only; used in QuadTreeSuite.scala + * + * @return + */ + def getCenterLength(): (ListBuffer[Double], ListBuffer[Double]) = { + (c, L) + } + + 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 < c(i) + L(i) / 2 && obj(i) + radius > c(i) - L(i) / 2) { + count += 1 + } + } + + if (count == obj.size) { + return true + } else { + return 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) < c(i) - L(i) / 2) { + minDist += math.pow(obj(i) - c(i) + L(i) / 2, 2) + } else if (obj(i) > c(i) + L(i) / 2) { + minDist += math.pow(obj(i) - c(i) - L(i) / 2, 2) + } + } + return minDist + } + + def whichChild(obj:Vector):Int = { + + var count = 0 + for (i <- 0 to obj.size - 1){ + if (obj(i) > c(i)) { + count += Math.pow(2,i).toInt + } + } + count + } + + def makeChildren() { + var cBuff = new ListBuffer[ListBuffer[Double]] + cBuff += c + var Childrennodes = new ListBuffer[Node] + val cPart = partitionBox(cBuff,L,L.length) + for (i <- cPart.indices){ + Childrennodes = Childrennodes :+ new Node(cPart(i), L.map(x => x/2.0), null) + + } --- End diff -- Maybe we can add a method such as `multiply(value: Double)` in `Vector` trait. But currently, there is no easy method for this situation. I think that following is best method currently: ```scala val mappedWidth = width match { case SparseVector(size, indices, data) => val newData = data.map(_ / 2.0) SparseVector(size, indices, data) case DenseVector(data) => val newData = data.map(_ / 2.0) DenseVector(data) } > 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)