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From tillrohrmann <...@git.apache.org>
Subject [GitHub] flink pull request: [FLINK-1745] Add exact k-nearest-neighbours al...
Date Tue, 24 Nov 2015 10:01:16 GMT
Github user tillrohrmann commented on a diff in the pull request:

    https://github.com/apache/flink/pull/1220#discussion_r45715550
  
    --- 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 = {
    +
    +      var count = 0
    +      for (i <- 0 to obj.size - 1) {
    +        if (obj(i) > center(i)) {
    +          count += Math.pow(2, obj.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
    +     * @param width
    +     * @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(){
    +    printTreeRecur(root)
    +  }
    +
    +  def printTreeRecur(n:Node){
    +    if(n.children != null) {
    +      for (c <- n.children){
    +        printTreeRecur(c)
    +      }
    +    }else{
    +      println("printing tree: n.objects " + n.objects)
    +    }
    +  }
    +
    +  /**
    +   * Recursively adds an object to the tree
    +   * @param obj
    +   */
    +  def insert(obj:Vector){
    +    insertRecur(obj,root)
    +  }
    +
    +  private def insertRecur(obj:Vector,n:Node) {
    +    if(n.children==null) {
    +      if(n.objects.length < maxPerBox )
    +      {
    +        n.objects += obj
    +      }
    +
    +      else{
    +        n.makeChildren()  ///make children nodes; place objects into them and clear node.objects
    +        for (o <- n.objects){
    +          insertRecur(o, n.children(n.whichChild(o)))
    +        }
    +        n.objects.clear()
    +        insertRecur(obj, n.children(n.whichChild(obj)))
    +      }
    +    } else{
    +      insertRecur(obj, n.children(n.whichChild(obj)))
    +    }
    +  }
    +
    +  /** Following methods are 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 obj.  D_s is then used during the
    +    * kNN query to find all points within a radius D_s of obj using searchNeighbors.
    +    * To find the "near" neighbors, a min-heap is defined on the leaf nodes of the quadtree.
    +    * 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(obj),
    +   *
    +   */
    +  private def subOne(tuple: (Double,Node)) = tuple._1
    +
    +  def searchNeighborsSiblingQueue(obj:Vector):ListBuffer[Vector] = {
    +    var ret = new ListBuffer[Vector]
    +    if (root.children == null) {   // edge case when the main box has not been partitioned
at all
    +      root.objects
    +    } else {
    +      var NodeQueue = new PriorityQueue[(Double, Node)]()(Ordering.by(subOne))
    +      searchRecurSiblingQueue(obj, root, NodeQueue)
    +
    +      var count = 0
    +      while (count < maxPerBox) {
    +        val dq = NodeQueue.dequeue()
    +        if (dq._2.objects.nonEmpty) {
    +          ret ++= dq._2.objects
    +          count += dq._2.objects.length
    +        }
    +      }
    +      ret
    +    }
    +}
    +
    +  private def searchRecurSiblingQueue(obj:Vector,n:Node,
    --- End diff --
    
    Could we rename `obj` into something more meaningful. For example `queryPoint` or so?


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