Efficient nearest neighbour search in Scala - algorithm

Question

Let this coordinates class with the Euclidean distance,
case class coord(x: Double, y: Double) {
def dist(c: coord) = Math.sqrt( Math.pow(x-c.x, 2) + Math.pow(y-c.y, 2) )
}
and let a grid of coordinates, for instance
val grid = (1 to 25).map {_ => coord(Math.random*5, Math.random*5) }
Then for any given coordinate
val x = coord(Math.random*5, Math.random*5)
the nearest points to x are
val nearest = grid.sortWith( (p,q) => p.dist(x) < q.dist(x) )
so the first three closest are nearest.take(3).
Is there a way to make these calculations more time efficient especially for the case of a grid with one million points ?

Answer 1

I'm not sure if this is helpful (or even stupid), but I thought of this:
You use a sort-function to sort ALL elements in the grid and then pick the first k elements. If you consider a sorting algorithm like recursive merge-sort, you have something like this:
Split collection in half
Recurse on both halves
Merge both sorted halves
Maybe you could optimize such a function for your needs. The merging part normally merges all elements from both halves, but you are only interested in the first k that result from the merging. So you could only merge until you have k elements and ignore the rest.
So in the worst-case, where k >= n (n is the size of the grid) you would still only have the complexity of merge-sort. O(n log n) To be honest I'm not able to determine the complexity of this solution relative to k. (too tired for that at the moment)
Here is an example implementation of that solution (it's definitely not optimal and not generalized):
def minK(seq: IndexedSeq[coord], x: coord, k: Int) = {
val dist = (c: coord) => c.dist(x)
def sort(seq: IndexedSeq[coord]): IndexedSeq[coord] = seq.size match {
case 0 | 1 => seq
case size => {
val (left, right) = seq.splitAt(size / 2)
merge(sort(left), sort(right))
}
}
def merge(left: IndexedSeq[coord], right: IndexedSeq[coord]) = {
val leftF = left.lift
val rightF = right.lift
val builder = IndexedSeq.newBuilder[coord]
#tailrec
def loop(leftIndex: Int = 0, rightIndex: Int = 0): Unit = {
if (leftIndex + rightIndex < k) {
(leftF(leftIndex), rightF(rightIndex)) match {
case (Some(leftCoord), Some(rightCoord)) => {
if (dist(leftCoord) < dist(rightCoord)) {
builder += leftCoord
loop(leftIndex + 1, rightIndex)
} else {
builder += rightCoord
loop(leftIndex, rightIndex + 1)
}
}
case (Some(leftCoord), None) => {
builder += leftCoord
loop(leftIndex + 1, rightIndex)
}
case (None, Some(rightCoord)) => {
builder += rightCoord
loop(leftIndex, rightIndex + 1)
}
case _ =>
}
}
}
loop()
builder.result
}
sort(seq)
}

Answer 2

Profile your code, to see what is costly.
Your way of sorting is already highly inefficient.
Do not recompute distances all the time. That isn't free - most likely your program spends 99% of the time with computing distances (use a profiler to find out!)
Finally, you can use index structures. For Euclidean distance you have probably the largest choice of indexes to accelerate finding the nearest neighbors. There is k-d-tree, but I found the R-tree to be often faster. If you want to play around with these, I recommend ELKI. It's a Java library for data mining (so it should be easy to use from Scala, too), and it has a huge choice of index structures.

Answer 3

This one was quite fun to do.
case class Coord(x: Double, y: Double) {
def dist(c: Coord) = Math.sqrt(Math.pow(x - c.x, 2) + Math.pow(y - c.y, 2))
}
class CoordOrdering(x: Coord) extends Ordering[Coord] {
def compare(a: Coord, b: Coord) = a.dist(x) compare b.dist(x)
}
def top[T](xs: Seq[T], n: Int)(implicit ord: Ordering[T]): Seq[T] = {
// xs is an ordered sequence of n elements. insert returns xs with e inserted
// if it is less than anything currently in the sequence (and in that case,
// the last element is dropped) otherwise returns an unmodifed sequence
def insert[T](xs: Seq[T], e: T)(implicit ord: Ordering[T]): Seq[T] = {
val (l, r) = xs.span(x => ord.lt(x, e))
(l ++ (e +: r)).take(n)
}
xs.drop(n).foldLeft(xs.take(n).sorted)(insert)
}
Minimally tested. Call it like this:
val grid = (1 to 250000).map { _ => Coord(Math.random * 5, Math.random * 5) }
val x = Coord(Math.random * 5, Math.random * 5)
top(grid, 3)(new CoordOrdering(x))
EDIT: It's quite easy to extend this to (pre-)compute the distances just once
val zippedGrid = grid map {_.dist(x)} zip grid
object ZippedCoordOrdering extends Ordering[(Double, Coord)] {
def compare(a:(Double, Coord), b:(Double, Coord)) = a._1 compare b._1
}
top(zippedGrid,3)(ZippedCoordOrdering).unzip._2

Answer 4

Here is an algorithm that makes use of an R-tree data structure. Not useful for the small data set described, but it scales well to a large number of objects.
Use an ordered list whose nodes represent either objects or R-tree bounding boxes. The order is closest first using whatever distance function you want. Maintain the order on insert.
Initialize the list by inserting the bounding boxes in the root node of the R-tree.
To get the next closest object:
(1) Remove the first element from the list.
(2) If it is an object, it is the closest one.
(3) If it is the bounding box of a non-leaf node of the R-tree, insert all the bounding boxes representing children of that node into the list in their proper places according to their distance.
(4) If it is the bounding box of an R-tree leaf node, insert the objects that are children of that node (the objects, not their bounding boxes) according to their distance.
(5) Go back to step (1).
The list will remain pretty short. At the front will be nearby objects that we are interested in, and later nodes in the list will be boxes representing collections of objects that are farther away.

Answer 5

It depends on whether exact or approximation.
As several benchmarks such as http://www.slideshare.net/erikbern/approximate-nearest-neighbor-methods-and-vector-models-nyc-ml-meetup show that approximation is a good solution in terms of efficient.
I wrote ann4s which is a Scala implementation of Annoy
Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data.
Take a look at this repo.