# NearestNeighbors.jl

`NearestNeighbors.jl`

is a package written in Julia to perform nearest neighbor searches.

## Creating a tree

There are currently three types of trees available:

`BruteTree`

: Not actually a tree. It linearly searches all points in a brute force fashion. Works with any`Metric`

.`KDTree`

: In a kd tree the points are recursively split into groups using hyper-planes. Therefore a`KDTree`

only works with axis aligned metrics which are:`Euclidean`

,`Chebyshev`

,`Minkowski`

and`Cityblock`

.`BallTree`

: Points are recursively split into groups bounded by hyper-spheres. Works with any`Metric`

.

These trees are created with the following syntax:

```
BruteTree(data, metric; leafsize, reorder)
KDTree(data, metric; leafsize, reorder)
BallTree(data, metric; leafsize, reorder)
```

`data`

: The data, i.e., the points to build up the tree from. It can either be- a matrix of size
`nd × np`

with the points to insert in the tree where`nd`

is the dimensionality of the points and`np`

is the number of points - a vector of vectors with fixed dimensionality,
`nd`

, which must be part of the type. Specifically,`data`

should be a`Vector{V}`

, where`V`

is itself a subtype of an`AbstractVector`

and such that`eltype(V)`

and`length(V)`

are defined. (For example, with 3D points,`V = SVector{3, Float64}`

works because`eltype(V) = Float64`

and`length(V) = 3`

are defined in`V`

.)

- a matrix of size
`metric`

: The metric to use, defaults to`Euclidean`

. This is one of the`Metric`

types defined in the`Distances.jl`

packages. It is possible to define your own metrics by simply creating new types that are subtypes of`Metric`

.`leafsize`

(keyword argument): Determines at what number of points to stop splitting the tree further. There is a trade-off between traversing the tree and having to evaluate the metric function for increasing number of points.`reorder`

(keyword argument): While building the tree this will put points close in distance close in memory since this helps with cache locality. In this case, a copy of the original data will be made so that the original data is left unmodified. This can have a significant impact on performance and is by default set to`true`

.

All trees in `NearestNeighbors.jl`

are static which means that points can not be added or removed from an already created tree.

Here are a few examples of creating trees:

```
using NearestNeighbors
data = rand(3, 10^4)
# Create trees
kdtree = KDTree(data; leafsize = 10)
balltree = BallTree(data, Minkowski(3.5); reorder = false)
brutetree = BruteTree(data)
```

## k Nearest Neighbor (kNN) searches

A kNN search finds the `k`

nearest neighbors to given point(s).
This is done with the method:

`knn(tree, points, k, sortres = false, skip = always_false) -> idxs, dists`

`tree`

: The tree instance`points`

: A vector or matrix of points to find the`k`

nearest neighbors to. If`points`

is a vector of numbers then this represents a single point, if`points`

is a matrix then the`k`

nearest neighbors to each point (column) will be computed.`points`

can also be a vector of other vectors where each element in the outer vector is considered a point.`sortres`

(optional): Determines if the results should be sorted before returning. In this case the results will be sorted in order of increasing distance to the point.`skip`

(optional): A predicate to determine if a given point should be skipped, for example if iterating over points and a point has already been visited.

It is generally better for performance to query once with a large number of points than to query multiple times with one point per query.

As a convenience, if you only want the closest nearest neighbor, you can call `nn`

instead for a cleaner result:

`nn(tree, points, skip = always_false) -> idxs, dists`

Some examples:

```
using NearestNeighbors
data = rand(3, 10^4)
k = 3
point = rand(3)
kdtree = KDTree(data)
idxs, dists = knn(kdtree, point, k, true)
idxs
# 3-element Array{Int64,1}:
# 4683
# 6119
# 3278
dists
# 3-element Array{Float64,1}:
# 0.039032201026256215
# 0.04134193711411951
# 0.042974090446474184
# Multiple points
points = rand(3, 4);
idxs, dists = knn(kdtree, points, k, true);
idxs
# 4-element Array{Array{Int64,1},1}:
# [3330, 4072, 2696]
# [1825, 7799, 8358]
# [3497, 2169, 3737]
# [1845, 9796, 2908]
# dists
# 4-element Array{Array{Float64,1},1}:
# [0.0298932, 0.0327349, 0.0365979]
# [0.0348751, 0.0498355, 0.0506802]
# [0.0318547, 0.037291, 0.0421208]
# [0.03321, 0.0360935, 0.0411951]
# Static vectors
v = @SVector[0.5, 0.3, 0.2];
idxs, dists = knn(kdtree, v, k, true);
idxs
# 3-element Array{Int64,1}:
# 842
# 3075
# 3046
dists
# 3-element Array{Float64,1}:
# 0.04178677766255837
# 0.04556078331418939
# 0.049967238112417205
```

## Range searches

A range search finds all neighbors within the range `r`

of given point(s).
This is done with the method:

`inrange(tree, points, r, sortres = false) -> idxs`

Note that for performance reasons the distances are not returned. The arguments to `inrange`

are the same as for `knn`

except that `sortres`

just sorts the returned index vector.

An example:

```
using NearestNeighbors
data = rand(3, 10^4)
r = 0.05
point = rand(3)
balltree = BallTree(data)
idxs = inrange(balltree, point, r, true)
# 4-element Array{Int64,1}:
# 317
# 983
# 4577
# 8675
neighborscount = inrangecount(balltree, point, r, true) # if you were just interested in the number of points, this function will count them without allocating arrays for the indexes
```

## Using on-disk data sets

By default, the trees store a copy of the `data`

provided during construction. This is problematic in case you want to work on data sets which are larger than available memory, thus wanting to `mmap`

the data or want to store the data in a different place, outside the tree.

`DataFreeTree`

can be used to strip a constructed tree of its data field and re-link it with that data at a later stage. An example of using a large on-disk data set looks like this:

```
using Mmap
ndim = 2; ndata = 10_000_000_000
data = Mmap.mmap(datafilename, Matrix{Float32}, (ndim, ndata))
data[:] = rand(Float32, ndim, ndata) # create example data
dftree = DataFreeTree(KDTree, data)
```

`dftree`

now only stores the indexing data structures. It can be passed around, saved and reloaded independently of `data`

.

To perform look-ups, `dftree`

is re-linked to the underlying data:

```
tree = injectdata(dftree, data) # yields a KDTree
knn(tree, data[:,1], 3) # perform operations as usual
```

## Author

Kristoffer Carlsson - @KristofferC - kristoffer.carlsson@chalmers.se