AdaptiveKDTrees.jl aims to complement some of the functionalities of NearestNeighbors.jl, namely:
- To allow for the progressive inclusion of new data points to the KDTree; and
- To allow for range searches with specific search radii not only for the query points, but to each point in the dataset.
An example:
using AdaptiveKDTrees.KNN
X = rand(3, 100)
tree = KDTree(X; leaf_size = 10)
ind, dist = nn(tree, rand(3))
inds, dists = knn(tree, rand(3), 10) # 10 element vectors
To add more points to the KD Tree:
add_point!(tree, rand(3)) # returns index
Some other functions:
@show npoints(tree)
# 101
@show tree
# KDTree with 101 points
Similarly:
using AdaptiveKDTrees.RangeSearch
X = rand(3, 100)
tree = RangeTree(X; leaf_size = 10)
add_point!(tree, rand(3))
One may also specify the radius from which each point is visible:
X = rand(3, 100)
R = rand(100)
tree = RangeTree(X, R)
For queries,
r = rand(Float64)
x = rand(3)
inds = find_in_range(
tree, x, r
)
...returns a vector of indices of points with distances to x such that
distance(X[:, i], x) < R[i] + r