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Parallelizing knn and inrange searches #131

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Parallelizing knn and inrange searches #131

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2 changes: 1 addition & 1 deletion Project.toml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
name = "NearestNeighbors"
uuid = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
version = "0.4.9"
version = "0.4.10"

[deps]
Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
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1 change: 1 addition & 0 deletions src/NearestNeighbors.jl
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Expand Up @@ -5,6 +5,7 @@ import Distances: Metric, result_type, eval_reduce, eval_end, eval_op, eval_star

using StaticArrays
import Base.show
using Base.Threads: @threads

export NNTree, BruteTree, KDTree, BallTree, DataFreeTree
export knn, nn, inrange # TODOs? , allpairs, distmat, npairs
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2 changes: 1 addition & 1 deletion src/inrange.jl
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Expand Up @@ -15,7 +15,7 @@ function inrange(tree::NNTree,

idxs = [Vector{Int}() for _ in 1:length(points)]

for i in 1:length(points)
@threads for i in 1:length(points)
inrange_point!(tree, points[i], radius, sortres, idxs[i])
end
return idxs
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8 changes: 4 additions & 4 deletions src/knn.jl
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Expand Up @@ -11,16 +11,16 @@ end
Performs a lookup of the `k` nearest neigbours to the `points` from the data
in the `tree`. If `sortres = true` the result is sorted such that the results are
in the order of increasing distance to the point. `skip` is an optional predicate
to determine if a point that would be returned should be skipped based on its
to determine if a point that would be returned should be skipped based on its
index.
"""
function knn(tree::NNTree{V}, points::Vector{T}, k::Int, sortres=false, skip::F=always_false) where {V, T <: AbstractVector, F<:Function}
check_input(tree, points)
check_k(tree, k)
n_points = length(points)
dists = [Vector{get_T(eltype(V))}(undef, k) for _ in 1:n_points]
idxs = [Vector{Int}(undef, k) for _ in 1:n_points]
for i in 1:n_points
dists = [Vector{get_T(eltype(V))}(undef, k) for _ in 1:n_points]
idxs = [Vector{Int}(undef, k) for _ in 1:n_points]
@threads for i in 1:n_points
knn_point!(tree, points[i], sortres, dists[i], idxs[i], skip)
end
return idxs, dists
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4 changes: 2 additions & 2 deletions test/test_inrange.jl
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@@ -1,7 +1,7 @@
# Does not test leafsize
@testset "inrange" begin
@testset "metric" for metric in [Euclidean()]
@testset "tree type" for TreeType in trees_with_brute
@testset "metric $Metric" for metric in [Euclidean()]
@testset "tree type $TreeType" for TreeType in trees_with_brute
function test(data)
tree = TreeType(data, metric; leafsize=2)
dosort = true
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4 changes: 2 additions & 2 deletions test/test_knn.jl
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Expand Up @@ -3,8 +3,8 @@
import Distances.evaluate

@testset "knn" begin
@testset "metric" for metric in [metrics; WeightedEuclidean(ones(2))]
@testset "tree type" for TreeType in trees_with_brute
@testset "metric $metric" for metric in [metrics; WeightedEuclidean(ones(2))]
@testset "tree type $TreeType" for TreeType in trees_with_brute
function test(data)
tree = TreeType(data, metric; leafsize=2)

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6 changes: 3 additions & 3 deletions test/test_monkey.jl
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Expand Up @@ -3,9 +3,9 @@ import NearestNeighbors.MinkowskiMetric
# some edge case has been missed in the real tests


@testset "metric" for metric in fullmetrics
@testset "tree type" for TreeType in trees_with_brute
@testset "type" for T in (Float32, Float64)
@testset "metric $metric" for metric in fullmetrics
@testset "tree type $TreeType" for TreeType in trees_with_brute
@testset "element type $T" for T in (Float32, Float64)
@testset "knn monkey" begin
# Checks that we find existing point in the tree
# and that it is the closest
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