fix skipped points

src/brute_tree.jl

@@ -25,7 +25,6 @@ end
 function BruteTree(data::AbstractVecOrMat{T}, metric::Metric = Euclidean();
                    reorder::Bool=false, leafsize::Int=0, storedata::Bool=true) where {T}
     dim = size(data, 1)
-    npoints = size(data, 2)
     BruteTree(copy_svec(T, data, Val(dim)),
               metric, reorder = reorder, leafsize = leafsize, storedata = storedata)
 end

src/hyperspheres.jl

@@ -43,7 +43,6 @@ end
 end
 
 function create_bsphere(data::AbstractVector{V}, metric::Metric, indices::Vector{Int}, low, high, ab) where {V}
-    n_dim = size(data, 1)
     n_points = high - low + 1
     # First find center of all points
     fill!(ab.center, 0.0)

src/knn.jl

@@ -11,15 +11,15 @@ 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]
+    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
         knn_point!(tree, points[i], sortres, dists[i], idxs[i], skip)
     end
@@ -30,6 +30,11 @@ function knn_point!(tree::NNTree{V}, point::AbstractVector{T}, sortres, dist, id
     fill!(idx, -1)
     fill!(dist, typemax(get_T(eltype(V))))
     _knn(tree, point, idx, dist, skip)
+    if skip !== always_false
+        skipped_idxs = findall(==(-1), idx)
+        deleteat!(idx, skipped_idxs)
+        deleteat!(dist, skipped_idxs)
+    end
     sortres && heap_sort_inplace!(dist, idx)
     if tree.reordered
         for j in eachindex(idx)

test/test_knn.jl

@@ -84,4 +84,10 @@ end
         test(data)
         test(view(data, :, :))
     end
+
+    data = [[0.13380863416387367, 0.7845254987714512],[0.1563342025559629, 0.7956456895676077],[0.23320094627474594, 0.9055515160266435]]
+    tree = KDTree(hcat(map(p -> [p[1], p[2]], data)...))
+    nearest, distance = knn(tree, [0.15, 0.8], 3, true, x -> x == 2)
+    @test nearest == [1, 3]
+    @test distance ≈ [0.02239688629947563, 0.13440059522389006]
 end