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Disable fast path update for minimum distance to hyper rectangle for Chebyshev since it doesn't work #192

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Disable fast path update for minimum distance to hyper rectangle for Chebyshev since it doesn't work #192

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01fa3b0
factor out function that updates new minimum
Jun 19, 2024
7f01e6c
disable fast path in updating min distance for Chebyshev
Jun 19, 2024
9eb5334
tweaks
Jun 19, 2024
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1 change: 0 additions & 1 deletion src/evaluation.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,6 @@
@inline eval_pow(d::Minkowski, s) = abs(s)^d.p

@inline eval_diff(::NonweightedMinowskiMetric, a, b, dim) = a - b
@inline eval_diff(::Chebyshev, ::Any, b, dim) = b
@inline eval_diff(m::WeightedMinowskiMetric, a, b, dim) = m.weights[dim] * (a-b)

function evaluate_maybe_end(d::Distances.UnionMetrics, a::AbstractVector,
Expand Down
13 changes: 13 additions & 0 deletions src/hyperrectangles.jl
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Expand Up @@ -28,3 +28,16 @@ get_max_distance_no_end(m, rec, point) =

get_min_distance_no_end(m, rec, point) =
get_min_max_distance_no_end(distance_function_min, m, rec, point)

@inline function update_new_min(M::Metric, old_min, hyper_rec, p_dim, split_dim, split_val)
@inbounds begin
lo = hyper_rec.mins[split_dim]
hi = hyper_rec.maxes[split_dim]
end
ddiff = distance_function_min(p_dim, hi, lo)
split_diff = abs(p_dim - split_val)
split_diff_pow = eval_pow(M, split_diff)
ddiff_pow = eval_pow(M, ddiff)
diff_tot = eval_diff(M, split_diff_pow, ddiff_pow, split_dim)
return old_min + diff_tot
end
28 changes: 12 additions & 16 deletions src/kd_tree.jl
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Expand Up @@ -173,8 +173,6 @@ function knn_kernel!(tree::KDTree{V},
split_dim = tree.split_dims[index]
p_dim = point[split_dim]
split_val = tree.split_vals[index]
lo = hyper_rec.mins[split_dim]
hi = hyper_rec.maxes[split_dim]
split_diff = p_dim - split_val
M = tree.metric
# Point is to the right of the split value
Expand All @@ -183,21 +181,21 @@ function knn_kernel!(tree::KDTree{V},
far = getleft(index)
hyper_rec_far = HyperRectangle(hyper_rec.mins, @inbounds setindex(hyper_rec.maxes, split_val, split_dim))
hyper_rec_close = HyperRectangle(@inbounds(setindex(hyper_rec.mins, split_val, split_dim)), hyper_rec.maxes)
ddiff = max(zero(eltype(V)), p_dim - hi)
else
close = getleft(index)
far = getright(index)
hyper_rec_far = HyperRectangle(@inbounds(setindex(hyper_rec.mins, split_val, split_dim)), hyper_rec.maxes)
hyper_rec_close = HyperRectangle(hyper_rec.mins, @inbounds setindex(hyper_rec.maxes, split_val, split_dim))
ddiff = max(zero(eltype(V)), lo - p_dim)
end
# Always call closer sub tree
knn_kernel!(tree, close, point, best_idxs, best_dists, min_dist, hyper_rec_close, skip)

split_diff_pow = eval_pow(M, split_diff)
ddiff_pow = eval_pow(M, ddiff)
diff_tot = eval_diff(M, split_diff_pow, ddiff_pow, split_dim)
new_min = eval_reduce(M, min_dist, diff_tot)
if M isa Chebyshev
new_min = get_min_distance_no_end(M, hyper_rec_far, point)
else
new_min = update_new_min(M, min_dist, hyper_rec, p_dim, split_dim, split_val)
end

if new_min < best_dists[1]
knn_kernel!(tree, far, point, best_idxs, best_dists, new_min, hyper_rec_far, skip)
end
Expand Down Expand Up @@ -233,8 +231,6 @@ function inrange_kernel!(tree::KDTree,

split_val = tree.split_vals[index]
split_dim = tree.split_dims[index]
lo = hyper_rec.mins[split_dim]
hi = hyper_rec.maxes[split_dim]
p_dim = point[split_dim]
split_diff = p_dim - split_val
M = tree.metric
Expand All @@ -246,13 +242,11 @@ function inrange_kernel!(tree::KDTree,
far = getleft(index)
hyper_rec_far = HyperRectangle(hyper_rec.mins, @inbounds setindex(hyper_rec.maxes, split_val, split_dim))
hyper_rec_close = HyperRectangle(@inbounds(setindex(hyper_rec.mins, split_val, split_dim)), hyper_rec.maxes)
ddiff = max(zero(p_dim - hi), p_dim - hi)
else # Point is to the left of the split value
close = getleft(index)
far = getright(index)
hyper_rec_far = HyperRectangle(@inbounds(setindex(hyper_rec.mins, split_val, split_dim)), hyper_rec.maxes)
hyper_rec_close = HyperRectangle(hyper_rec.mins, @inbounds setindex(hyper_rec.maxes, split_val, split_dim))
ddiff = max(zero(lo - p_dim), lo - p_dim)
end
# Call closer sub tree
count += inrange_kernel!(tree, close, point, r, idx_in_ball, hyper_rec_close, min_dist)
Expand All @@ -263,10 +257,12 @@ function inrange_kernel!(tree::KDTree,
# It would be interesting to benchmark this on some different data sets.

# Call further sub tree with the new min distance
split_diff_pow = eval_pow(M, split_diff)
ddiff_pow = eval_pow(M, ddiff)
diff_tot = eval_diff(M, split_diff_pow, ddiff_pow, split_dim)
new_min = eval_reduce(M, min_dist, diff_tot)
if M isa Chebyshev
new_min = get_min_distance_no_end(M, hyper_rec_far, point)
else
new_min = update_new_min(M, min_dist, hyper_rec, p_dim, split_dim, split_val)
end

count += inrange_kernel!(tree, far, point, r, idx_in_ball, hyper_rec_far, new_min)
return count
end
24 changes: 24 additions & 0 deletions test/runtests.jl
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Expand Up @@ -80,4 +80,28 @@ using NearestNeighbors: HyperRectangle, get_min_distance_no_end, get_max_distanc
@test get_min_distance_no_end(m, hr, point) ≈ NearestNeighbors.eval_pow(m, m(closest_point, point))
@test get_max_distance_no_end(m, hr, point) ≈ NearestNeighbors.eval_pow(m, m(furthest_point, point))
end

for m in ms
hyper_rec = NearestNeighbors.HyperRectangle{SVector{1, Float32}}(Float32[0.5553872], Float32[0.6169486])
point = [0.5]
min_dist = NearestNeighbors.get_min_distance_no_end(m, hyper_rec, point)
split_dim = 1
split_val = 0.5844354f0
hyper_rec_far = NearestNeighbors.HyperRectangle{SVector{1, Float32}}(Float32[0.5844354], Float32[0.6169486])
new_min = NearestNeighbors.update_new_min(m, min_dist, hyper_rec, point[split_dim], split_dim, split_val)
new_min_true = NearestNeighbors.get_min_distance_no_end(m, hyper_rec_far, point)
@test new_min ≈ new_min_true
end

for m in ms
hyper_rec = NearestNeighbors.HyperRectangle{SVector{2, Float64}}([0.07935189250034036, 0.682552911042077], [0.1619776648454222, 0.8046815005307764])
point = [0.06630748183735935, 0.7541470744398973]
min_dist = NearestNeighbors.get_min_distance_no_end(m, hyper_rec, point)
split_dim = 2
split_val = 0.7388396209627084
hyper_rec_far = NearestNeighbors.HyperRectangle{SVector{2, Float64}}([0.07935189250034036, 0.682552911042077], [0.1619776648454222, 0.7388396209627084])
new_min = NearestNeighbors.update_new_min(m, min_dist, hyper_rec, point[split_dim], split_dim, split_val)
new_min_true = NearestNeighbors.get_min_distance_no_end(m, hyper_rec_far, point)
@test new_min ≈ new_min_true broken = m isa Chebyshev
end
end
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