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Add custom EuclideanDistance #1120

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Add custom EuclideanDistance #1120

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c94b0b6
Add custom 'EuclideanDistance'
eliascarv Oct 25, 2024
35cce99
Remove unused code
eliascarv Oct 25, 2024
bd18907
Update imports
eliascarv Oct 25, 2024
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8 changes: 4 additions & 4 deletions src/Meshes.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -15,9 +15,8 @@ using Random
using Bessels: gamma
using Unitful: AbstractQuantity, numtype
using StatsBase: AbstractWeights, Weights, quantile
using Distances: PreMetric, Euclidean, Mahalanobis
using Distances: PreMetric, MinkowskiMetric, Euclidean, Mahalanobis
using Distances: Haversine, SphericalAngle
using Distances: evaluate, result_type
using Rotations: Rotation, QuatRotation, Angle2d
using Rotations: rotation_between
using CoordRefSystems: Basic, Projected, Geographic
Expand All @@ -37,8 +36,8 @@ import Base: ==, !
import Base: +, -, *
import Base: <, >, ≤, ≥
import StatsBase: sample
import Distances: evaluate
import NearestNeighbors: MinkowskiMetric
import Distances: evaluate, result_type
using NearestNeighbors: eval_pow, eval_diff

# Transforms API
import TransformsBase: Transform, →
Expand Down Expand Up @@ -558,6 +557,7 @@ export
measurematrix,
adjacencymatrix,
atol,
EuclideanDistance,

# visualization
viz,
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45 changes: 45 additions & 0 deletions src/distances.jl
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Expand Up @@ -5,6 +5,51 @@
# flip arguments so that points always come first
evaluate(d::PreMetric, g::Geometry, p::Point) = evaluate(d, p, g)

# ----------
# EUCLIDEAN
# ----------

struct EuclideanDistance <: MinkowskiMetric end

# Distances.jl interface
result_type(::EuclideanDistance, ::Type{T₁}, ::Type{T₂}) where {T₁,T₂} = float(promote_type(T₁, T₂))

@propagate_inbounds function (d::EuclideanDistance)(a, b)
@boundscheck if length(a) ≠ length(b)
throw(
DimensionMismatch(
"first collection has length $(length(a)) which does not match the length of the second, $(length(b))."
)
)
end
norm((bᵢ - aᵢ) for (aᵢ, bᵢ) in zip(a, b))
end

# implementations for geometries
(d::EuclideanDistance)(p₁::Point, p₂::Point) = norm(p₂ - p₁)

function (d::EuclideanDistance)(p::Point, l::Line)
a, b = l(0), l(1)
u = p - a
v = b - a
α = (u ⋅ v) / (v ⋅ v)
norm(u - α * v)
end

(d::EuclideanDistance)(l::Line, p::Point) = d(p, l)

function (d::EuclideanDistance)(l₁::Line, l₂::Line)
λ₁, λ₂, r, rₐ = intersectparameters(l₁(0), l₁(1), l₂(0), l₂(1))

if (r == rₐ == 2) || (r == rₐ == 1) # lines intersect or are colinear
zero(result_type(d, lentype(l₁), lentype(l₂)))
elseif (r == 1) && (rₐ == 2) # lines are parallel
d(l₁(0), l₂)
else # get distance between closest points on each line
d(l₁(λ₁), l₂(λ₂))
end
end

"""
evaluate(distance::Euclidean, point, line)

Expand Down