move implementations into type overloading (aka. functor)

README.md

@@ -54,12 +54,14 @@ Each distance corresponds to a *distance type*. You can always compute a certain
 
 ```julia
 r = evaluate(dist, x, y)
+r = dist(x, y)
 ```
 
 Here, dist is an instance of a distance type. For example, the type for Euclidean distance is ``Euclidean`` (more distance types will be introduced in the next section), then you can compute the Euclidean distance between ``x`` and ``y`` as
 
 ```julia
 r = evaluate(Euclidean(), x, y)
+r = Euclidean()(x, y)
 ```
 
 Common distances also come with convenient functions for distance evaluation. For example, you may also compute Euclidean distance between two vectors as below

src/bhattacharyya.jl

@@ -37,13 +37,11 @@ bhattacharyya_coeff(a::T, b::T) where {T <: Number} = throw("Bhattacharyya coeff
 
 
 # Bhattacharyya distance
-evaluate(dist::BhattacharyyaDist, a::AbstractVector{T}, b::AbstractVector{T}) where {T <: Number} = -log(bhattacharyya_coeff(a, b))
-bhattacharyya(a::AbstractVector, b::AbstractVector) = evaluate(BhattacharyyaDist(), a, b)
-evaluate(dist::BhattacharyyaDist, a::T, b::T) where {T <: Number} = throw("Bhattacharyya distance cannot be calculated for scalars")
-bhattacharyya(a::T, b::T) where {T <: Number} = evaluate(BhattacharyyaDist(), a, b)
+(::BhattacharyyaDist)(a::AbstractVector{T}, b::AbstractVector{T}) where {T <: Number} = -log(bhattacharyya_coeff(a, b))
+(::BhattacharyyaDist)(a::T, b::T) where {T <: Number} = throw("Bhattacharyya distance cannot be calculated for scalars")
+bhattacharyya(a, b) = BhattacharyyaDist()(a, b)
 
 # Hellinger distance
-evaluate(dist::HellingerDist, a::AbstractVector{T}, b::AbstractVector{T}) where {T <: Number} = sqrt(1 - bhattacharyya_coeff(a, b))
-hellinger(a::AbstractVector, b::AbstractVector) = evaluate(HellingerDist(), a, b)
-evaluate(dist::HellingerDist, a::T, b::T) where {T <: Number} = throw("Hellinger distance cannot be calculated for scalars")
-hellinger(a::T, b::T) where {T <: Number} = evaluate(HellingerDist(), a, b)
+(::HellingerDist)(a::AbstractVector{T}, b::AbstractVector{T}) where {T <: Number} = sqrt(1 - bhattacharyya_coeff(a, b))
+(::HellingerDist)(a::T, b::T) where {T <: Number} = throw("Hellinger distance cannot be calculated for scalars")
+hellinger(a, b) = HellingerDist()(a, b)

src/bregman.jl

@@ -22,7 +22,7 @@ end
 Bregman(F, ∇) =  Bregman(F, ∇, LinearAlgebra.dot)
 
 # Evaluation fuction 
-function evaluate(dist::Bregman, p::AbstractVector, q::AbstractVector)
+function (dist::Bregman)(p::AbstractVector, q::AbstractVector)
     # Create cache vals.
     FP_val = dist.F(p);
     FQ_val = dist.F(q); 
@@ -45,4 +45,4 @@ function evaluate(dist::Bregman, p::AbstractVector, q::AbstractVector)
 end 
 
 # Convenience function. 
-bregman(F, ∇, x, y; inner = LinearAlgebra.dot) = evaluate(Bregman(F, ∇, inner), x, y)
+bregman(F, ∇, x, y; inner = LinearAlgebra.dot) = Bregman(F, ∇, inner)(x, y)

src/generic.jl

@@ -21,6 +21,7 @@ abstract type SemiMetric <: PreMetric end
 #
 abstract type Metric <: SemiMetric end
 
+evaluate(dist::PreMetric, a, b) = dist(a, b)
 
 # Generic functions
 
@@ -41,7 +42,7 @@ function colwise!(r::AbstractArray, metric::PreMetric, a::AbstractVector, b::Abs
     n = size(b, 2)
     length(r) == n || throw(DimensionMismatch("Incorrect size of r."))
     @inbounds for j = 1:n
-        r[j] = evaluate(metric, a, view(b, :, j))
+        r[j] = metric(a, view(b, :, j))
     end
     r
 end
@@ -50,7 +51,7 @@ function colwise!(r::AbstractArray, metric::PreMetric, a::AbstractMatrix, b::Abs
     n = size(a, 2)
     length(r) == n || throw(DimensionMismatch("Incorrect size of r."))
     @inbounds for j = 1:n
-        r[j] = evaluate(metric, view(a, :, j), b)
+        r[j] = metric(view(a, :, j), b)
     end
     r
 end
@@ -59,7 +60,7 @@ function colwise!(r::AbstractArray, metric::PreMetric, a::AbstractMatrix, b::Abs
     n = get_common_ncols(a, b)
     length(r) == n || throw(DimensionMismatch("Incorrect size of r."))
     @inbounds for j = 1:n
-        r[j] = evaluate(metric, view(a, :, j), view(b, :, j))
+        r[j] = metric(view(a, :, j), view(b, :, j))
     end
     r
 end
@@ -97,7 +98,7 @@ function _pairwise!(r::AbstractMatrix, metric::PreMetric,
     @inbounds for j = 1:size(b, 2)
         bj = view(b, :, j)
         for i = 1:size(a, 2)
-            r[i, j] = evaluate(metric, view(a, :, i), bj)
+            r[i, j] = metric(view(a, :, i), bj)
         end
     end
     r
@@ -109,7 +110,7 @@ function _pairwise!(r::AbstractMatrix, metric::SemiMetric, a::AbstractMatrix)
     @inbounds for j = 1:n
         aj = view(a, :, j)
         for i = (j + 1):n
-            r[i, j] = evaluate(metric, view(a, :, i), aj)
+            r[i, j] = metric(view(a, :, i), aj)
         end
         r[j, j] = 0
         for i = 1:(j - 1)

src/haversine.jl

@@ -12,7 +12,7 @@ end
 
 const VecOrLengthTwoTuple{T} = Union{AbstractVector{T}, NTuple{2, T}}
 
-function evaluate(dist::Haversine, x::VecOrLengthTwoTuple, y::VecOrLengthTwoTuple)
+function (dist::Haversine)(x::VecOrLengthTwoTuple, y::VecOrLengthTwoTuple)
     length(x) == length(y) == 2 || haversine_error()
 
     @inbounds begin
@@ -33,6 +33,6 @@ function evaluate(dist::Haversine, x::VecOrLengthTwoTuple, y::VecOrLengthTwoTupl
     2 * dist.radius * asin( min(√a, one(a)) ) # take care of floating point errors
 end
 
-haversine(x::VecOrLengthTwoTuple, y::VecOrLengthTwoTuple, radius::Real) = evaluate(Haversine(radius), x, y)
+haversine(x::VecOrLengthTwoTuple, y::VecOrLengthTwoTuple, radius::Real) = Haversine(radius)(x, y)
 
 @noinline haversine_error() = throw(ArgumentError("expected both inputs to have length 2 in Haversine distance"))

src/mahalanobis.jl

@@ -13,7 +13,7 @@ result_type(::SqMahalanobis{T}, ::Type, ::Type) where {T} = T
 
 # SqMahalanobis
 
-function evaluate(dist::SqMahalanobis{T}, a::AbstractVector, b::AbstractVector) where {T <: Real}
+function (dist::SqMahalanobis{T})(a::AbstractVector, b::AbstractVector) where {T <: Real}
     if length(a) != length(b)
         throw(DimensionMismatch("first array has length $(length(a)) which does not match the length of the second, $(length(b))."))
     end
@@ -23,7 +23,7 @@ function evaluate(dist::SqMahalanobis{T}, a::AbstractVector, b::AbstractVector)
     return dot(z, Q * z)
 end
 
-sqmahalanobis(a::AbstractVector, b::AbstractVector, Q::AbstractMatrix) = evaluate(SqMahalanobis(Q), a, b)
+sqmahalanobis(a::AbstractVector, b::AbstractVector, Q::AbstractMatrix) = SqMahalanobis(Q)(a, b)
 
 function colwise!(r::AbstractArray, dist::SqMahalanobis{T}, a::AbstractMatrix, b::AbstractMatrix) where {T <: Real}
     Q = dist.qmat
@@ -83,11 +83,11 @@ end
 
 # Mahalanobis
 
-function evaluate(dist::Mahalanobis{T}, a::AbstractVector, b::AbstractVector) where {T <: Real}
-    sqrt(evaluate(SqMahalanobis(dist.qmat), a, b))
+function (dist::Mahalanobis{T})(a::AbstractVector, b::AbstractVector) where {T <: Real}
+    sqrt(SqMahalanobis(dist.qmat)(a, b))
 end
 
-mahalanobis(a::AbstractVector, b::AbstractVector, Q::AbstractMatrix) = evaluate(Mahalanobis(Q), a, b)
+mahalanobis(a::AbstractVector, b::AbstractVector, Q::AbstractMatrix) = Mahalanobis(Q)(a, b)
 
 function colwise!(r::AbstractArray, dist::Mahalanobis{T}, a::AbstractMatrix, b::AbstractMatrix) where {T <: Real}
     sqrt!(colwise!(r, SqMahalanobis(dist.qmat), a, b))