use only() instead of first() (#403)

* use only() instead of first() for 1-"vectors" that were for the benefit of Flux

* fix one test that should not have worked as it was

* add missing scalar Sinus constructor

Author: st--

src/basekernels/constant.jl

@@ -73,8 +73,8 @@ end
 
 @functor ConstantKernel
 
-kappa(κ::ConstantKernel, x::Real) = first(κ.c) * one(x)
+kappa(κ::ConstantKernel, x::Real) = only(κ.c) * one(x)
 
 metric(::ConstantKernel) = Delta()
 
-Base.show(io::IO, κ::ConstantKernel) = print(io, "Constant Kernel (c = ", first(κ.c), ")")
+Base.show(io::IO, κ::ConstantKernel) = print(io, "Constant Kernel (c = ", only(κ.c), ")")

src/basekernels/exponential.jl

@@ -137,14 +137,14 @@ end
 
 @functor GammaExponentialKernel
 
-kappa(κ::GammaExponentialKernel, d::Real) = exp(-d^first(κ.γ))
+kappa(κ::GammaExponentialKernel, d::Real) = exp(-d^only(κ.γ))
 
 metric(k::GammaExponentialKernel) = k.metric
 
 iskroncompatible(::GammaExponentialKernel) = true
 
 function Base.show(io::IO, κ::GammaExponentialKernel)
     return print(
-        io, "Gamma Exponential Kernel (γ = ", first(κ.γ), ", metric = ", κ.metric, ")"
+        io, "Gamma Exponential Kernel (γ = ", only(κ.γ), ", metric = ", κ.metric, ")"
     )
 end

src/basekernels/fbm.jl

@@ -28,16 +28,16 @@ function (κ::FBMKernel)(x::AbstractVector{<:Real}, y::AbstractVector{<:Real})
     modX = sum(abs2, x)
     modY = sum(abs2, y)
     modXY = sqeuclidean(x, y)
-    h = first(κ.h)
+    h = only(κ.h)
     return (modX^h + modY^h - modXY^h) / 2
 end
 
 function (κ::FBMKernel)(x::Real, y::Real)
-    return (abs2(x)^first(κ.h) + abs2(y)^first(κ.h) - abs2(x - y)^first(κ.h)) / 2
+    return (abs2(x)^only(κ.h) + abs2(y)^only(κ.h) - abs2(x - y)^only(κ.h)) / 2
 end
 
 function Base.show(io::IO, κ::FBMKernel)
-    return print(io, "Fractional Brownian Motion Kernel (h = ", first(κ.h), ")")
+    return print(io, "Fractional Brownian Motion Kernel (h = ", only(κ.h), ")")
 end
 
 _fbm(modX, modY, modXY, h) = (modX^h + modY^h - modXY^h) / 2

src/basekernels/matern.jl

@@ -34,7 +34,7 @@ MaternKernel(; nu::Real=1.5, ν::Real=nu, metric=Euclidean()) = MaternKernel(ν,
 @functor MaternKernel
 
 @inline function kappa(κ::MaternKernel, d::Real)
-    result = _matern(first(κ.ν), d)
+    result = _matern(only(κ.ν), d)
     return ifelse(iszero(d), one(result), result)
 end
 
@@ -46,7 +46,7 @@ end
 metric(k::MaternKernel) = k.metric
 
 function Base.show(io::IO, κ::MaternKernel)
-    return print(io, "Matern Kernel (ν = ", first(κ.ν), ", metric = ", κ.metric, ")")
+    return print(io, "Matern Kernel (ν = ", only(κ.ν), ", metric = ", κ.metric, ")")
 end
 
 ## Matern12Kernel = ExponentialKernel aliased in exponential.jl

src/basekernels/polynomial.jl

@@ -26,11 +26,11 @@ LinearKernel(; c::Real=0.0) = LinearKernel(c)
 
 @functor LinearKernel
 
-kappa(κ::LinearKernel, xᵀy::Real) = xᵀy + first(κ.c)
+kappa(κ::LinearKernel, xᵀy::Real) = xᵀy + only(κ.c)
 
 metric(::LinearKernel) = DotProduct()
 
-Base.show(io::IO, κ::LinearKernel) = print(io, "Linear Kernel (c = ", first(κ.c), ")")
+Base.show(io::IO, κ::LinearKernel) = print(io, "Linear Kernel (c = ", only(κ.c), ")")
 
 """
     PolynomialKernel(; degree::Int=2, c::Real=0.0)
@@ -53,7 +53,7 @@ struct PolynomialKernel{Tc<:Real} <: SimpleKernel
 
     function PolynomialKernel{Tc}(degree::Int, c::Vector{Tc}) where {Tc}
         @check_args(PolynomialKernel, degree, degree >= one(degree), "degree ≥ 1")
-        @check_args(PolynomialKernel, c, first(c) >= zero(Tc), "c ≥ 0")
+        @check_args(PolynomialKernel, c, only(c) >= zero(Tc), "c ≥ 0")
         return new{Tc}(degree, c)
     end
 end
@@ -68,10 +68,10 @@ function Functors.functor(::Type{<:PolynomialKernel}, x)
     return (c=x.c,), reconstruct_polynomialkernel
 end
 
-kappa(κ::PolynomialKernel, xᵀy::Real) = (xᵀy + first(κ.c))^κ.degree
+kappa(κ::PolynomialKernel, xᵀy::Real) = (xᵀy + only(κ.c))^κ.degree
 
 metric(::PolynomialKernel) = DotProduct()
 
 function Base.show(io::IO, κ::PolynomialKernel)
-    return print(io, "Polynomial Kernel (c = ", first(κ.c), ", degree = ", κ.degree, ")")
+    return print(io, "Polynomial Kernel (c = ", only(κ.c), ", degree = ", κ.degree, ")")
 end

src/basekernels/rational.jl

@@ -32,13 +32,13 @@ end
 @functor RationalKernel
 
 function kappa(κ::RationalKernel, d::Real)
-    return (one(d) + d / first(κ.α))^(-first(κ.α))
+    return (one(d) + d / only(κ.α))^(-only(κ.α))
 end
 
 metric(k::RationalKernel) = k.metric
 
 function Base.show(io::IO, κ::RationalKernel)
-    return print(io, "Rational Kernel (α = ", first(κ.α), ", metric = ", κ.metric, ")")
+    return print(io, "Rational Kernel (α = ", only(κ.α), ", metric = ", κ.metric, ")")
 end
 
 """
@@ -72,18 +72,18 @@ end
 @functor RationalQuadraticKernel
 
 function kappa(κ::RationalQuadraticKernel, d::Real)
-    return (one(d) + d^2 / (2 * first(κ.α)))^(-first(κ.α))
+    return (one(d) + d^2 / (2 * only(κ.α)))^(-only(κ.α))
 end
 function kappa(κ::RationalQuadraticKernel{<:Real,<:Euclidean}, d²::Real)
-    return (one(d²) + d² / (2 * first(κ.α)))^(-first(κ.α))
+    return (one(d²) + d² / (2 * only(κ.α)))^(-only(κ.α))
 end
 
 metric(k::RationalQuadraticKernel) = k.metric
 metric(::RationalQuadraticKernel{<:Real,<:Euclidean}) = SqEuclidean()
 
 function Base.show(io::IO, κ::RationalQuadraticKernel)
     return print(
-        io, "Rational Quadratic Kernel (α = ", first(κ.α), ", metric = ", κ.metric, ")"
+        io, "Rational Quadratic Kernel (α = ", only(κ.α), ", metric = ", κ.metric, ")"
     )
 end
 
@@ -122,7 +122,7 @@ end
 @functor GammaRationalKernel
 
 function kappa(κ::GammaRationalKernel, d::Real)
-    return (one(d) + d^first(κ.γ) / first(κ.α))^(-first(κ.α))
+    return (one(d) + d^only(κ.γ) / only(κ.α))^(-only(κ.α))
 end
 
 metric(k::GammaRationalKernel) = k.metric
@@ -131,9 +131,9 @@ function Base.show(io::IO, κ::GammaRationalKernel)
     return print(
         io,
         "Gamma Rational Kernel (α = ",
-        first(κ.α),
+        only(κ.α),
         ", γ = ",
-        first(κ.γ),
+        only(κ.γ),
         ", metric = ",
         κ.metric,
         ")",

src/distances/sinus.jl

@@ -2,10 +2,12 @@ struct Sinus{T} <: Distances.UnionSemiMetric
     r::Vector{T}
 end
 
+Sinus(r::Real) = Sinus([r])
+
 Distances.parameters(d::Sinus) = d.r
 @inline Distances.eval_op(::Sinus, a::Real, b::Real, p::Real) = abs2(sinpi(a - b) / p)
 @inline (dist::Sinus)(a::AbstractArray, b::AbstractArray) = Distances._evaluate(dist, a, b)
-@inline (dist::Sinus)(a::Number, b::Number) = abs2(sinpi(a - b) / first(dist.r))
+@inline (dist::Sinus)(a::Number, b::Number) = abs2(sinpi(a - b) / only(dist.r))
 
 Distances.result_type(::Sinus{T}, Ta::Type, Tb::Type) where {T} = promote_type(T, Ta, Tb)
 

src/kernels/scaledkernel.jl

@@ -23,7 +23,7 @@ end
 
 @functor ScaledKernel
 
-(k::ScaledKernel)(x, y) = first(k.σ²) * k.kernel(x, y)
+(k::ScaledKernel)(x, y) = only(k.σ²) * k.kernel(x, y)
 
 function kernelmatrix(κ::ScaledKernel, x::AbstractVector, y::AbstractVector)
     return κ.σ² .* kernelmatrix(κ.kernel, x, y)
@@ -75,5 +75,5 @@ Base.show(io::IO, κ::ScaledKernel) = printshifted(io, κ, 0)
 
 function printshifted(io::IO, κ::ScaledKernel, shift::Int)
     printshifted(io, κ.kernel, shift)
-    return print(io, "\n" * ("\t"^(shift + 1)) * "- σ² = $(first(κ.σ²))")
+    return print(io, "\n" * ("\t"^(shift + 1)) * "- σ² = $(only(κ.σ²))")
 end

src/kernels/transformedkernel.jl

@@ -28,10 +28,10 @@ function (k::TransformedKernel{<:SimpleKernel,<:ScaleTransform})(
 end
 
 function _scale(t::ScaleTransform, metric::Euclidean, x, y)
-    return first(t.s) * evaluate(metric, x, y)
+    return only(t.s) * evaluate(metric, x, y)
 end
 function _scale(t::ScaleTransform, metric::Union{SqEuclidean,DotProduct}, x, y)
-    return first(t.s)^2 * evaluate(metric, x, y)
+    return only(t.s)^2 * evaluate(metric, x, y)
 end
 _scale(t::ScaleTransform, metric, x, y) = evaluate(metric, t(x), t(y))
 

src/transform/ardtransform.jl

@@ -32,7 +32,7 @@ end
 
 dim(t::ARDTransform) = length(t.v)
 
-(t::ARDTransform)(x::Real) = first(t.v) * x
+(t::ARDTransform)(x::Real) = only(t.v) * x
 (t::ARDTransform)(x) = t.v .* x
 
 _map(t::ARDTransform, x::AbstractVector{<:Real}) = t.v' .* x