Introduce DiffPt for the covariance function of derivatives

src/diffKernel.jl

@@ -1,4 +1,3 @@
-using OneHotArrays: OneHotVector
 import ForwardDiff as FD
 import LinearAlgebra as LA
 
@@ -33,18 +32,30 @@ function DiffPt(x::T, partials::NTuple{Order,KeyT}) where {T,Order,KeyT}
     return DiffPt{Order,KeyT,T}(x, partials)
 end
 
-partial(func) = func
-function partial(func, idx::Int)
-    return x -> FD.derivative(0) do dx
-        return func(x .+ dx * OneHotVector(idx, length(x)))
+"""
+    tangentCurve(x₀, i::IndexType)
+returns the function (t ↦ x₀ + teᵢ) where eᵢ is the unit vector at index i
+"""
+function tangentCurve(x0::AbstractArray{N,T}, idx::IndexType) where {N, T}
+    return t -> begin
+        x = similar(x0)
+        copyto!(x, x0)
+        x[idx] +=t
+        return x
     end
 end
-function partial(func, partials::Int...)
+function tangentCurve(x0::Number, ::IndexType)
+    return t -> x0 + t
+end
+
+partial(func) = func
+function partial(func, idx::IndexType)
+    return x -> FD.derivative(func ∘ tangentCurve(x, idx), 0)
+end
+function partial(func, partials::IndexType...)
     idx, state = iterate(partials)
     return partial(
-        x -> FD.derivative(0) do dx
-            return func(x .+ dx * OneHotVector(idx, length(x)))
-        end,
+        x -> FD.derivative(func ∘ tangentCurve(x, idx), 0),
         Base.rest(partials, state)...,
     )
 end