Deprecate use of Mahalanobis distance

Author: devmotion

docs/src/kernels.md

@@ -153,17 +153,19 @@ where $r$ has the same dimension as $x$ and $r_i > 0$.
 
 ## Piecewise Polynomial Kernel
 
-The [`PiecewisePolynomialKernel`](@ref) is defined for $x, x'\in \mathbb{R}^D$, a positive-definite matrix $P \in \mathbb{R}^{D \times D}$, and $V \in \{0,1,2,3\}$ as
+The [`PiecewisePolynomialKernel`](@ref) is defined for $x, x' \in \mathbb{R}^d$ and
+$v \in \{0,1,2,3\}$ as
 ```math
-  k(x,x'; P, V) = \max(1 - \sqrt{x^\top P x'}, 0)^{j + V} f_V(\sqrt{x^\top P x'}, j),
+k(x, x'; v) = \max(1 - \|x - x'\|, 0)^{j + V} f_v(\|x - x'\|, j),
 ```
-where $j = \lfloor \frac{D}{2}\rfloor + V + 1$, and $f_V$ are polynomials defined as follows:
+where $j = \lfloor \frac{d}{2}\rfloor + v + 1$, and $f_v$ are polynomials defined as
+follows:
 ```math
 \begin{aligned}
-    f_0(r, j) &= 1, \\
-    f_1(r, j) &= 1 + (j + 1) r, \\
-    f_2(r, j) &= 1 + (j + 2) r + ((j^2 + 4j + 3) / 3) r^2, \\
-    f_3(r, j) &= 1 + (j + 3) r + ((6 j^2 + 36j + 45) / 15) r^2 + ((j^3 + 9 j^2 + 23j + 15) / 15) r^3.
+f_0(r, j) &= 1, \\
+f_1(r, j) &= 1 + (j + 1) r, \\
+f_2(r, j) &= 1 + (j + 2) r + ((j^2 + 4j + 3) / 3) r^2, \\
+f_3(r, j) &= 1 + (j + 3) r + ((6 j^2 + 36j + 45) / 15) r^2 + ((j^3 + 9 j^2 + 23j + 15) / 15) r^3.
 \end{aligned}
 ```
 

src/KernelFunctions.jl

@@ -31,7 +31,7 @@ export FBMKernel
 export MaternKernel, Matern12Kernel, Matern32Kernel, Matern52Kernel
 export LinearKernel, PolynomialKernel
 export RationalQuadraticKernel, GammaRationalQuadraticKernel
-export MahalanobisKernel, GaborKernel, PiecewisePolynomialKernel
+export GaborKernel, PiecewisePolynomialKernel
 export PeriodicKernel, NeuralNetworkKernel
 export KernelSum, KernelProduct
 export TransformedKernel, ScaledKernel
@@ -84,7 +84,6 @@ include(joinpath("basekernels", "exponential.jl"))
 include(joinpath("basekernels", "exponentiated.jl"))
 include(joinpath("basekernels", "fbm.jl"))
 include(joinpath("basekernels", "gabor.jl"))
-include(joinpath("basekernels", "maha.jl"))
 include(joinpath("basekernels", "matern.jl"))
 include(joinpath("basekernels", "nn.jl"))
 include(joinpath("basekernels", "periodic.jl"))
@@ -112,6 +111,8 @@ include("zygote_adjoints.jl")
 
 include("test_utils.jl")
 
+include("deprecated.jl")
+
 function __init__()
     @require Kronecker="2c470bb0-bcc8-11e8-3dad-c9649493f05e" begin
         include(joinpath("matrix", "kernelkroneckermat.jl"))

src/basekernels/maha.jl

@@ -1,48 +1,60 @@
 """
-    PiecewisePolynomialKernel{V}(maha::AbstractMatrix)
+    PiecewisePolynomialKernel(; v::Int=0, d::Int)
+    PiecewisePolynomialKernel{v}(d::Int)
 
-Piecewise Polynomial covariance function with compact support, V = 0,1,2,3.
-The kernel functions are 2V times continuously differentiable and the corresponding
-processes are hence V times mean-square differentiable. The kernel function is:
+Piecewise polynomial kernel with compact support.
+
+The kernel is defined for ``x, x' \\in \\mathbb{R}^d`` and ``v \\in \\{0,1,2,3\\}`` as
+```math
+k(x, x'; v) = \\max(1 - \\|x - x'\\|, 0)^{j + V} f_v(\\|x - x'\\|, j),
+```
+where ``j = \\lfloor \\frac{d}{2}\\rfloor + v + 1``, and ``f_v`` are polynomials defined as
+follows:
 ```math
-    κ(x, y) = max(1 - r, 0)^(j + V) * f(r, j) with j = floor(D / 2) + V + 1
+\\begin{aligned}
+f_0(r, j) &= 1, \\\\
+f_1(r, j) &= 1 + (j + 1) r, \\\\
+f_2(r, j) &= 1 + (j + 2) r + ((j^2 + 4j + 3) / 3) r^2, \\\\
+f_3(r, j) &= 1 + (j + 3) r + ((6 j^2 + 36j + 45) / 15) r^2 + ((j^3 + 9 j^2 + 23j + 15) / 15) r^3.
+\\end{aligned}
 ```
-where `r` is the Mahalanobis distance mahalanobis(x,y) with `maha` as the metric.
+
+The kernel is ``2v`` times continuously differentiable and the corresponding Gaussian
+process is hence ``v`` times mean-square differentiable.
 """
-struct PiecewisePolynomialKernel{V, A<:AbstractMatrix{<:Real}} <: SimpleKernel
-    maha::A
+struct PiecewisePolynomialKernel{V} <: SimpleKernel
     j::Int
-    function PiecewisePolynomialKernel{V}(maha::AbstractMatrix{<:Real}) where V
+    function PiecewisePolynomialKernel{V}(d::Int) where V
         V in (0, 1, 2, 3) || error("Invalid parameter V=$(V). Should be 0, 1, 2 or 3.")
-        LinearAlgebra.checksquare(maha)
-        j = div(size(maha, 1), 2) + V + 1
-        return new{V,typeof(maha)}(maha, j)
+        d > 0 || error("number of dimensions has to be positive")
+        j = div(d, 2) + V + 1
+        return new{V}(j)
     end
 end
 
-function PiecewisePolynomialKernel(;v::Integer=0, maha::AbstractMatrix{<:Real})
-    return PiecewisePolynomialKernel{v}(maha)
-end
-
-# Have to reconstruct the type parameter
-# See also https://github.com/FluxML/Functors.jl/issues/3#issuecomment-626747663
-function Functors.functor(::Type{<:PiecewisePolynomialKernel{V}}, x) where V
-    function reconstruct_kernel(xs)
-        return PiecewisePolynomialKernel{V}(xs.maha)
+# TODO: remove `maha` keyword argument in next breaking release
+function PiecewisePolynomialKernel(; v::Int=0, maha=nothing, d::Int=-1)
+    if maha !== nothing
+        Base.depwarn("keyword argument `maha` is deprecated", :PiecewisePolynomialKernel)
+        d = size(maha, 1)
+        return transform(PiecewisePolynomialKernel{v}(d), cholesky(maha).U)
+    else
+        return PiecewisePolynomialKernel{v}(d)
     end
-    return (maha = x.maha,), reconstruct_kernel
 end
 
-_f(κ::PiecewisePolynomialKernel{0}, r, j) = 1
-_f(κ::PiecewisePolynomialKernel{1}, r, j) = 1 + (j + 1) * r
-_f(κ::PiecewisePolynomialKernel{2}, r, j) = 1 + (j + 2) * r + (j^2 + 4 * j + 3) / 3 * r.^2
-_f(κ::PiecewisePolynomialKernel{3}, r, j) = 1 + (j + 3) * r +
+_f(::PiecewisePolynomialKernel{1}, r, j) = 1 + (j + 1) * r
+_f(::PiecewisePolynomialKernel{2}, r, j) = 1 + (j + 2) * r + (j^2 + 4 * j + 3) / 3 * r.^2
+_f(::PiecewisePolynomialKernel{3}, r, j) = 1 + (j + 3) * r +
     (6 * j^2 + 36j + 45) / 15 * r.^2 + (j^3 + 9 * j^2 + 23j + 15) / 15 * r.^3
 
-kappa(κ::PiecewisePolynomialKernel{V}, r) where V = max(1 - r, 0)^(κ.j + V) * _f(κ, r, κ.j)
+kappa(κ::PiecewisePolynomialKernel{0}, r) = max(1 - r, 0)^κ.j
+function kappa(κ::PiecewisePolynomialKernel{V}, r) where V
+    return max(1 - r, 0)^(κ.j + V) * _f(κ, r, κ.j)
+end
 
-metric(κ::PiecewisePolynomialKernel) = Mahalanobis(κ.maha)
+metric(::PiecewisePolynomialKernel) = Euclidean()
 
 function Base.show(io::IO, κ::PiecewisePolynomialKernel{V}) where {V}
-    print(io, "Piecewise Polynomial Kernel (v = ", V, ", size(maha) = ", size(κ.maha), ")")
+    print(io, "Piecewise Polynomial Kernel (v = ", V, ", ⌊d/2⌋ = ", κ.j - 1 - V , ")")
 end

src/basekernels/piecewisepolynomial.jl

@@ -0,0 +1,5 @@
+# TODO: remove tests when removed
+@deprecate MahalanobisKernel(; P::AbstractMatrix{<:Real}) transform(SqExponentialKernel(), cholesky(P).U)
+
+# TODO: remove keyword argument `maha` when removed
+@deprecate PiecewisePolynomialKernel{V}(A::AbstractMatrix{<:Real}) where V transform(PiecewisePolynomialKernel{V}(size(A, 1)), cholesky(A).U)