Simplified piecewisepolynomial

Author: theogf

src/basekernels/piecewisepolynomial.jl

@@ -10,12 +10,14 @@ processes are hence v times  mean-square differentiable. The kernel function is:
 where `r` is the Mahalanobis distance mahalanobis(x,y) with `maha` as the metric.
 
 """
-struct PiecewisePolynomialKernel{V, A<:AbstractMatrix{<:Real}} <: BaseKernel
+struct PiecewisePolynomialKernel{V, A<:AbstractMatrix{<:Real}} <: SimpleKernel
     maha::A
+    j::Int64
     function PiecewisePolynomialKernel{V}(maha::AbstractMatrix{<:Real}) where V
         V in (0, 1, 2, 3) || error("Invalid paramter v=$(V). Should be 0, 1, 2 or 3.")
         LinearAlgebra.checksquare(maha)
-        return new{V,typeof(maha)}(maha)
+        j = div(size(maha, 1), 2) + V + 1
+        return new{V,typeof(maha)}(maha, j)
     end
 end
 
@@ -33,75 +35,7 @@ function _piecewisepolynomial(κ::PiecewisePolynomialKernel{V}, r, j) where V
     return max(1 - r, 0)^(j + V) * _f(κ, r, j)
 end
 
-function kappa(
-    κ::PiecewisePolynomialKernel{V},
-    x::AbstractVector{<:Real},
-    y::AbstractVector{<:Real},
-) where {V}
-    r = evaluate(metric(κ), x, y)
-    j = div(size(x, 2), 1) + V + 1
-    return _piecewisepolynomial(κ, r, j)
-end
-
-function kernelmatrix(
-    κ::PiecewisePolynomialKernel{V},
-    X::AbstractMatrix;
-    obsdim::Int = defaultobs
-) where {V}
-    j = div(size(X, feature_dim(obsdim)), 2) + V + 1
-    return map(r->_piecewisepolynomial(κ, r, j), pairwise(metric(κ), X; dims=obsdim))
-end
-
-function kernelmatrix(
-    κ::PiecewisePolynomialKernel{V},
-    X::AbstractMatrix,
-    Y::AbstractMatrix;
-    obsdim::Int = defaultobs
-) where {V}
-    j = div(size(X, feature_dim(obsdim)), 2) + V + 1
-    return map(r->_piecewisepolynomial(κ, r, j), pairwise(metric(κ), X, Y; dims=obsdim))
-end
-
-
-function _kernelmatrix(κ::PiecewisePolynomialKernel{V}, X, Y, obsdim) where {V}
-    j = div(size(X, feature_dim(obsdim)), 2) + V + 1
-    return map(r->_piecewisepolynomial(κ, r, j), pairwise(metric(κ), X, Y; dims=obsdim))
-end
-
-function kernelmatrix!(
-    K::AbstractMatrix,
-    κ::PiecewisePolynomialKernel{V},
-    X::AbstractMatrix;
-    obsdim::Int = defaultobs
-) where {V}
-    @assert obsdim ∈ [1,2] "obsdim should be 1 or 2 (see docs of kernelmatrix))"
-    if !check_dims(K, X, X, feature_dim(obsdim), obsdim)
-        throw(DimensionMismatch(
-            "Dimensions of the target array K $(size(K)) are not consistent with X " *
-            "$(size(X))",
-        ))
-    end
-    j = div(size(X, feature_dim(obsdim)), 2) + V + 1
-    return map!(r->_piecewisepolynomial(κ,r,j), K, pairwise(metric(κ), X; dims=obsdim))
-end
-
-function kernelmatrix!(
-    K::AbstractMatrix,
-    κ::PiecewisePolynomialKernel{V},
-    X::AbstractMatrix,
-    Y::AbstractMatrix;
-    obsdim::Int = defaultobs,
-) where {V}
-    @assert obsdim ∈ [1,2] "obsdim should be 1 or 2 (see docs of kernelmatrix))"
-    if !check_dims(K, X, Y, feature_dim(obsdim), obsdim)
-        throw(DimensionMismatch(
-            "Dimensions $(size(K)) of the target array K are not consistent with X " *
-            "($(size(X))) and Y ($(size(Y)))",
-        ))
-    end
-    j = div(size(X, feature_dim(obsdim)), 2) + V + 1
-    return map!(r->_piecewisepolynomial(κ,r,j), K, pairwise(metric(κ), X, Y; dims=obsdim))
-end
+kappa(κ::PiecewisePolynomialKernel, d::Real) = _piecewisepolynomial(κ, r, κ.j)
 
 metric(κ::PiecewisePolynomialKernel) = Mahalanobis(κ.maha)