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Improve convert definitions (#179)
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* Improve `convert` definitions

* Add tests

* Bump version number
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@devmotion
devmotion authored Sep 25, 2023
1 parent 53e64ac commit a9924c6
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Showing 7 changed files with 72 additions and 24 deletions.
2 changes: 1 addition & 1 deletion Project.toml
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@@ -1,6 +1,6 @@
name = "PDMats"
uuid = "90014a1f-27ba-587c-ab20-58faa44d9150"
version = "0.11.18"
version = "0.11.19"

[deps]
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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4 changes: 4 additions & 0 deletions src/generics.jl
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Expand Up @@ -4,6 +4,10 @@
AbstractPDMat(A::AbstractPDMat) = A
AbstractPDMat(A::AbstractMatrix) = PDMat(A)

## convert
Base.convert(::Type{AbstractMatrix{T}}, a::AbstractPDMat) where {T<:Real} = convert(AbstractPDMat{T}, a)
Base.convert(::Type{AbstractArray{T}}, a::AbstractPDMat) where {T<:Real} = convert(AbstractMatrix{T}, a)

## arithmetics

pdadd!(r::Matrix, a::Matrix, b::AbstractPDMat{T}) where {T<:Real} = pdadd!(r, a, b, one(T))
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8 changes: 6 additions & 2 deletions src/pdiagmat.jl
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Expand Up @@ -13,8 +13,12 @@ AbstractPDMat(A::Symmetric{<:Real,<:Diagonal{<:Real}}) = PDiagMat(A.data.diag)
AbstractPDMat(A::Hermitian{<:Real,<:Diagonal{<:Real}}) = PDiagMat(A.data.diag)

### Conversion
Base.convert(::Type{PDiagMat{T}}, a::PDiagMat) where {T<:Real} = PDiagMat(convert(AbstractArray{T}, a.diag))
Base.convert(::Type{AbstractArray{T}}, a::PDiagMat) where {T<:Real} = convert(PDiagMat{T}, a)
Base.convert(::Type{PDiagMat{T}}, a::PDiagMat{T}) where {T<:Real} = a
function Base.convert(::Type{PDiagMat{T}}, a::PDiagMat) where {T<:Real}
diag = convert(AbstractVector{T}, a.diag)
return PDiagMat{T,typeof(diag)}(a.dim, diag)
end
Base.convert(::Type{AbstractPDMat{T}}, a::PDiagMat) where {T<:Real} = convert(PDiagMat{T}, a)

### Basics

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11 changes: 8 additions & 3 deletions src/pdmat.jl
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Expand Up @@ -22,9 +22,14 @@ PDMat(fac::Cholesky) = PDMat(AbstractMatrix(fac), fac)
AbstractPDMat(A::Cholesky) = PDMat(A)

### Conversion
Base.convert(::Type{PDMat{T}}, a::PDMat) where {T<:Real} = PDMat(convert(AbstractArray{T}, a.mat))
Base.convert(::Type{AbstractArray{T}}, a::PDMat) where {T<:Real} = convert(PDMat{T}, a)
Base.convert(::Type{AbstractArray{T}}, a::PDMat{T}) where {T<:Real} = a
Base.convert(::Type{PDMat{T}}, a::PDMat{T}) where {T<:Real} = a
function Base.convert(::Type{PDMat{T}}, a::PDMat) where {T<:Real}
chol = convert(Cholesky{T}, a.chol)
S = typeof(chol.factors)
mat = convert(S, a.mat)
return PDMat{T,S}(size(mat, 1), mat, chol)
end
Base.convert(::Type{AbstractPDMat{T}}, a::PDMat) where {T<:Real} = convert(PDMat{T}, a)

### Basics

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10 changes: 9 additions & 1 deletion src/pdsparsemat.jl
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Expand Up @@ -24,7 +24,15 @@ AbstractPDMat(A::SparseMatrixCSC) = PDSparseMat(A)
AbstractPDMat(A::CholTypeSparse) = PDSparseMat(A)

### Conversion
Base.convert(::Type{PDSparseMat{T}}, a::PDSparseMat) where {T<:Real} = PDSparseMat(convert(SparseMatrixCSC{T}, a.mat))
Base.convert(::Type{PDSparseMat{T}}, a::PDSparseMat{T}) where {T<:Real} = a
function Base.convert(::Type{PDSparseMat{T}}, a::PDSparseMat) where {T<:Real}
# CholTypeSparse only supports Float64 and ComplexF64 type parameters!
# So there is no point in recomputing `cholesky(mat)` and we just reuse
# the existing Cholesky factorization
mat = convert(AbstractMatrix{T}, a.mat)
return PDSparseMat{T,typeof(mat)}(a.dim, mat, a.chol)
end
Base.convert(::Type{AbstractPDMat{T}}, a::PDSparseMat) where {T<:Real} = convert(PDSparseMat{T}, a)

### Basics

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3 changes: 2 additions & 1 deletion src/scalmat.jl
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Expand Up @@ -7,8 +7,9 @@ struct ScalMat{T<:Real} <: AbstractPDMat{T}
end

### Conversion
Base.convert(::Type{ScalMat{T}}, a::ScalMat{T}) where {T<:Real} = a
Base.convert(::Type{ScalMat{T}}, a::ScalMat) where {T<:Real} = ScalMat(a.dim, T(a.value))
Base.convert(::Type{AbstractArray{T}}, a::ScalMat) where {T<:Real} = convert(ScalMat{T}, a)
Base.convert(::Type{AbstractPDMat{T}}, a::ScalMat) where {T<:Real} = convert(ScalMat{T}, a)

### Basics

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58 changes: 42 additions & 16 deletions test/pdmtypes.jl
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Expand Up @@ -45,23 +45,49 @@ using Test
end

@testset "float type conversions" begin
m = Matrix{Float32}(I, 2, 2)
@test convert(PDMat{Float64}, PDMat(m)).mat == PDMat(convert(Array{Float64}, m)).mat
@test convert(AbstractArray{Float64}, PDMat(m)).mat == PDMat(convert(Array{Float64}, m)).mat
m = ones(Float32,2)
@test convert(PDiagMat{Float64}, PDiagMat(m)).diag == PDiagMat(convert(Array{Float64}, m)).diag
@test convert(AbstractArray{Float64}, PDiagMat(m)).diag == PDiagMat(convert(Array{Float64}, m)).diag
x = one(Float32); d = 4
@test convert(ScalMat{Float64}, ScalMat(d, x)).value == ScalMat(d, convert(Float64, x)).value
@test convert(AbstractArray{Float64}, ScalMat(d, x)).value == ScalMat(d, convert(Float64, x)).value
s = SparseMatrixCSC{Float32}(I, 2, 2)
@test convert(PDSparseMat{Float64}, PDSparseMat(s)).mat == PDSparseMat(convert(SparseMatrixCSC{Float64}, s)).mat
end
for T in (Float32, Float64), S in (Float32, Float64)
A = PDMat(Matrix{T}(I, 2, 2))
for R in (AbstractArray{S}, AbstractMatrix{S}, AbstractPDMat{S}, PDMat{S})
B = @inferred(convert(R, A))
@test B isa PDMat{S}
@test B == A
@test (B === A) === (S === T)
@test (B.mat === A.mat) === (S === T)
@test (B.chol === A.chol) === (S === T)
end

A = PDiagMat(ones(T, 2))
for R in (AbstractArray{S}, AbstractMatrix{S}, AbstractPDMat{S}, PDiagMat{S})
B = @inferred(convert(R, A))
@test B isa PDiagMat{S}
@test B == A
@test (B === A) === (S === T)
@test (B.diag === A.diag) === (S === T)
end

@testset "no-op conversion with correct eltype (#101)" begin
X = PDMat((Y->Y'Y)(randn(Float32, 4, 4)))
@test convert(AbstractArray{Float32}, X) === X
@test convert(AbstractArray{Float64}, X) !== X
A = ScalMat(4, T(1))
for R in (AbstractArray{S}, AbstractMatrix{S}, AbstractPDMat{S}, ScalMat{S})
B = @inferred(convert(R, A))
@test B isa ScalMat{S}
@test B == A
@test (B === A) === (S === T)
@test (B.value === A.value) === (S === T)
end

if HAVE_CHOLMOD
A = PDSparseMat(SparseMatrixCSC{T}(I, 2, 2))
for R in (AbstractArray{S}, AbstractMatrix{S}, AbstractPDMat{S}, PDSparseMat{S})
B = @inferred(convert(R, A))
@test B isa PDSparseMat{S}
@test B == A
@test (B === A) === (S === T)
@test (B.mat === A.mat) === (S === T)
# CholMOD only supports Float64 and ComplexF64 type parameters!
# Hence the Cholesky factorization is reused
@test B.chol === A.chol
end
end
end
end

@testset "type stability of whiten! and unwhiten!" begin
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Registration pull request created: JuliaRegistries/General/92155

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v0.11.19 -m "<description of version>" a9924c675105142a4890c8707283019b3c0e45bf
git push origin v0.11.19

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