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Handle dependencies via Pkg extensions (#208)
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| Original file line number | Diff line number | Diff line change |
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| module LinearMapsSparseArraysExt | ||
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| import SparseArrays: sparse, blockdiag, SparseMatrixCSC | ||
| using SparseArrays: AbstractSparseMatrix | ||
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| using LinearAlgebra, LinearMaps | ||
| import LinearMaps: _issymmetric, _ishermitian | ||
| using LinearMaps: WrappedMap, CompositeMap, LinearCombination, ScaledMap, UniformScalingMap, | ||
| AdjointMap, TransposeMap, BlockMap, BlockDiagonalMap, KroneckerMap, KroneckerSumMap, | ||
| VecOrMatMap, AbstractVecOrMatOrQ, MapOrVecOrMat | ||
| using LinearMaps: convert_to_lmaps, _tail, _unsafe_mul! | ||
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| _issymmetric(A::AbstractSparseMatrix) = issymmetric(A) | ||
| _ishermitian(A::AbstractSparseMatrix) = ishermitian(A) | ||
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| # blockdiagonal concatenation via extension of blockdiag | ||
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| """ | ||
| blockdiag(As::Union{LinearMap,AbstractVecOrMatOrQ}...)::BlockDiagonalMap | ||
| Construct a (lazy) representation of the diagonal concatenation of the arguments. | ||
| To avoid fallback to the generic `blockdiag`, there must be a `LinearMap` | ||
| object among the first 8 arguments. | ||
| """ | ||
| blockdiag | ||
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| for k in 1:8 # is 8 sufficient? | ||
| Is = ntuple(n->:($(Symbol(:A, n))::AbstractVecOrMatOrQ), Val(k-1)) | ||
| # yields (:A1, :A2, :A3, ..., :A(k-1)) | ||
| L = :($(Symbol(:A, k))::LinearMap) | ||
| # yields :Ak | ||
| mapargs = ntuple(n ->:($(Symbol(:A, n))), Val(k-1)) | ||
| # yields (:LinearMap(A1), :LinearMap(A2), ..., :LinearMap(A(k-1))) | ||
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| @eval function blockdiag($(Is...), $L, As::MapOrVecOrMat...) | ||
| return BlockDiagonalMap(convert_to_lmaps($(mapargs...))..., | ||
| $(Symbol(:A, k)), | ||
| convert_to_lmaps(As...)...) | ||
| end | ||
| end | ||
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| # conversion to sparse arrays | ||
| # sparse: create sparse matrix representation of LinearMap | ||
| function sparse(A::LinearMap{T}) where {T} | ||
| M, N = size(A) | ||
| rowind = Int[] | ||
| nzval = T[] | ||
| colptr = Vector{Int}(undef, N+1) | ||
| v = fill(zero(T), N) | ||
| Av = Vector{T}(undef, M) | ||
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| @inbounds for i in eachindex(v) | ||
| v[i] = one(T) | ||
| _unsafe_mul!(Av, A, v) | ||
| js = findall(!iszero, Av) | ||
| colptr[i] = length(nzval) + 1 | ||
| if length(js) > 0 | ||
| append!(rowind, js) | ||
| append!(nzval, Av[js]) | ||
| end | ||
| v[i] = zero(T) | ||
| end | ||
| colptr[N+1] = length(nzval) + 1 | ||
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| return SparseMatrixCSC(M, N, colptr, rowind, nzval) | ||
| end | ||
| Base.convert(::Type{SparseMatrixCSC}, A::LinearMap) = sparse(A) | ||
| SparseMatrixCSC(A::LinearMap) = sparse(A) | ||
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| sparse(A::ScaledMap{<:Any, <:Any, <:VecOrMatMap}) = | ||
| A.λ * sparse(A.lmap.lmap) | ||
| sparse(A::WrappedMap) = sparse(A.lmap) | ||
| Base.convert(::Type{SparseMatrixCSC}, A::WrappedMap) = convert(SparseMatrixCSC, A.lmap) | ||
| for (T, t) in ((:AdjointMap, adjoint), (:TransposeMap, transpose)) | ||
| @eval sparse(A::$T) = $t(convert(SparseMatrixCSC, A.lmap)) | ||
| end | ||
| function sparse(ΣA::LinearCombination{<:Any, <:Tuple{Vararg{VecOrMatMap}}}) | ||
| mats = map(A->getfield(A, :lmap), ΣA.maps) | ||
| return sum(sparse, mats) | ||
| end | ||
| function sparse(AB::CompositeMap{<:Any, <:Tuple{VecOrMatMap, VecOrMatMap}}) | ||
| B, A = AB.maps | ||
| return sparse(A.lmap)*sparse(B.lmap) | ||
| end | ||
| function sparse(λA::CompositeMap{<:Any, <:Tuple{VecOrMatMap, UniformScalingMap}}) | ||
| A, J = λA.maps | ||
| return J.λ*sparse(A.lmap) | ||
| end | ||
| function sparse(Aλ::CompositeMap{<:Any, <:Tuple{UniformScalingMap, VecOrMatMap}}) | ||
| J, A = Aλ.maps | ||
| return sparse(A.lmap)*J.λ | ||
| end | ||
| function sparse(A::BlockMap) | ||
| return hvcat( | ||
| A.rows, | ||
| convert(SparseMatrixCSC, first(A.maps)), | ||
| convert.(AbstractArray, _tail(A.maps))... | ||
| ) | ||
| end | ||
| function sparse(A::BlockDiagonalMap) | ||
| return blockdiag(convert.(SparseMatrixCSC, A.maps)...) | ||
| end | ||
| Base.convert(::Type{AbstractMatrix}, A::BlockDiagonalMap) = sparse(A) | ||
| function sparse(A::KroneckerMap) | ||
| return kron( | ||
| convert(SparseMatrixCSC, first(A.maps)), | ||
| convert.(AbstractMatrix, _tail(A.maps))... | ||
| ) | ||
| end | ||
| function sparse(L::KroneckerSumMap) | ||
| A, B = L.maps | ||
| IA = sparse(Diagonal(ones(Bool, size(A, 1)))) | ||
| IB = sparse(Diagonal(ones(Bool, size(B, 1)))) | ||
| return kron(convert(AbstractMatrix, A), IB) + kron(IA, convert(AbstractMatrix, B)) | ||
| end | ||
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| end # module LinearMapsSparseArraysExt |
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| Original file line number | Diff line number | Diff line change |
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| module LinearMapsStatisticsExt | ||
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| import Statistics: mean | ||
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| using LinearMaps | ||
| using LinearMaps: LinearMapTupleOrVector, LinearCombination | ||
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| mean(f::F, maps::LinearMapTupleOrVector) where {F} = sum(f, maps) / length(maps) | ||
| mean(maps::LinearMapTupleOrVector) = mean(identity, maps) | ||
| mean(A::LinearCombination) = mean(A.maps) | ||
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| end # module ChainRulesCore |
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@JuliaRegistrator register
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Registration pull request created: JuliaRegistries/General/86386
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: