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Rewrite vectorized unary functions over SparseMatrixCSCs, leveraging …
…higher order functions and multiple dispatch to displace eval. Fixes some apparent type instabilities.
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| ## Unary arithmetic and boolean operators | ||
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| """ | ||
| Helper macro for the unary broadcast definitions below. Takes parent method `fp` and a set | ||
| of desired child methods `fcs`, and builds an expression defining each of the child methods | ||
| such that `fc(A::SparseMatrixCSC) = fp(fc, A)`. | ||
| """ | ||
| macro _enumerate_childmethods(fp, fcs...) | ||
| fcexps = Expr(:block) | ||
| for fc in fcs | ||
| push!(fcexps.args, :( $(esc(fc))(A::SparseMatrixCSC) = $(esc(fp))($(esc(fc)), A) ) ) | ||
| end | ||
| return fcexps | ||
| end | ||
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| # Operations that map zeros to zeros and may map nonzeros to zeros, yielding a sparse matrix | ||
| """ | ||
| Takes unary function `f` that maps zeros to zeros and may map nonzeros to zeros, and returns | ||
| a new `SparseMatrixCSC{TiA,TvB}` `B` generated by applying `f` to each nonzero entry in | ||
| `A` and retaining only the resulting nonzeros. | ||
| """ | ||
| function _broadcast_unary_nz2z_z2z_T{TvA,TiA,TvB}(f::Function, A::SparseMatrixCSC{TvA,TiA}, ::Type{TvB}) | ||
| Bcolptr = Array{TiA}(A.n + 1) | ||
| Browval = Array{TiA}(nnz(A)) | ||
| Bnzval = Array{TvB}(nnz(A)) | ||
| Bk = 1 | ||
| @inbounds for j in 1:A.n | ||
| Bcolptr[j] = Bk | ||
| for Ak in nzrange(A, j) | ||
| x = f(A.nzval[Ak]) | ||
| if x != 0 | ||
| Browval[Bk] = A.rowval[Ak] | ||
| Bnzval[Bk] = x | ||
| Bk += 1 | ||
| end | ||
| end | ||
| end | ||
| Bcolptr[A.n + 1] = Bk | ||
| resize!(Browval, Bk - 1) | ||
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KristofferC
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| resize!(Bnzval, Bk - 1) | ||
| return SparseMatrixCSC(A.m, A.n, Bcolptr, Browval, Bnzval) | ||
| end | ||
| function _broadcast_unary_nz2z_z2z{Tv}(f::Function, A::SparseMatrixCSC{Tv}) | ||
| _broadcast_unary_nz2z_z2z_T(f, A, Tv) | ||
| end | ||
| @_enumerate_childmethods(_broadcast_unary_nz2z_z2z, | ||
| sin, sinh, sind, asin, asinh, asind, | ||
| tan, tanh, tand, atan, atanh, atand, | ||
| sinpi, cosc, ceil, floor, trunc, round) | ||
| real(A::SparseMatrixCSC) = copy(A) | ||
| imag{Tv,Ti}(A::SparseMatrixCSC{Tv,Ti}) = spzeros(Tv, Ti, A.m, A.n) | ||
| real{TTv}(A::SparseMatrixCSC{Complex{TTv}}) = _broadcast_unary_nz2z_z2z_T(real, A, TTv) | ||
| imag{TTv}(A::SparseMatrixCSC{Complex{TTv}}) = _broadcast_unary_nz2z_z2z_T(imag, A, TTv) | ||
| ceil{To}(::Type{To}, A::SparseMatrixCSC) = _broadcast_unary_nz2z_z2z_T(ceil, A, To) | ||
| floor{To}(::Type{To}, A::SparseMatrixCSC) = _broadcast_unary_nz2z_z2z_T(floor, A, To) | ||
| trunc{To}(::Type{To}, A::SparseMatrixCSC) = _broadcast_unary_nz2z_z2z_T(trunc, A, To) | ||
| round{To}(::Type{To}, A::SparseMatrixCSC) = _broadcast_unary_nz2z_z2z_T(round, A, To) | ||
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| # Operations that map zeros to zeros and map nonzeros to nonzeros, yielding a sparse matrix | ||
| """ | ||
| Takes unary function `f` that maps zeros to zeros and nonzeros to nonzeros, and returns a | ||
| new `SparseMatrixCSC{TiA,TvB}` `B` generated by applying `f` to each nonzero entry in `A`. | ||
| """ | ||
| function _broadcast_unary_nz2nz_z2z_T{TvA,TiA,TvB}(f::Function, A::SparseMatrixCSC{TvA,TiA}, ::Type{TvB}) | ||
| Bcolptr = Vector{TiA}(A.n + 1) | ||
| Browval = Vector{TiA}(nnz(A)) | ||
| Bnzval = Vector{TvB}(nnz(A)) | ||
| copy!(Bcolptr, 1, A.colptr, 1, A.n + 1) | ||
| copy!(Browval, 1, A.rowval, 1, nnz(A)) | ||
| @inbounds @simd for k in 1:nnz(A) | ||
| Bnzval[k] = f(A.nzval[k]) | ||
| end | ||
| return SparseMatrixCSC(A.m, A.n, Bcolptr, Browval, Bnzval) | ||
| end | ||
| function _broadcast_unary_nz2nz_z2z{Tv}(f::Function, A::SparseMatrixCSC{Tv}) | ||
| _broadcast_unary_nz2nz_z2z_T(f, A, Tv) | ||
| end | ||
| @_enumerate_childmethods(_broadcast_unary_nz2nz_z2z, | ||
| log1p, expm1, abs, abs2, conj) | ||
| abs2{TTv}(A::SparseMatrixCSC{Complex{TTv}}) = _broadcast_unary_nz2nz_z2z_T(abs2, A, TTv) | ||
| abs{TTv}(A::SparseMatrixCSC{Complex{TTv}}) = _broadcast_unary_nz2nz_z2z_T(abs, A, TTv) | ||
| abs{TTv<:Integer}(A::SparseMatrixCSC{Complex{TTv}}) = _broadcast_unary_nz2nz_z2z_T(abs, A, Float64) | ||
| abs{TTv<:BigInt}(A::SparseMatrixCSC{Complex{TTv}}) = _broadcast_unary_nz2nz_z2z_T(abs, A, BigFloat) | ||
| function conj!(A::SparseMatrixCSC) | ||
| @inbounds @simd for k in 1:nnz(A) | ||
| A.nzval[k] = conj(A.nzval[k]) | ||
| end | ||
| return A | ||
| end | ||
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| # Operations that map both zeros and nonzeros to zeros, yielding a dense matrix | ||
| """ | ||
| Takes unary function `f` that maps both zeros and nonzeros to nonzeros, and returns a new | ||
| `Matrix{TvB}` `B` effectively generated by applying `f` to every entry in `A`. | ||
| """ | ||
| function _broadcast_unary_nz2nz_z2nz{Tv}(f::Function, A::SparseMatrixCSC{Tv}) | ||
| B = fill(f(zero(Tv)), size(A)) | ||
| @inbounds for j in 1:A.n | ||
| for k in nzrange(A, j) | ||
| i = A.rowval[k] | ||
| x = A.nzval[k] | ||
| B[i,j] = f(x) | ||
| end | ||
| end | ||
| return B | ||
| end | ||
| @_enumerate_childmethods(_broadcast_unary_nz2nz_z2nz, | ||
| log, log2, log10, exp, exp2, exp10, sinc, cospi, | ||
| cos, cosh, cosd, acos, acosd, | ||
| cot, coth, cotd, acot, acotd, | ||
| sec, sech, secd, asech, | ||
| csc, csch, cscd, acsch) | ||
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| ## Broadcasting kernels specialized for returning a SparseMatrixCSC | ||
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Do you know if there is a difference between
resizeanddeleteatwhen they are both used to truncate an array? Will resizing a large array to a small one make the note unused memory available for the GC? Other places in the sparse code usesdeleteatfor this purpose so we should figure out which one is best and use it consistently.