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export transpose! and ctranspose! #7814

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Nov 11, 2014
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export transpose! and ctranspose! #7814

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23210fa
export transpose! and ctranspose!
Jutho Aug 2, 2014
ba1f731
fix transpose! for sparse
Jutho Nov 11, 2014
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2 changes: 2 additions & 0 deletions base/exports.jl
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Expand Up @@ -632,6 +632,7 @@ export
cond,
condskeel,
cross,
ctranspose!,
ctranspose,
det,
diag,
Expand Down Expand Up @@ -692,6 +693,7 @@ export
svdvals,
sylvester,
trace,
transpose!,
transpose,
tril!,
tril,
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1 change: 1 addition & 0 deletions base/sparse.jl
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Expand Up @@ -4,6 +4,7 @@ module SparseMatrix

importall Base
import Base.NonTupleType, Base.float, Base.Order, Base.Sort.Forward
import Base.transpose!, Base.ctranspose!

export SparseMatrixCSC,
blkdiag, dense, diag, diagm, droptol!, dropzeros!, etree, full,
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8 changes: 4 additions & 4 deletions base/sparse/csparse.jl
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Expand Up @@ -123,7 +123,7 @@ end
# Based on Direct Methods for Sparse Linear Systems, T. A. Davis, SIAM, Philadelphia, Sept. 2006.
# Section 2.5: Transpose
# http://www.cise.ufl.edu/research/sparse/CSparse/
function transpose!{Tv,Ti}(S::SparseMatrixCSC{Tv,Ti}, T::SparseMatrixCSC{Tv,Ti})
function transpose!{Tv,Ti}(T::SparseMatrixCSC{Tv,Ti}, S::SparseMatrixCSC{Tv,Ti})
(mS, nS) = size(S)
nnzS = nnz(S)
colptr_S = S.colptr
Expand Down Expand Up @@ -162,10 +162,10 @@ function transpose{Tv,Ti}(S::SparseMatrixCSC{Tv,Ti})
nzval_T = Array(Tv, nnzS)

T = SparseMatrixCSC(mT, nT, colptr_T, rowval_T, nzval_T)
return transpose!(S, T)
return transpose!(T, S)
end

function ctranspose!{Tv,Ti}(S::SparseMatrixCSC{Tv,Ti}, T::SparseMatrixCSC{Tv,Ti})
function ctranspose!{Tv,Ti}(T::SparseMatrixCSC{Tv,Ti}, S::SparseMatrixCSC{Tv,Ti})
(mS, nS) = size(S)
nnzS = nnz(S)
colptr_S = S.colptr
Expand Down Expand Up @@ -204,7 +204,7 @@ function ctranspose{Tv,Ti}(S::SparseMatrixCSC{Tv,Ti})
nzval_T = Array(Tv, nnzS)

T = SparseMatrixCSC(mT, nT, colptr_T, rowval_T, nzval_T)
return ctranspose!(S, T)
return ctranspose!(T, S)
end

# Compute the elimination tree of A using triu(A) returning the parent vector.
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2 changes: 1 addition & 1 deletion base/sparse/sparsematrix.jl
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Expand Up @@ -2067,8 +2067,8 @@ function sortSparseMatrixCSC!{Tv,Ti}(A::SparseMatrixCSC{Tv,Ti}; sortindices::Sym
if sortindices == :doubletranspose
nB, mB = size(A)
B = SparseMatrixCSC(mB, nB, Array(Ti, nB+1), similar(A.rowval), similar(A.nzval))
transpose!(A, B)
transpose!(B, A)
transpose!(A, B)
return A
end

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4 changes: 4 additions & 0 deletions doc/stdlib/base.rst
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Expand Up @@ -4313,6 +4313,10 @@ Indexing, Assignment, and Concatenation

Like :func:`permutedims`, except the inverse of the given permutation is applied.

.. function:: permutedims!(dest,src,perm)

Permute the dimensions of array ``src`` and store the result in the array ``dest``. ``perm`` is a vector specifying a permutation of length ``ndims(src)``. The preallocated array ``dest`` should have ``size(dest)=size(src)[perm]`` and is completely overwritten. No in-place permutation is supported and unexpected results will happen if `src` and `dest` have overlapping memory regions.

.. function:: squeeze(A, dims)

Remove the dimensions specified by ``dims`` from array ``A``
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8 changes: 8 additions & 0 deletions doc/stdlib/linalg.rst
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Expand Up @@ -552,10 +552,18 @@ Linear algebra functions in Julia are largely implemented by calling functions f

The transposition operator (``.'``).

.. function:: transpose!(dest,src)

Transpose array ``src`` and store the result in the preallocated array ``dest``, which should have a size corresponding to ``(size(src,2),size(src,1))``. No in-place transposition is supported and unexpected results will happen if `src` and `dest` have overlapping memory regions.

.. function:: ctranspose(A)

The conjugate transposition operator (``'``).

.. function:: ctranspose!(dest,src)

Conjugate transpose array ``src`` and store the result in the preallocated array ``dest``, which should have a size corresponding to ``(size(src,2),size(src,1))``. No in-place transposition is supported and unexpected results will happen if `src` and `dest` have overlapping memory regions.

.. function:: eigs(A, [B,]; nev=6, which="LM", tol=0.0, maxiter=1000, sigma=nothing, ritzvec=true, v0=zeros((0,))) -> (d,[v,],nconv,niter,nmult,resid)

``eigs`` computes eigenvalues ``d`` of ``A`` using Lanczos or Arnoldi iterations for real symmetric or general nonsymmetric matrices respectively. If ``B`` is provided, the generalized eigen-problem is solved. The following keyword arguments are supported:
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