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view libinterp/corefcn/kron.cc @ 21301:40de9f8f23a6
Use '#include "config.h"' rather than <config.h>.
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hess.cc, hex2num.cc, hook-fcn.cc, input.cc, inv.cc, jit-ir.cc, jit-typeinfo.cc,
jit-util.cc, kron.cc, load-path.cc, load-save.cc, lookup.cc,
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dim-vector.cc, fCColVector.cc, fCDiagMatrix.cc, fCMatrix.cc, fCNDArray.cc,
fCRowVector.cc, fColVector.cc, fDiagMatrix.cc, fMatrix.cc, fNDArray.cc,
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octave-config.in.cc:
Use '#include "config.h"' rather than <config.h>.
author | Rik <rik@octave.org> |
---|---|
date | Thu, 18 Feb 2016 13:34:50 -0800 |
parents | fcac5dbbf9ed |
children | ecce63c99c3f |
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/* Copyright (C) 2002-2015 John W. Eaton This file is part of Octave. Octave is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. Octave is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with Octave; see the file COPYING. If not, see <http://www.gnu.org/licenses/>. */ // Author: Paul Kienzle <pkienzle@users.sf.net> #ifdef HAVE_CONFIG_H # include "config.h" #endif #include "dMatrix.h" #include "fMatrix.h" #include "CMatrix.h" #include "fCMatrix.h" #include "dSparse.h" #include "CSparse.h" #include "dDiagMatrix.h" #include "fDiagMatrix.h" #include "CDiagMatrix.h" #include "fCDiagMatrix.h" #include "PermMatrix.h" #include "mx-inlines.cc" #include "quit.h" #include "defun.h" #include "error.h" #include "ovl.h" template <typename R, typename T> static MArray<T> kron (const MArray<R>& a, const MArray<T>& b) { assert (a.ndims () == 2); assert (b.ndims () == 2); octave_idx_type nra = a.rows (); octave_idx_type nrb = b.rows (); octave_idx_type nca = a.cols (); octave_idx_type ncb = b.cols (); MArray<T> c (dim_vector (nra*nrb, nca*ncb)); T *cv = c.fortran_vec (); for (octave_idx_type ja = 0; ja < nca; ja++) for (octave_idx_type jb = 0; jb < ncb; jb++) for (octave_idx_type ia = 0; ia < nra; ia++) { octave_quit (); mx_inline_mul (nrb, cv, a(ia, ja), b.data () + nrb*jb); cv += nrb; } return c; } template <typename R, typename T> static MArray<T> kron (const MDiagArray2<R>& a, const MArray<T>& b) { assert (b.ndims () == 2); octave_idx_type nra = a.rows (); octave_idx_type nrb = b.rows (); octave_idx_type dla = a.diag_length (); octave_idx_type nca = a.cols (); octave_idx_type ncb = b.cols (); MArray<T> c (dim_vector (nra*nrb, nca*ncb), T ()); for (octave_idx_type ja = 0; ja < dla; ja++) for (octave_idx_type jb = 0; jb < ncb; jb++) { octave_quit (); mx_inline_mul (nrb, &c.xelem (ja*nrb, ja*ncb + jb), a.dgelem (ja), b.data () + nrb*jb); } return c; } template <typename T> static MSparse<T> kron (const MSparse<T>& A, const MSparse<T>& B) { octave_idx_type idx = 0; MSparse<T> C (A.rows () * B.rows (), A.columns () * B.columns (), A.nnz () * B.nnz ()); C.cidx (0) = 0; for (octave_idx_type Aj = 0; Aj < A.columns (); Aj++) for (octave_idx_type Bj = 0; Bj < B.columns (); Bj++) { octave_quit (); for (octave_idx_type Ai = A.cidx (Aj); Ai < A.cidx (Aj+1); Ai++) { octave_idx_type Ci = A.ridx (Ai) * B.rows (); const T v = A.data (Ai); for (octave_idx_type Bi = B.cidx (Bj); Bi < B.cidx (Bj+1); Bi++) { C.data (idx) = v * B.data (Bi); C.ridx (idx++) = Ci + B.ridx (Bi); } } C.cidx (Aj * B.columns () + Bj + 1) = idx; } return C; } static PermMatrix kron (const PermMatrix& a, const PermMatrix& b) { octave_idx_type na = a.rows (); octave_idx_type nb = b.rows (); const Array<octave_idx_type>& pa = a.col_perm_vec (); const Array<octave_idx_type>& pb = b.col_perm_vec (); Array<octave_idx_type> res_perm (dim_vector (na * nb, 1)); octave_idx_type rescol = 0; for (octave_idx_type i = 0; i < na; i++) { octave_idx_type a_add = pa(i) * nb; for (octave_idx_type j = 0; j < nb; j++) res_perm.xelem (rescol++) = a_add + pb(j); } return PermMatrix (res_perm, true); } template <typename MTA, typename MTB> octave_value do_kron (const octave_value& a, const octave_value& b) { MTA am = octave_value_extract<MTA> (a); MTB bm = octave_value_extract<MTB> (b); return octave_value (kron (am, bm)); } octave_value dispatch_kron (const octave_value& a, const octave_value& b) { octave_value retval; if (a.is_perm_matrix () && b.is_perm_matrix ()) retval = do_kron<PermMatrix, PermMatrix> (a, b); else if (a.is_sparse_type () || b.is_sparse_type ()) { if (a.is_complex_type () || b.is_complex_type ()) retval = do_kron<SparseComplexMatrix, SparseComplexMatrix> (a, b); else retval = do_kron<SparseMatrix, SparseMatrix> (a, b); } else if (a.is_diag_matrix ()) { if (b.is_diag_matrix () && a.rows () == a.columns () && b.rows () == b.columns ()) { // We have two diagonal matrices, the product of those will be // another diagonal matrix. To do that efficiently, extract // the diagonals as vectors and compute the product. That // will be another vector, which we then use to construct a // diagonal matrix object. Note that this will fail if our // digaonal matrix object is modified to allow the nonzero // values to be stored off of the principal diagonal (i.e., if // diag ([1,2], 3) is modified to return a diagonal matrix // object instead of a full matrix object). octave_value tmp = dispatch_kron (a.diag (), b.diag ()); retval = tmp.diag (); } else if (a.is_single_type () || b.is_single_type ()) { if (a.is_complex_type ()) retval = do_kron<FloatComplexDiagMatrix, FloatComplexMatrix> (a, b); else if (b.is_complex_type ()) retval = do_kron<FloatDiagMatrix, FloatComplexMatrix> (a, b); else retval = do_kron<FloatDiagMatrix, FloatMatrix> (a, b); } else { if (a.is_complex_type ()) retval = do_kron<ComplexDiagMatrix, ComplexMatrix> (a, b); else if (b.is_complex_type ()) retval = do_kron<DiagMatrix, ComplexMatrix> (a, b); else retval = do_kron<DiagMatrix, Matrix> (a, b); } } else if (a.is_single_type () || b.is_single_type ()) { if (a.is_complex_type ()) retval = do_kron<FloatComplexMatrix, FloatComplexMatrix> (a, b); else if (b.is_complex_type ()) retval = do_kron<FloatMatrix, FloatComplexMatrix> (a, b); else retval = do_kron<FloatMatrix, FloatMatrix> (a, b); } else { if (a.is_complex_type ()) retval = do_kron<ComplexMatrix, ComplexMatrix> (a, b); else if (b.is_complex_type ()) retval = do_kron<Matrix, ComplexMatrix> (a, b); else retval = do_kron<Matrix, Matrix> (a, b); } return retval; } DEFUN (kron, args, , "-*- texinfo -*-\n\ @deftypefn {} {} kron (@var{A}, @var{B})\n\ @deftypefnx {} {} kron (@var{A1}, @var{A2}, @dots{})\n\ Form the Kronecker product of two or more matrices.\n\ \n\ This is defined block by block as\n\ \n\ @example\n\ x = [ a(i,j)*b ]\n\ @end example\n\ \n\ For example:\n\ \n\ @example\n\ @group\n\ kron (1:4, ones (3, 1))\n\ @result{} 1 2 3 4\n\ 1 2 3 4\n\ 1 2 3 4\n\ @end group\n\ @end example\n\ \n\ If there are more than two input arguments @var{A1}, @var{A2}, @dots{},\n\ @var{An} the Kronecker product is computed as\n\ \n\ @example\n\ kron (kron (@var{A1}, @var{A2}), @dots{}, @var{An})\n\ @end example\n\ \n\ @noindent\n\ Since the Kronecker product is associative, this is well-defined.\n\ @end deftypefn") { int nargin = args.length (); if (nargin < 2) print_usage (); octave_value retval; octave_value a = args(0); octave_value b = args(1); retval = dispatch_kron (a, b); for (octave_idx_type i = 2; i < nargin; i++) retval = dispatch_kron (retval, args(i)); return retval; } /* %!test %! x = ones (2); %! assert (kron (x, x), ones (4)); %!shared x, y, z, p1, p2, d1, d2 %! x = [1, 2]; %! y = [-1, -2]; %! z = [1, 2, 3, 4; 1, 2, 3, 4; 1, 2, 3, 4]; %! p1 = eye (3)([2, 3, 1], :); ## Permutation matrix %! p2 = [0 1 0; 0 0 1; 1 0 0]; ## Non-permutation equivalent %! d1 = diag ([1 2 3]); ## Diag type matrix %! d2 = [1 0 0; 0 2 0; 0 0 3]; ## Non-diag equivalent %!assert (kron (1:4, ones (3, 1)), z) %!assert (kron (single (1:4), ones (3, 1)), single (z)) %!assert (kron (sparse (1:4), ones (3, 1)), sparse (z)) %!assert (kron (complex (1:4), ones (3, 1)), z) %!assert (kron (complex (single(1:4)), ones (3, 1)), single(z)) %!assert (kron (x, y, z), kron (kron (x, y), z)) %!assert (kron (x, y, z), kron (x, kron (y, z))) %!assert (kron (p1, p1), kron (p2, p2)) %!assert (kron (p1, p2), kron (p2, p1)) %!assert (kron (d1, d1), kron (d2, d2)) %!assert (kron (d1, d2), kron (d2, d1)) %!assert (kron (diag ([1, 2]), diag ([3, 4])), diag ([3, 4, 6, 8])) %% Test for two diag matrices. See the comments above in %% dispatch_kron for this case. %% %!test %! expected = zeros (16, 16); %! expected (1, 11) = 3; %! expected (2, 12) = 4; %! expected (5, 15) = 6; %! expected (6, 16) = 8; %! assert (kron (diag ([1, 2], 2), diag ([3, 4], 2)), expected) */