octave-nkf: liboctave/Sparse.cc comparison

comparison liboctave/Sparse.cc @ 10425:0677c5d80b77

rewrite 1D sparse indexing

author	Jaroslav Hajek <highegg@gmail.com>
date	Fri, 19 Mar 2010 13:00:06 +0100
parents	99e9bae2d81e
children	10207338603a

comparison

equal deleted inserted replaced

-:0b05b204775b
+:0677c5d80b77
 #include "Sparse.h"
 #include "sparse-sort.h"
 #include "sparse-util.h"
 #include "oct-spparms.h"
+#include "mx-inlines.cc"
 template <class T>
 T&
 Sparse<T>::SparseRep::elem (octave_idx_type _r, octave_idx_type _c)
 {
 return trans ? this->transpose () : *this;
 }
 template <class T>
 void
-Sparse<T>::resize_no_fill (const dim_vector& dv)
+Sparse<T>::resize1 (octave_idx_type n)
+{
+octave_idx_type nr = rows (), nc = cols ();
+if (nr == 1 || nr == 0)
+resize (1, n);
+else if (nc == 1)
+resize (n, 1);
+else
+gripe_invalid_resize ();
+}
+template <class T>
+void
+Sparse<T>::resize (const dim_vector& dv)
 {
 octave_idx_type n = dv.length ();
 if (n != 2)
 {
 (*current_liboctave_error_handler) ("sparse array must be 2-D");
 return;
 }
-resize_no_fill (dv(0), dv(1));
+resize (dv(0), dv(1));
 }
 template <class T>
 void
-Sparse<T>::resize_no_fill (octave_idx_type r, octave_idx_type c)
+Sparse<T>::resize (octave_idx_type r, octave_idx_type c)
 {
 if (r < 0 || c < 0)
 {
 (*current_liboctave_error_handler)
 ("can't resize to negative dimension");
 // Either A(:) = [] or A(idx) = [] with idx enumerating all
 // elements, so we delete all elements and return [](0x0).  To
 // preserve the orientation of the vector, you have to use
 // A(idx,:) = [] (delete rows) or A(:,idx) (delete columns).
-resize_no_fill (0, 0);
+resize (0, 0);
 return;
 }
 idx_arg.sort (true);
 if (idx_j.is_colon ())
 {
 // A(:,:) -- We are deleting columns and rows, so the result
 // is [](0x0).
-resize_no_fill (0, 0);
+resize (0, 0);
 return;
 }
 if (idx_j.is_colon_equiv (nc, 1))
 {
 // A(:,j) -- We are deleting columns by enumerating them,
 // If we enumerate all of them, we should have zero columns
 // with the same number of rows that we started with.
-resize_no_fill (nr, 0);
+resize (nr, 0);
 return;
 }
 }
 if (idx_j.is_colon () && idx_i.is_colon_equiv (nr, 1))
 {
 // A(i,:) -- We are deleting rows by enumerating them.  If we
 // enumerate all of them, we should have zero rows with the
 // same number of columns that we started with.
-resize_no_fill (0, nc);
+resize (0, nc);
 return;
 }
 if (idx_i.is_colon_equiv (nr, 1))
 {
 if (idx_j.is_colon_equiv (nc, 1))
-resize_no_fill (0, 0);
+resize (0, 0);
 else
 {
 idx_j.sort (true);
 octave_idx_type num_to_delete = idx_j.length (nc);
 if (num_to_delete != 0)
 {
 if (nr == 1 && num_to_delete == nc)
-resize_no_fill (0, 0);
+resize (0, 0);
 else
 {
 octave_idx_type new_nc = nc;
 octave_idx_type new_nnz = nnz ();
 }
 }
 else if (idx_j.is_colon_equiv (nc, 1))
 {
 if (idx_i.is_colon_equiv (nr, 1))
-resize_no_fill (0, 0);
+resize (0, 0);
 else
 {
 idx_i.sort (true);
 octave_idx_type num_to_delete = idx_i.length (nr);
 if (num_to_delete != 0)
 {
 if (nc == 1 && num_to_delete == nr)
-resize_no_fill (0, 0);
+resize (0, 0);
 else
 {
 octave_idx_type new_nr = nr;
 octave_idx_type new_nnz = nnz ();
 return retval;
 }
 template <class T>
 Sparse<T>
-Sparse<T>::index (const idx_vector& idx_arg, bool resize_ok) const
+Sparse<T>::index (const idx_vector& idx, bool resize_ok) const
 {
 Sparse<T> retval;
 assert (ndims () == 2);
+// FIXME: please don't fix the shadowed member warning yet because
+// Sparse<T>::idx will eventually go away.
 octave_idx_type nr = dim1 ();
 octave_idx_type nc = dim2 ();
 octave_idx_type nz = nnz ();
-// Use safe_numel so that we get an error if the matrix is too big to be indexed.
+octave_idx_type nel = numel (); // Can throw.
-octave_idx_type orig_len = nr * nc;
+const dim_vector idx_dims = idx.orig_dimensions ();
-dim_vector idx_orig_dims = idx_arg.orig_dimensions ();
+if (idx_dims.length () > 2)
-octave_idx_type idx_orig_rows = idx_arg.orig_rows ();
-octave_idx_type idx_orig_columns = idx_arg.orig_columns ();
-if (idx_orig_dims.length () > 2)
 (*current_liboctave_error_handler)
 ("cannot index sparse matrix with an N-D Array");
-else if (idx_arg.is_colon ())
+else if (idx.is_colon ())
 {
 // Fast magic colon processing.
-retval = Sparse<T> (nr * nc, 1, nz);
+retval = Sparse<T> (nel, 1, nz);
 for (octave_idx_type i = 0; i < nc; i++)
-for (octave_idx_type j = cidx(i); j < cidx(i+1); j++)
+{
-{
+for (octave_idx_type j = cidx(i); j < cidx(i+1); j++)
-octave_quit ();
+{
 retval.xdata(j) = data(j);
 retval.xridx(j) = ridx(j) + i * nr;
 }
+}
 retval.xcidx(0) = 0;
 retval.xcidx(1) = nz;
 }
-else if (! resize_ok && idx_arg.extent (length ()) > length ())
+else if (idx.extent (nel) > nel)
 {
-gripe_index_out_of_range (1, 1, idx_arg.extent (orig_len), orig_len);
+// resize_ok is completely handled here.
+if (resize_ok)
+{
+octave_idx_type ext = idx.extent (nel);
+Sparse<T> tmp = *this;
+tmp.resize1 (ext);
+retval = tmp.index (idx);
+}
+else
+gripe_index_out_of_range (1, 1, idx.extent (nel), nel);
 }
 else if (nr == 1 && nc == 1)
 {
 // You have to be pretty sick to get to this bit of code,
 // since you have a scalar stored as a sparse matrix, and
 // then want to make a dense matrix with sparse
 // representation. Ok, we'll do it, but you deserve what
 // you get!!
-octave_idx_type n = idx_arg.length (length ());
+retval = Sparse<T> (idx_dims(0), idx_dims(1), nz ? data (0) : T ());
-if (n == 0)
+}
+else if (nc == 1)
-retval = Sparse<T> (idx_orig_dims);
+{
-else if (nz < 1)
+// Sparse column vector.
-if (n >= idx_orig_dims.numel ())
+octave_idx_type lb, ub;
-retval = Sparse<T> (idx_orig_dims);
+octave_sort<octave_idx_type> isort;
-else
-retval = Sparse<T> (dim_vector (n, 1));
+if (idx.is_scalar ())
-else if (n >= idx_orig_dims.numel ())
+{
-{
+// Scalar index - just a binary lookup.
-T el = elem (0);
+octave_idx_type i = isort.lookup (ridx (), nz, idx(0));
-octave_idx_type new_nr = idx_orig_rows;
+if (i > 0 && ridx(i-1) == idx(0))
-octave_idx_type new_nc = idx_orig_columns;
+retval = Sparse (1, 1, data (i-1));
-for (octave_idx_type i = 2; i < idx_orig_dims.length (); i++)
+else
-new_nc *= idx_orig_dims (i);
+retval = Sparse (1, 1);
+}
-retval = Sparse<T> (new_nr, new_nc, idx_arg.ones_count ());
+else if (idx.is_cont_range (nel, lb, ub))
+{
-octave_idx_type ic = 0;
+// Special-case a contiguous range.
-for (octave_idx_type i = 0; i < n; i++)
+// Look-up indices first.
-{
+octave_idx_type li = isort.lookup (ridx (), nz, lb);
-if (i % new_nr == 0)
+octave_idx_type ui = isort.lookup (ridx (), nz, ub);
-retval.xcidx(i / new_nr) = ic;
+// Adjust to lower bounds.
+li -= (li > 0 && ridx(li-1) == lb);
-octave_idx_type ii = idx_arg.elem (i);
+ui -= (ui > 0 && ridx(ui-1) == ub);
-if (ii == 0)
+// Copy data and adjust indices.
-{
+octave_idx_type nz_new = ui - li;
-octave_quit ();
+retval = Sparse<T> (ub - lb, 1, nz_new);
-retval.xdata(ic) = el;
+copy_or_memcpy (nz_new, data () + li, retval.data ());
-retval.xridx(ic++) = i % new_nr;
+mx_inline_sub (nz_new, retval.xridx (), ridx () + li, lb);
-}
+retval.xcidx(1) = nz_new;
-}
-retval.xcidx (new_nc) = ic;
 }
 else
 {
-T el = elem (0);
+// If indexing a sparse column vector by a vector, the result is a
-retval = Sparse<T> (n, 1, nz);
+// sparse column vector, otherwise it inherits the shape of index.
+// Vector transpose is cheap, so do it right here.
-for (octave_idx_type i = 0; i < nz; i++)
+const Array<octave_idx_type> idxa = (idx_dims(0) == 1
-{
+? idx.as_array ().transpose ()
-octave_quit ();
+: idx.as_array ());
-retval.xdata(i) = el;
-retval.xridx(i) = i;
+octave_idx_type new_nr = idxa.rows (), new_nc = idxa.cols ();
-}
-retval.xcidx(0) = 0;
+// Lookup.
-retval.xcidx(1) = n;
+// FIXME: Could specialize for sorted idx?
-}
+NoAlias< Array<octave_idx_type> > lidx (new_nr, new_nc);
-}
+isort.lookup (ridx (), nz, idxa.data (), idxa.numel (), lidx.fortran_vec ());
-else if (nr == 1 || nc == 1)
-{
+// Count matches.
-// If indexing a vector with a matrix, return value has same
+retval = Sparse<T> (idxa.rows (), idxa.cols ());
-// shape as the index.  Otherwise, it has same orientation as
+for (octave_idx_type j = 0; j < new_nc; j++)
-// indexed object.
+{
-octave_idx_type len = length ();
+octave_idx_type nzj = 0;
-octave_idx_type n = idx_arg.length (len);
+for (octave_idx_type i = 0; i < new_nr; i++)
-octave_idx_type l, u;
+{
-if (n == 0)
+octave_idx_type l = lidx(i, j);
-if (nr == 1)
+if (l > 0 && ridx(l-1) == idxa(i, j))
-retval = Sparse<T> (dim_vector (1, 0));
+nzj++;
 else
-retval = Sparse<T> (dim_vector (0, 1));
+lidx(i, j) = 0;
-else if (nz < 1)
+}
-if (idx_orig_rows == 1 || idx_orig_columns == 1)
+retval.xcidx(j+1) = retval.xcidx(j) + nzj;
-retval = Sparse<T> ((nr == 1 ? 1 : n), (nr == 1 ? n : 1));
+}
-else
-retval = Sparse<T> (idx_orig_dims);
+retval.change_capacity (retval.xcidx(new_nc));
-else if (idx_arg.is_range () && idx_arg.is_cont_range (n, l, u))
-{
+// Copy data and set row indices.
-// Special case of indexing a sparse vector by a continuous range
+octave_idx_type k = 0;
-if (nr == 1)
+for (octave_idx_type j = 0; j < new_nc; j++)
-{
+for (octave_idx_type i = 0; i < new_nr; i++)
-octave_idx_type new_nzmx = cidx(u) - cidx(l);
+{
-retval = Sparse<T> (1, n, new_nzmx);
+octave_idx_type l = lidx(i, j) - 1;
-octave_idx_type *iidx = retval.xcidx ();
+if (l >= 0)
-copy_or_memcpy (n + 1, rep->c + l, iidx);
+{
-octave_idx_type ii = iidx[0];
+retval.data(k) = data(l);
-if (ii != 0)
+retval.xridx(k++) = i;
-{
-for (octave_idx_type i = 0; i < n + 1; i++)
-iidx[i] -= ii;
-}
-copy_or_memcpy (new_nzmx, rep->d + ii, retval.rep->d);
-copy_or_memcpy (new_nzmx, rep->r + ii, retval.rep->r);
-}
-else
-{
-octave_idx_type j1 = -1;
-octave_idx_type new_nzmx = 0;
-for (octave_idx_type j = 0; j < nz; j++)
-{
-octave_idx_type j2 = ridx (j);
-if (j2 >= l && j2 < u)
-{
-new_nzmx++;
-if (j1 < 0)
-j1 = j;
-}
-if (j2 >= u)
-break;
 }
+}
-retval = Sparse<T> (n, 1, new_nzmx);
+}
-if (new_nzmx > 0)
+}
-{
+else if (nr == 1)
-retval.xcidx(0) = 0;
+{
-retval.xcidx(1) = new_nzmx;
+octave_idx_type lb, ub;
-copy_or_memcpy (new_nzmx, rep->d + j1, retval.rep->d);
+if (idx.is_scalar ())
-octave_idx_type *iidx = retval.xridx ();
+retval = Sparse<T> (1, 1, elem (0, idx(0)));
-copy_or_memcpy (new_nzmx, rep->r + j1, iidx);
+else if (idx.is_cont_range (nel, lb, ub))
-if (l != 0)
+{
-{
+// Special-case a contiguous range.
-for (octave_idx_type i = 0; i < new_nzmx; i++)
+octave_idx_type lbi = cidx(lb), ubi = cidx(ub), new_nz = ubi - lbi;
-iidx[i] -= l;
+retval = Sparse<T> (1, ub - lb, new_nz);
-}
+copy_or_memcpy (new_nz, data () + lbi, retval.data ());
-}
+fill_or_memset (new_nz, static_cast<octave_idx_type> (0), retval.ridx ());
-}
+mx_inline_sub (ub - lb, retval.cidx () + 1, cidx () + lb, lbi);
 }
 else
 {
-octave_idx_type new_nzmx = 0;
+// Sparse row vectors occupy O(nr) storage anyway, so let's just
-if (nr == 1)
+// convert the matrix to full, index, and sparsify the result.
-for (octave_idx_type i = 0; i < n; i++)
+retval = Sparse<T> (array_value ().index (idx));
-{
-octave_quit ();
-octave_idx_type ii = idx_arg.elem (i);
-if (ii < len)
-if (cidx(ii) != cidx(ii+1))
-new_nzmx++;
-}
-else
-for (octave_idx_type i = 0; i < n; i++)
-{
-octave_idx_type ii = idx_arg.elem (i);
-if (ii < len)
-for (octave_idx_type j = 0; j < nz; j++)
-{
-octave_quit ();
-if (ridx(j) == ii)
-new_nzmx++;
-if (ridx(j) >= ii)
-break;
-}
-}
-if (idx_orig_rows == 1 || idx_orig_columns == 1)
-{
-if (nr == 1)
-{
-retval = Sparse<T> (1, n, new_nzmx);
-octave_idx_type jj = 0;
-retval.xcidx(0) = 0;
-for (octave_idx_type i = 0; i < n; i++)
-{
-octave_quit ();
-octave_idx_type ii = idx_arg.elem (i);
-if (ii < len)
-if (cidx(ii) != cidx(ii+1))
-{
-retval.xdata(jj) = data(cidx(ii));
-retval.xridx(jj++) = 0;
-}
-retval.xcidx(i+1) = jj;
-}
-}
-else
-{
-retval = Sparse<T> (n, 1, new_nzmx);
-retval.xcidx(0) = 0;
-retval.xcidx(1) = new_nzmx;
-octave_idx_type jj = 0;
-for (octave_idx_type i = 0; i < n; i++)
-{
-octave_idx_type ii = idx_arg.elem (i);
-if (ii < len)
-for (octave_idx_type j = 0; j < nz; j++)
-{
-octave_quit ();
-if (ridx(j) == ii)
-{
-retval.xdata(jj) = data(j);
-retval.xridx(jj++) = i;
-}
-if (ridx(j) >= ii)
-break;
-}
-}
-}
-}
-else
-{
-octave_idx_type new_nr;
-octave_idx_type new_nc;
-if (n >= idx_orig_dims.numel ())
-{
-new_nr = idx_orig_rows;
-new_nc = idx_orig_columns;
-}
-else
-{
-new_nr = n;
-new_nc = 1;
-}
-retval = Sparse<T> (new_nr, new_nc, new_nzmx);
-if (nr == 1)
-{
-octave_idx_type jj = 0;
-retval.xcidx(0) = 0;
-for (octave_idx_type i = 0; i < n; i++)
-{
-octave_quit ();
-octave_idx_type ii = idx_arg.elem (i);
-if (ii < len)
-if (cidx(ii) != cidx(ii+1))
-{
-retval.xdata(jj) = data(cidx(ii));
-retval.xridx(jj++) = 0;
-}
-retval.xcidx(i/new_nr+1) = jj;
-}
-}
-else
-{
-octave_idx_type jj = 0;
-retval.xcidx(0) = 0;
-for (octave_idx_type i = 0; i < n; i++)
-{
-octave_idx_type ii = idx_arg.elem (i);
-if (ii < len)
-for (octave_idx_type j = 0; j < nz; j++)
-{
-octave_quit ();
-if (ridx(j) == ii)
-{
-retval.xdata(jj) = data(j);
-retval.xridx(jj++) = i;
-}
-if (ridx(j) >= ii)
-break;
-}
-retval.xcidx(i/new_nr+1) = jj;
-}
-}
-}
 }
 }
 else
 {
 (*current_liboctave_warning_with_id_handler)
 ("Octave:fortran-indexing", "single index used for sparse matrix");
-// This code is only for indexing matrices.  The vector
+if (nr != 0 && idx.is_scalar ())
-// cases are handled above.
+retval = Sparse<T> (1, 1, elem (idx(0) % nr, idx(0) / nr));
-octave_idx_type result_nr = idx_orig_rows;
-octave_idx_type result_nc = idx_orig_columns;
-if (nz < 1)
-retval = Sparse<T> (result_nr, result_nc);
 else
 {
-// Count number of non-zero elements
+// Indexing a non-vector sparse matrix by linear indexing.
-octave_idx_type new_nzmx = 0;
+// I suppose this is rare (and it may easily overflow), so let's take the easy way,
-octave_idx_type kk = 0;
+// and reshape first to column vector, which is already handled above.
-for (octave_idx_type j = 0; j < result_nc; j++)
+retval = index (idx_vector::colon).index (idx);
-{
+// In this case we're supposed to always inherit the shape, but column(row) doesn't do
-for (octave_idx_type i = 0; i < result_nr; i++)
+// it, so we'll do it instead.
-{
+if (idx_dims (0) == 1 && idx_dims (1) != 1)
-octave_quit ();
+retval = retval.transpose ();
-octave_idx_type ii = idx_arg.elem (kk++);
-if (ii < orig_len)
-{
-octave_idx_type fr = ii % nr;
-octave_idx_type fc = (ii - fr) / nr;
-for (octave_idx_type k = cidx(fc); k < cidx(fc+1); k++)
-{
-if (ridx(k) == fr)
-new_nzmx++;
-if (ridx(k) >= fr)
-break;
-}
-}
-}
-}
-retval = Sparse<T> (result_nr, result_nc, new_nzmx);
-kk = 0;
-octave_idx_type jj = 0;
-retval.xcidx(0) = 0;
-for (octave_idx_type j = 0; j < result_nc; j++)
-{
-for (octave_idx_type i = 0; i < result_nr; i++)
-{
-octave_quit ();
-octave_idx_type ii = idx_arg.elem (kk++);
-if (ii < orig_len)
-{
-octave_idx_type fr = ii % nr;
-octave_idx_type fc = (ii - fr) / nr;
-for (octave_idx_type k = cidx(fc); k < cidx(fc+1); k++)
-{
-if (ridx(k) == fr)
-{
-retval.xdata(jj) = data(k);
-retval.xridx(jj++) = i;
-}
-if (ridx(k) >= fr)
-break;
-}
-}
-}
-retval.xcidx(j+1) = jj;
-}
 }
 }
 return retval;
 }
 gripe_index_out_of_range (2, 2, idx_j.extent (nc), nc);
 else
 {
 if (n == 0 || m == 0)
 {
-retval.resize_no_fill (n, m);
+retval.resize (n, m);
 }
 else
 {
 bool idx_i_colon = idx_i.is_colon_equiv (nr);
 bool idx_j_colon = idx_j.is_colon_equiv (nc);
 }
 }
 }
 return d;
+}
+template <class T>
+Array<T>
+Sparse<T>::array_value () const
+{
+NoAlias< Array<T> > retval (dims (), T());
+if (rows () == 1)
+{
+octave_idx_type i = 0;
+for (octave_idx_type j = 0, nc = cols (); j < nc; j++)
+{
+if (cidx(j+1) > i)
+retval(j) = data (i++);
+}
+}
+else
+{
+for (octave_idx_type j = 0, nc = cols (); j < nc; j++)
+for (octave_idx_type i = cidx(j), iu = cidx(j+1); i < iu; i++)
+retval (ridx(i), j) = data (i);
+}
+return retval;
 }
 // FIXME
 // Unfortunately numel can overflow for very large but very sparse matrices.
 // For now just flag an error when this happens.

Mercurial > hg > octave-nkf

comparison liboctave/Sparse.cc @ 10425:0677c5d80b77