const Sparse<T> operator-(const Sparse<T> &s)
{
Vector<T> data(s.priv_data().size());
std::transform(s.priv_data().begin(), s.priv_data().end(),
data.begin(), std::negate<T>());
return Sparse<T>(s.dimensions(), s.priv_row_start(), s.priv_column(),
data);
}
const Sparse<T> operator/(const Sparse<T> &s, T b)
{
Vector<T> data(s.priv_data().size());
std::transform(s.priv_data().begin(), s.priv_data().end(),
data.begin(), divided_constant<T,T>(b));
return Sparse<T>(s.dimensions(), s.priv_row_start(), s.priv_column(),
data);
}
static const Sparse<elt_t> do_kron(const Sparse<elt_t> &s2, const Sparse<elt_t> &s1)
{
index rows1 = s1.rows();
index cols1 = s1.columns();
index rows2 = s2.rows();
index cols2 = s2.columns();
index number_nonzero = s1.length() * s2.length();
index total_rows = rows1 * rows2;
index total_cols = cols1 * cols2;
if (number_nonzero == 0)
return Sparse<elt_t>(total_rows, total_cols);
Tensor<elt_t> output_data(number_nonzero);
Indices output_column(number_nonzero);
Indices output_row_start(total_rows+1);
Indices output_dims(igen << total_rows << total_cols);
typename Tensor<elt_t>::iterator out_data = output_data.begin();
typename Indices::iterator out_column = output_column.begin();
typename Indices::iterator out_begin = out_column;
typename Indices::iterator out_row_start = output_row_start.begin();
// C([i,j],[k,l]) = s1(i,k) s2(j,l)
*(out_row_start++) = 0;
for (index l = 0; l < rows2; l++) {
for (index k = 0; k < rows1; k++) {
for (index j = s2.priv_row_start()[l]; j < s2.priv_row_start()[l+1]; j++) {
for (index i = s1.priv_row_start()[k]; i < s1.priv_row_start()[k+1]; i++) {
*(out_data++) = s1.priv_data()[i] * s2.priv_data()[j];
*(out_column++) = s1.priv_column()[i] + cols1 * s2.priv_column()[j];
}
}
*(out_row_start++) = out_column - out_begin;
}
}
return Sparse<elt_t>(output_dims, output_row_start, output_column, output_data);
}
const Sparse<T> sparse_binop(const Sparse<T> &m1, const Sparse<T> &m2,
binop op)
{
size_t rows = m1.rows();
size_t cols = m1.columns();
assert(rows == m2.rows() && cols == m2.columns());
if (rows == 0 || cols == 0)
return m1;
index max_size = m1.priv_data().size() + m2.priv_data().size();
Vector<T> data(max_size);
Indices column(max_size);
Indices row_start(rows + 1);
typename Vector<T>::iterator out_data = data.begin();
typename Indices::iterator out_column = column.begin();
typename Indices::iterator out_row_start = row_start.begin();
typename Vector<T>::iterator out_begin = out_data;
typename Vector<T>::const_iterator m1_data = m1.priv_data().begin();
typename Indices::const_iterator m1_row_start = m1.priv_row_start().begin();
typename Indices::const_iterator m1_column = m1.priv_column().begin();
typename Vector<T>::const_iterator m2_data = m2.priv_data().begin();
typename Indices::const_iterator m2_row_start = m2.priv_row_start().begin();
typename Indices::const_iterator m2_column = m2.priv_column().begin();
index j1 = *(m1_row_start++); // data start for this row in M1
index l1 = (*m1_row_start) - j1; // # elements in this row in M1
index j2 = *(m2_row_start++); // data start for this row in M2
index l2 = (*m2_row_start) - j2; // # elements in this row in M2
*out_row_start = 0;
while (1) {
// We look for the next unprocessed matrix element on this row,
// for both matrices. c1 and c2 are the columns associated to
// each element on each matrix.
index c1 = l1 ? *m1_column : cols;
index c2 = l2 ? *m2_column : cols;
T value;
index c;
if (c1 < c2) {
// There is an element a column c1 on matrix m1, but the
// same element at m2 is zero
value = op(*m1_data, number_zero<T>());
c = c1;
l1--; m1_column++; m1_data++;
} else if (c2 < c1) {
// There is an element a column c2 on matrix m2, but the
// same element at m1 is zero
value = op(number_zero<T>(), *m2_data);
c = c2;
l2--; m2_column++; m2_data++;
} else if (c2 < cols) {
// Both elements in m1 and m2 are nonzero.
value = op(*m1_data, *m2_data);
c = c1;
l1--; l2--;
m1_column++; m1_data++;
m2_column++; m2_data++;
} else {
// We have processed all elements in this row.
out_row_start++;
*out_row_start = out_data - out_begin;
if (--rows == 0) {
break;
}
j1 = *m1_row_start; m1_row_start++; l1 = (*m1_row_start) - j1;
j2 = *m2_row_start; m2_row_start++; l2 = (*m2_row_start) - j2;
continue;
}
if (!(value == number_zero<T>())) {
*(out_data++) = value;
*(out_column++) = c;
}
}
index j = out_data - out_begin;
Indices the_column(j);
std::copy(column.begin(), column.begin() + j, the_column.begin());
Vector<T> the_data(j);
std::copy(data.begin(), data.begin() + j, the_data.begin());
return Sparse<T>(m1.dimensions(), row_start, the_column, the_data);
}
