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/*
* These are sparsetools functions that are not currently used
*
*/
/*
* Compute C = A*B for CSR matrices A,B
*
*
* Input Arguments:
* I n_row - number of rows in A
* I n_col - number of columns in B (hence C is n_row by n_col)
* I Ap[n_row+1] - row pointer
* I Aj[nnz(A)] - column indices
* T Ax[nnz(A)] - nonzeros
* I Bp[?] - row pointer
* I Bj[nnz(B)] - column indices
* T Bx[nnz(B)] - nonzeros
* Output Arguments:
* vec<I> Cp - row pointer
* vec<I> Cj - column indices
* vec<T> Cx - nonzeros
*
* Note:
* Output arrays Cp, Cj, and Cx will be allocated within in the method
*
* Note:
* Input: A and B column indices *are not* assumed to be in sorted order
* Output: C column indices *are not* assumed to be in sorted order
* Cx will not contain any zero entries
*
* Complexity: O(n_row*K^2 + max(n_row,n_col))
* where K is the maximum nnz in a row of A
* and column of B.
*
*
* This implementation closely follows the SMMP algorithm:
*
* "Sparse Matrix Multiplication Package (SMMP)"
* Randolph E. Bank and Craig C. Douglas
*
* http://citeseer.ist.psu.edu/445062.html
* http://www.mgnet.org/~douglas/ccd-codes.html
*
*/
template <class I, class T>
void csrmucsr(const I n_row,
const I n_col,
const I Ap[],
const I Aj[],
const T Ax[],
const I Bp[],
const I Bj[],
const T Bx[],
std::vector<I>* Cp,
std::vector<I>* Cj,
std::vector<T>* Cx)
{
Cp->resize(n_row+1,0);
std::vector<I> next(n_col,-1);
std::vector<T> sums(n_col, 0);
for(I i = 0; i < n_row; i++){
I head = -2;
I length = 0;
I jj_start = Ap[i];
I jj_end = Ap[i+1];
for(I jj = jj_start; jj < jj_end; jj++){
I j = Aj[jj];
I kk_start = Bp[j];
I kk_end = Bp[j+1];
for(I kk = kk_start; kk < kk_end; kk++){
I k = Bj[kk];
sums[k] += Ax[jj]*Bx[kk];
if(next[k] == -1){
next[k] = head;
head = k;
length++;
}
}
}
for(I jj = 0; jj < length; jj++){
if(sums[head] != 0){
Cj->push_back(head);
Cx->push_back(sums[head]);
}
I temp = head;
head = next[head];
next[temp] = -1; //clear arrays
sums[temp] = 0;
}
(*Cp)[i+1] = Cx->size();
}
}
/*
* Compute M = A for CSR matrix A, dense matrix M
*
* Input Arguments:
* I n_row - number of rows in A
* I n_col - number of columns in A
* I Ap[n_row+1] - row pointer
* I Aj[nnz(A)] - column indices
* T Ax[nnz(A)] - nonzeros
* T Mx[n_row*n_col] - dense matrix
*
* Note:
* Output array Mx is assumed to be allocated and
* initialized to 0 by the caller.
*
*/
template <class I, class T>
void csr_todense(const I n_row,
const I n_col,
const I Ap[],
const I Aj[],
const T Ax[],
T Mx[])
{
I row_base = 0;
for(I i = 0; i < n_row; i++){
I row_start = Ap[i];
I row_end = Ap[i+1];
for(I jj = row_start; jj < row_end; jj++){
I j = Aj[jj];
Mx[row_base + j] = Ax[jj];
}
row_base += n_col;
}
}
/*
* Compute B = A for CSR matrix A, COO matrix B
*
* Also, with the appropriate arguments can also be used to:
* - convert CSC->COO
*
* Input Arguments:
* I n_row - number of rows in A
* I n_col - number of columns in A
* I Ap[n_row+1] - row pointer
* I Aj[nnz(A)] - column indices
* T Ax[nnz(A)] - nonzeros
*
* Output Arguments:
* vec<I> Bi - row indices
* vec<I> Bj - column indices
* vec<T> Bx - nonzeros
*
* Note:
* Output arrays Bi, Bj, Bx will be allocated within in the method
*
* Note:
* Complexity: Linear.
*
*/
template <class I, class T>
void csr_tocoo(const I n_row,
const I n_col,
const I Ap[],
const I Aj[],
const T Ax[],
std::vector<I>* Bi,
std::vector<I>* Bj,
std::vector<T>* Bx)
{
I nnz = Ap[n_row];
Bi->reserve(nnz);
Bi->reserve(nnz);
Bx->reserve(nnz);
for(I i = 0; i < n_row; i++){
I row_start = Ap[i];
I row_end = Ap[i+1];
for(I jj = row_start; jj < row_end; jj++){
Bi->push_back(i);
Bj->push_back(Aj[jj]);
Bx->push_back(Ax[jj]);
}
}
}
/*
* Construct CSC matrix A from diagonals
*
* Input Arguments:
* I n_row - number of rows in A
* I n_col - number of columns in A
* I n_diags - number of diagonals
* I diags_indx[n_diags] - where to place each diagonal
* T diags[n_diags][min(n_row,n_col)] - diagonals
*
* Output Arguments:
* vec<I> Ap - row pointer
* vec<I> Aj - column indices
* vec<T> Ax - nonzeros
*
* Note:
* Output arrays Ap, Aj, Ax will be allocated within in the method
*
* Note:
* Output: row indices are not in sorted order
*
* Complexity: Linear
*
*/
template <class I, class T>
void spdiags(const I n_row,
const I n_col,
const I n_diag,
const I offsets[],
const T diags[],
std::vector<I> * Ap,
std::vector<I> * Ai,
std::vector<T> * Ax)
{
const I diags_length = std::min(n_row,n_col);
Ap->push_back(0);
for(I i = 0; i < n_col; i++){
for(I j = 0; j < n_diag; j++){
if(offsets[j] <= 0){ //sub-diagonal
I row = i - offsets[j];
if (row >= n_row){ continue; }
Ai->push_back(row);
Ax->push_back(diags[j*diags_length + i]);
} else { //super-diagonal
I row = i - offsets[j];
if (row < 0 || row >= n_row){ continue; }
Ai->push_back(row);
Ax->push_back(diags[j*diags_length + row]);
}
}
Ap->push_back(Ai->size());
}
}
template <class I, class T, int R, int C, class bin_op>
void bsr_binop_bsr_fixed(const I n_brow, const I n_bcol,
const I Ap[], const I Aj[], const T Ax[],
const I Bp[], const I Bj[], const T Bx[],
I Cp[], I Cj[], T Cx[],
const bin_op& op)
{
//Method that works for unsorted indices
const I RC = R*C;
T zeros[RC] = {0};
Cp[0] = 0;
I nnz = 0;
std::cout << "using bsr_ fixed" << std::endl;
for(I i = 0; i < n_brow; i++){
I A_pos = Ap[i];
I B_pos = Bp[i];
I A_end = Ap[i+1];
I B_end = Bp[i+1];
I A_j = Aj[A_pos];
I B_j = Bj[B_pos];
//while not finished with either row
while(A_pos < A_end && B_pos < B_end){
if(A_j == B_j){
Cj[nnz] = A_j;
vec_binop_vec<RC> (Ax + RC*A_pos, Bx + RC*B_pos, Cx + RC*nnz, op);
if( is_nonzero_block(Cx + RC*nnz,RC) ){
nnz++;
}
A_j = Aj[++A_pos];
B_j = Bj[++B_pos];
} else if (A_j < B_j) {
Cj[nnz] = A_j;
vec_binop_vec<RC> (Ax + RC*A_pos, zeros, Cx + RC*nnz, op);
if( is_nonzero_block(Cx + RC*nnz,RC) ){
nnz++;
}
A_j = Aj[++A_pos];
} else {
//B_j < A_j
Cj[nnz] = B_j;
vec_binop_vec<RC> (zeros, Bx + RC*A_pos, Cx + RC*nnz, op);
if( is_nonzero_block(Cx + RC*nnz,RC) ){
nnz++;
}
B_j = Bj[++B_pos];
}
}
//tail
while(A_pos < A_end){
Cj[nnz] = A_j;
vec_binop_vec<RC> (Ax + RC*A_pos, zeros, Cx + RC*nnz, op);
if( is_nonzero_block(Cx + RC*nnz,RC) ){
nnz++;
}
A_j = Aj[++A_pos];
}
while(B_pos < B_end){
Cj[nnz] = B_j;
vec_binop_vec<RC> (zeros, Bx + RC*A_pos, Cx + RC*nnz, op);
if( is_nonzero_block(Cx + RC*nnz,RC) ){
nnz++;
}
B_j = Bj[++B_pos];
}
Cp[i+1] = nnz;
}
}
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