void qrfactor(matrix & Q, matrix & R, matrix & betas, const matrix & A){
int i, j, k, l, N, M; i = 0; j = 0; k = 0; l = 0; N = 0; M = 0;
fpp beta, temp; beta = 0.0; temp = 0.0;
N = A.get_rows(); M = A.get_cols();
matrix bigV(N,1), temp_row(N,1); zeros(bigV); zeros(temp_row);
submatrix x, v;
if((Q.get_rows() != N) || (Q.get_cols() != M) ||(R.get_rows() != N) || (R.get_cols() != M) || (betas.get_rows() != A.get_rows()) || (betas.get_cols() != 1)){
std::cerr << "QR dimensions incompatible! Q(" << Q.get_rows() << "," << Q.get_cols() << "), R(" << R.get_rows() << "," << R.get_cols() << "), betas:(" << betas.get_rows() << "," << betas.get_cols() << "), A:(" << A.get_rows() << "," << A.get_cols() << ")." << std::endl;
exit(-1);
}
R = A;
zeros(Q);
for(i = 0; i < N; i++){
Q(i,i) = 1.0;
}
for(i = 0; i < N-1; i++){
x.subcreate(R, i, i, N-i, 1);
v.subcreate(bigV, i, 0, N-i, 1);
house(v,beta,x);
for(k = i; k < M; k++){
for(j = i; j < N; j++){
temp = 0.0;
for(l = i; l < N; l++){
temp += beta*v(l-i,0)*v(j-i,0)*R(l,k);
}
temp_row(j,0) = R(j,k) - temp;
}
for(l = i; l < N; l++){
R(l,k) = temp_row(l,0);
}
}
for(k = 0; k < M; k++){
for(j = i; j < N; j++){
temp = 0.0;
for(l = i; l < N; l++){
temp += beta*v(l-i,0)*v(j-i,0)*Q(l,k);
}
temp_row(j,0) = Q(j,k) - temp;
}
for(l = i; l < N; l++){
Q(l,k) = temp_row(l,0);
}
}
betas(i,0) = beta;
}
}
/**
* Description not yet available.
* \param
*/
dvar_matrix operator*(const dvar_matrix& m1, const dmatrix& cm2)
{
if (m1.colmin() != cm2.rowmin() || m1.colmax() != cm2.rowmax())
{
cerr << " Incompatible array bounds in "
"dmatrix operator*(const dvar_matrix& x, const dmatrix& m)\n";
ad_exit(21);
}
dmatrix cm1=value(m1);
//dmatrix cm2=value(m2);
dmatrix tmp(m1.rowmin(),m1.rowmax(), cm2.colmin(), cm2.colmax());
#ifdef OPT_LIB
const size_t rowsize = (size_t)cm2.rowsize();
#else
const int _rowsize = cm2.rowsize();
assert(_rowsize > 0);
const size_t rowsize = (size_t)_rowsize;
#endif
try
{
double* temp_col = new double[rowsize];
temp_col-=cm2.rowmin();
for (int j=cm2.colmin(); j<=cm2.colmax(); j++)
{
for (int k=cm2.rowmin(); k<=cm2.rowmax(); k++)
{
temp_col[k] = cm2.elem(k,j);
}
for (int i=cm1.rowmin(); i<=cm1.rowmax(); i++)
{
double sum=0.0;
dvector& temp_row = cm1(i);
for (int k=cm1.colmin(); k<=cm1.colmax(); k++)
{
sum+=temp_row(k) * (temp_col[k]);
// sum+=temp_row(k) * cm2(k,j);
}
tmp(i,j)=sum;
}
}
temp_col+=cm2.rowmin();
delete [] temp_col;
temp_col = 0;
}
catch (std::bad_alloc& e)
{
cerr << "Error[" << __FILE__ << ':' << __LINE__
<< "]: Unable to allocate array.\n";
//ad_exit(21);
throw e;
}
dvar_matrix vtmp=nograd_assign(tmp);
save_identifier_string("TEST1");
//m1.save_dvar_matrix_value();
m1.save_dvar_matrix_position();
cm2.save_dmatrix_value();
cm2.save_dmatrix_position();
vtmp.save_dvar_matrix_position();
save_identifier_string("TEST6");
gradient_structure::GRAD_STACK1->
set_gradient_stack(dmcm_prod);
return vtmp;
}
void Basker<Int,Entry,Exe_Space>::btf_blk_amd
(
BASKER_MATRIX &M,
INT_1DARRAY p,
INT_1DARRAY btf_nnz,
INT_1DARRAY btf_work
)
{
// printf("=============BTF_BLK_AMD_CALLED========\n");
if(Options.incomplete == BASKER_TRUE)
{
//We note that AMD on incomplete ILUK
//Seems realy bad and leads to a zero on the diag
//Therefore, we simply return the natural ordering
for(Int i = 0 ; i < M.ncol; i++)
{
p(i) = i;
}
//We will makeup work to be 1,
//Since BTF is not supported in our iluk
for(Int b = 0; b < btf_nblks; b++)
{
btf_nnz(b) = 1;
btf_work(b) =1;
}
//printf("Short amd blk\n");
return;
}
//p == length(M)
//Scan over all blks
//Note, that this needs to be made parallel in the
//future (Future Josh will be ok with this, right?)
//This is a horrible way to do this!!!!!
//KLU does this very nice, but they also make all the little blks
INT_1DARRAY temp_col;
MALLOC_INT_1DARRAY(temp_col, M.ncol+1);
INT_1DARRAY temp_row;
MALLOC_INT_1DARRAY(temp_row, M.nnz);
//printf("Done with btf_blk_amd malloc \n");
//printf("blks: %d \n" , btf_nblks);
for(Int b = 0; b < btf_nblks; b++)
{
Int blk_size = btf_tabs(b+1) - btf_tabs(b);
//printf("blk: %d blk_size: %d \n",
// b, blk_size);
if(blk_size < 3)
{
//printf("debug, blk_size: %d \n", blk_size);
for(Int ii = 0; ii < blk_size; ++ii)
{
//printf("set %d \n", btf_tabs(b)+ii-M.scol);
p(ii+btf_tabs(b)) = btf_tabs(b)+ii-M.scol;
}
btf_work(b) = blk_size*blk_size*blk_size;
btf_nnz(b) = (.5*(blk_size*blk_size) + blk_size);
continue;
}
INT_1DARRAY tempp;
MALLOC_INT_1DARRAY(tempp, blk_size+1);
//Fill in temp matrix
Int nnz = 0;
Int column = 1;
temp_col(0) = 0;
for(Int k = btf_tabs(b); k < btf_tabs(b+1); k++)
{
for(Int i = M.col_ptr(k); i < M.col_ptr(k+1); i++)
{
if(M.row_idx(i) < btf_tabs(b))
continue;
temp_row(nnz) = M.row_idx(i) - btf_tabs(b);
nnz++;
}// end over all row_idx
temp_col(column) = nnz;
column++;
}//end over all columns k
#ifdef BASKER_DEBUG_ORDER_AMD
printf("col_ptr: ");
for(Int i = 0 ; i < blk_size+1; i++)
{
printf("%d, ", temp_col(i));
}
printf("\n");
printf("row_idx: ");
for(Int i = 0; i < nnz; i++)
{
printf("%d, ", temp_row(i));
}
printf("\n");
#endif
double l_nnz = 0;
double lu_work = 0;
BaskerSSWrapper<Int>::amd_order(blk_size, &(temp_col(0)),
&(temp_row(0)),&(tempp(0)),
l_nnz, lu_work);
btf_nnz(b) = l_nnz;
btf_work(b) = lu_work;
#ifdef BASKER_DEBUG_ORDER_AMD
printf("blk: %d order: \n", b);
for(Int ii = 0; ii < blk_size; ii++)
{
printf("%d, ", tempp(ii));
}
#endif
//Add to the bigger perm vector
for(Int ii = 0; ii < blk_size; ii++)
{
//printf("loc: %d val: %d \n",
//ii+btf_tabs(b), tempp(ii)+btf_tabs(b));
p(tempp(ii)+btf_tabs(b)) = ii+btf_tabs(b);
}
FREE_INT_1DARRAY(tempp);
}//over all blk_tabs
#ifdef BASKER_DEBUG_AMD_ORDER
printf("blk amd final order\n");
for(Int ii = 0; ii < M.ncol; ii++)
{
printf("%d, ", p(ii));
}
printf("\n");
#endif
FREE_INT_1DARRAY(temp_col);
FREE_INT_1DARRAY(temp_row);
}//end blk_amd()
void Basker<Int,Entry,Exe_Space>::blk_amd(BASKER_MATRIX &M, INT_1DARRAY p)
{
//p == length(M)
//Scan over all blks
//Note, that this needs to be made parallel in the
//future (Future Josh will be ok with this, right?)
//This is a horrible way to do this!!!!!
//KLU does this very nice, but they also make all the little blks
INT_1DARRAY temp_col;
MALLOC_INT_1DARRAY(temp_col, M.ncol+1);
INT_1DARRAY temp_row;
MALLOC_INT_1DARRAY(temp_row, M.nnz);
for(Int b = btf_tabs_offset; b < btf_nblks; b++)
{
Int blk_size = btf_tabs(b+1) - btf_tabs(b);
if(blk_size < 3)
{
//printf("debug, blk_size: %d \n", blk_size);
for(Int ii = 0; ii < blk_size; ++ii)
{
//printf("set %d \n", btf_tabs(b)+ii-M.scol);
p(ii+btf_tabs(b)) = btf_tabs(b)+ii-M.scol;
}
continue;
}
INT_1DARRAY tempp;
MALLOC_INT_1DARRAY(tempp, blk_size+1);
//Fill in temp matrix
Int nnz = 0;
Int column = 1;
temp_col(0) = 0;
for(Int k = btf_tabs(b); k < btf_tabs(b+1); k++)
{
for(Int i = M.col_ptr(k); i < M.col_ptr(k+1); i++)
{
if(M.row_idx(i) < btf_tabs(b))
continue;
temp_row(nnz) = M.row_idx(i) - btf_tabs(b);
nnz++;
}// end over all row_idx
temp_col(column) = nnz;
column++;
}//end over all columns k
#ifdef BASKER_DEBUG_ORDER_AMD
printf("col_ptr: ");
for(Int i = 0 ; i < blk_size+1; i++)
{
printf("%d, ", temp_col(i));
}
printf("\n");
printf("row_idx: ");
for(Int i = 0; i < nnz; i++)
{
printf("%d, ", temp_row(i));
}
printf("\n");
#endif
BaskerSSWrapper<Int>::amd_order(blk_size, &(temp_col(0)),
&(temp_row(0)),&(tempp(0)));
#ifdef BASKER_DEBUG_ORDER_AMD
printf("blk: %d order: \n", b);
for(Int ii = 0; ii < blk_size; ii++)
{
printf("%d, ", tempp(ii));
}
#endif
//Add to the bigger perm vector
for(Int ii = 0; ii < blk_size; ii++)
{
//printf("loc: %d val: %d \n",
//ii+btf_tabs(b), tempp(ii)+btf_tabs(b));
p(tempp(ii)+btf_tabs(b)) = ii+btf_tabs(b);
}
FREE_INT_1DARRAY(tempp);
}//over all blk_tabs
#ifdef BASKER_DEBUG_AMD_ORDER
printf("blk amd final order\n");
for(Int ii = 0; ii < M.ncol; ii++)
{
printf("%d, ", p(ii));
}
printf("\n");
#endif
FREE_INT_1DARRAY(temp_col);
FREE_INT_1DARRAY(temp_row);
}//end blk_amd()
void SeparableConvolution2d(const RowMatrixXf& image,
const Eigen::RowVectorXf& kernel_x,
const Eigen::RowVectorXf& kernel_y,
const BorderType& border_type,
RowMatrixXf* out) {
const int full_size = kernel_x.size();
const int half_size = full_size / 2;
out->resize(image.rows(), image.cols());
// Convolving a vertical filter across rows is the same thing as transpose
// multiply i.e. kernel_y^t * rows. This will give us the convoled value for
// each row. However, care must be taken at the top and bottom borders.
const RowVectorXf reverse_kernel_y = kernel_y.reverse();
if (border_type == REFLECT) {
for (int i = 0; i < half_size; i++) {
const int forward_size = i + half_size + 1;
const int reverse_size = full_size - forward_size;
out->row(i) = kernel_y.tail(forward_size) *
image.block(0, 0, forward_size, image.cols()) +
reverse_kernel_y.tail(reverse_size) *
image.block(1, 0, reverse_size, image.cols());
// Apply the same technique for the end rows.
// TODO(csweeney): Move this to its own loop for cache exposure?
out->row(image.rows() - i - 1) =
kernel_y.head(forward_size) * image.block(image.rows() - forward_size,
0, forward_size,
image.cols()) +
reverse_kernel_y.head(reverse_size) *
image.block(image.rows() - reverse_size - 1, 0, reverse_size,
image.cols());
}
} else {
// Perform border with REPLICATE as the option.
for (int i = 0; i < half_size; i++) {
const int forward_size = i + half_size + 1;
const int reverse_size = full_size - forward_size;
out->row(i) = kernel_y.tail(forward_size) *
image.block(0, 0, forward_size, image.cols()) +
reverse_kernel_y.tail(reverse_size) *
image.row(0).replicate(reverse_size, 1);
// Apply the same technique for the end rows.
out->row(image.rows() - i - 1) =
kernel_y.head(forward_size) * image.block(image.rows() - forward_size,
0, forward_size,
image.cols()) +
reverse_kernel_y.head(reverse_size) *
image.row(image.rows() - 1).replicate(reverse_size, 1);
}
}
// Applying the rest of the y filter.
#ifdef AKAZE_USE_OPENMP
#pragma omp parallel for
#endif
for (int row = half_size; row < image.rows() - half_size; row++) {
out->row(row) =
kernel_y * image.block(row - half_size, 0, full_size, out->cols());
}
// Convolving with the horizontal filter is easy. Rather than using the kernel
// as a sliding indow, we use the row pixels as a sliding window around the
// filter. We prepend and append the proper border values so that we are sure
// to end up with the correct convolved values.
if (border_type == REFLECT) {
RowVectorXf temp_row(image.cols() + full_size - 1);
#ifdef AKAZE_USE_OPENMP
#pragma omp parallel for firstprivate(temp_row)
#endif
for (int row = 0; row < out->rows(); row++) {
temp_row.head(half_size) =
out->row(row).segment(1, half_size).reverse();
temp_row.segment(half_size, image.cols()) = out->row(row);
temp_row.tail(half_size) =
out->row(row)
.segment(image.cols() - 1 - half_size, half_size)
.reverse();
// Convolve the row. We perform the first step here explicitly so that we
// avoid setting the row equal to zero.
out->row(row) = kernel_x(0) * temp_row.head(image.cols());
for (int i = 1; i < full_size; i++) {
out->row(row) += kernel_x(i) * temp_row.segment(i, image.cols());
}
}
} else {
RowVectorXf temp_row(image.cols() + full_size - 1);
#ifdef AKAZE_USE_OPENMP
#pragma omp parallel for firstprivate(temp_row)
#endif
for (int row = 0; row < out->rows(); row++) {
temp_row.head(half_size).setConstant((*out)(row, 0));
temp_row.segment(half_size, image.cols()) = out->row(row);
temp_row.tail(half_size).setConstant((*out)(row, out->cols() - 1));
// Convolve the row. We perform the first step here explicitly so that we
// avoid setting the row equal to zero.
out->row(row) = kernel_x(0) * temp_row.head(image.cols());
for (int i = 1; i < full_size; i++) {
out->row(row) += kernel_x(i) * temp_row.segment(i, image.cols());
}
}
}
}
