// inserts the sparse matrix 'ins' into the sparse matrix 'original' in the place given by 'row' and 'col' integers
void insertSparseBlock(const Eigen::SparseMatrix<Scalar>& ins, Eigen::SparseMatrix<Scalar>& original, const unsigned int& row, const unsigned int& col)
{
for (int k=0; k<ins.outerSize(); ++k)
for (Eigen::SparseMatrix<Scalar>::InnerIterator iti(ins,k); iti; ++iti)
original.coeffRef(iti.row() + row, iti.col() + col) = iti.value();
original.makeCompressed();
}
// Test that column indexes of values from sparse matrix in sparse
// format are extracted after makeCompressed().
TEST(SparseStuff, csr_extract_v_sparse_compressed) {
stan::math::matrix_d m(2, 3);
Eigen::SparseMatrix<double, Eigen::RowMajor> a;
m << 2.0, 4.0, 6.0, 0.0, 0.0, 0.0;
a = m.sparseView();
a.makeCompressed();
std::vector<int> result = stan::math::csr_extract_v(a);
EXPECT_EQ(1, result[0]);
EXPECT_EQ(2, result[1]);
EXPECT_EQ(3, result[2]);
EXPECT_EQ(3, result.size());
}
// Test that values from a dense matrix in sparse format are extracted
// after A.makeCompressed();
TEST(SparseStuff, csr_extract_w_dense_compressed) {
stan::math::matrix_d m(2, 3);
Eigen::SparseMatrix<double, Eigen::RowMajor> a;
m << 2.0, 4.0, 6.0, 8.0, 10.0, 12.0;
a = m.sparseView();
a.makeCompressed();
stan::math::vector_d result = stan::math::csr_extract_w(a);
EXPECT_FLOAT_EQ( 2.0, result(0));
EXPECT_FLOAT_EQ( 4.0, result(1));
EXPECT_FLOAT_EQ( 6.0, result(2));
EXPECT_FLOAT_EQ( 8.0, result(3));
EXPECT_FLOAT_EQ(10.0, result(4));
EXPECT_FLOAT_EQ(12.0, result(5));
}
void place::scanToSparse(const cv::Mat &scan,
Eigen::SparseMatrix<double> &sparse) {
std::vector<Eigen::Triplet<double>> tripletList;
for (int i = 0; i < scan.rows; ++i) {
const uchar *src = scan.ptr<uchar>(i);
for (int j = 0; j < scan.cols; ++j) {
if (src[j] == 255)
continue;
double confidence = 1.0 - (double)src[j] / 255.0;
tripletList.push_back(Eigen::Triplet<double>(i, j, confidence));
}
}
sparse = Eigen::SparseMatrix<double>(scan.rows, scan.cols);
sparse.setFromTriplets(tripletList.begin(), tripletList.end());
sparse.makeCompressed();
sparse.prune(1.0);
}
Eigen::SparseMatrix<double> joint2conditional(Eigen::SparseMatrix<double> edgePot)// pa is the second dimension
{ // second dimension of edgePot is the parent
Eigen::SparseMatrix<double> Conditional;
Conditional.resize(edgePot.rows(), edgePot.cols());
Eigen::SparseVector<double> Parent_Marginal;
Parent_Marginal.resize(edgePot.cols());
for (int id_col = 0; id_col < edgePot.cols(); id_col++)
{
Eigen::SparseVector<double> tmp_vec = edgePot.block(0, id_col, edgePot.rows(), 1);
Parent_Marginal.coeffRef(id_col) = tmp_vec.sum();
if (Parent_Marginal.coeff(id_col)>TOLERANCE)
for (int id_row = 0; id_row < edgePot.rows(); id_row++)
{
Conditional.coeffRef(id_row, id_col) = edgePot.coeff(id_row, id_col) / Parent_Marginal.coeff(id_col);
}
}
Conditional.makeCompressed();
Conditional.prune(TOLERANCE);
return Conditional;
}
IGL_INLINE void igl::arap_linear_block_spokes(
const MatV & V,
const MatF & F,
const int d,
Eigen::SparseMatrix<Scalar> & Kd)
{
using namespace std;
using namespace Eigen;
// simplex size (3: triangles, 4: tetrahedra)
int simplex_size = F.cols();
// Number of elements
int m = F.rows();
// Temporary output
Matrix<int,Dynamic,2> edges;
Kd.resize(V.rows(), V.rows());
vector<Triplet<Scalar> > Kd_IJV;
if(simplex_size == 3)
{
// triangles
Kd.reserve(7*V.rows());
Kd_IJV.reserve(7*V.rows());
edges.resize(3,2);
edges <<
1,2,
2,0,
0,1;
}else if(simplex_size == 4)
{
// tets
Kd.reserve(17*V.rows());
Kd_IJV.reserve(17*V.rows());
edges.resize(6,2);
edges <<
1,2,
2,0,
0,1,
3,0,
3,1,
3,2;
}
// gather cotangent weights
Matrix<Scalar,Dynamic,Dynamic> C;
cotmatrix_entries(V,F,C);
// should have weights for each edge
assert(C.cols() == edges.rows());
// loop over elements
for(int i = 0;i<m;i++)
{
// loop over edges of element
for(int e = 0;e<edges.rows();e++)
{
int source = F(i,edges(e,0));
int dest = F(i,edges(e,1));
double v = 0.5*C(i,e)*(V(source,d)-V(dest,d));
Kd_IJV.push_back(Triplet<Scalar>(source,dest,v));
Kd_IJV.push_back(Triplet<Scalar>(dest,source,-v));
Kd_IJV.push_back(Triplet<Scalar>(source,source,v));
Kd_IJV.push_back(Triplet<Scalar>(dest,dest,-v));
}
}
Kd.setFromTriplets(Kd_IJV.begin(),Kd_IJV.end());
Kd.makeCompressed();
}
