csr_extract.hpp

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#ifndef STAN_MATH_PRIM_FUN_CSR_EXTRACT_HPP
#define STAN_MATH_PRIM_FUN_CSR_EXTRACT_HPP
 
#include <stan/math/prim/fun/Eigen.hpp>
#include <stan/math/prim/fun/to_ref.hpp>
 
namespace stan {
namespace math {
 
template <typename T>
const std::tuple<Eigen::Matrix<T, Eigen::Dynamic, 1>, std::vector<int>,
                 std::vector<int>>
csr_extract(const Eigen::SparseMatrix<T, Eigen::RowMajor>& A) {
  auto a_nonzeros = A.nonZeros();
  Eigen::Matrix<T, Eigen::Dynamic, 1> w
      = Eigen::Matrix<T, Eigen::Dynamic, 1>::Zero(a_nonzeros);
  std::vector<int> v(a_nonzeros);
  for (int nze = 0; nze < a_nonzeros; ++nze) {
    w[nze] = *(A.valuePtr() + nze);
    v[nze] = *(A.innerIndexPtr() + nze) + stan::error_index::value;
  }
  std::vector<int> u(A.outerSize() + 1);  // last entry is garbage.
  for (int nze = 0; nze <= A.outerSize(); ++nze) {
    u[nze] = *(A.outerIndexPtr() + nze) + stan::error_index::value;
  }
  return std::make_tuple(std::move(w), std::move(v), std::move(u));
}
 
/* Extract the non-zero values from a dense matrix by converting
 * to sparse and calling the sparse matrix extractor.
 *
 * @tparam T type of elements in the matrix
 * @tparam R number of rows, can be Eigen::Dynamic
 * @tparam C number of columns, can be Eigen::Dynamic
 *
 * @param[in] A dense matrix.
 * @return a tuple W,V,U.
 */
template <typename T, require_eigen_dense_base_t<T>* = nullptr>
const std::tuple<Eigen::Matrix<scalar_type_t<T>, Eigen::Dynamic, 1>,
                 std::vector<int>, std::vector<int>>
csr_extract(const T& A) {
  // conversion to sparse seems to touch data twice, so we need to call to_ref
  Eigen::SparseMatrix<scalar_type_t<T>, Eigen::RowMajor> B
      = to_ref(A).sparseView();
  return csr_extract(B);
}
  // end of csr_format group
 
}  // namespace math
}  // namespace stan
 
#endif