Multi-Variable Random Sampling

Multi-Variable Random Sampling#

2025-10-17

2 min read time

Applies to Linux

Multi-Variable Gaussian#

#include <raft/random/multi_variable_gaussian.hpp>

namespace raft::random

enum class multi_variable_gaussian_decomposition_method#

Matrix decomposition method for multi_variable_gaussian to use.

multi_variable_gaussian can use any of the following methods.

  • CHOLESKY: Uses Cholesky decomposition on the normal equations. This may be faster than the other two methods, but less accurate.

  • JACOBI: Uses the singular value decomposition (SVD) computed with cuSOLVER’s gesvdj algorithm, which is based on the Jacobi method (sweeps of plane rotations). This exposes more parallelism for small and medium size matrices than the QR option below.

  • QR: Uses the SVD computed with cuSOLVER’s gesvd algorithm, which is based on the QR algorithm.

Values:

enumerator CHOLESKY#
enumerator JACOBI#
enumerator QR#
template<typename ValueType>
void multi_variable_gaussian(
raft::resources const &handle,
rmm::device_async_resource_ref mem_resource,
std::optional<raft::device_vector_view<const ValueType, int>> x,
raft::device_matrix_view<ValueType, int, raft::col_major> P,
raft::device_matrix_view<ValueType, int, raft::col_major> X,
const multi_variable_gaussian_decomposition_method method
)#
template<typename ValueType>
void multi_variable_gaussian(
raft::resources const &handle,
std::optional<raft::device_vector_view<const ValueType, int>> x,
raft::device_matrix_view<ValueType, int, raft::col_major> P,
raft::device_matrix_view<ValueType, int, raft::col_major> X,
const multi_variable_gaussian_decomposition_method method
)#