SVD_Flags
import { SVD_Flags } from '@banou/opencv-wasm'Use after await initOpenCV(). See the initialization and named imports guide.
Native object: release it with using or delete(). Factories can return null; check before calling methods.
Singular Value Decomposition
Class for computing Singular Value Decomposition of a floating-point matrix. The Singular Value Decomposition is used to solve least-square problems, under-determined linear systems, invert matrices, compute condition numbers, and so on.
If you want to compute a condition number of a matrix or an absolute value of
its determinant, you do not need u and vt. You can pass
flags=SVD::NO_UV|... . Another flag SVD::FULL_UV indicates that full-size u
and vt must be computed, which is not necessary most of the time.
See: invert, solve, eigen, determinant
Constructors and members
static MODIFY_A
allow the algorithm to modify the decomposed matrix; it can save space and speed up processing. currently ignored.
MODIFY_A: SVD_FlagsValue<1>,static NO_UV
indicates that only a vector of singular values w is to be processed, while u and vt
will be set to empty matrices
NO_UV: SVD_FlagsValue<2>,static FULL_UV
when the matrix is not square, by default the algorithm produces u and vt matrices of sufficiently large size for the further A reconstruction; if, however, FULL_UV flag is specified, u and vt will be full-size square orthogonal matrices.
FULL_UV: SVD_FlagsValue<4>These signatures describe this package. Upstream documentation can mention optional backends that are absent from this build. Check runtime compatibility before choosing a backend or file format.