Performs iterative reweighted least squares. More...
#include <irls.h>
Inheritance diagram for TooN::IRLS< Size, Precision, Reweight >:
Collaboration diagram for TooN::IRLS< Size, Precision, Reweight >:
Public Member Functions | |
| IRLS (int size=Size) | |
| template<int Size2, typename Precision2 , typename Base2 > | |
| void | add_mJ (Precision m, const Vector< Size2, Precision2, Base2 > &J) |
| void | operator+= (const IRLS &meas) |
| Matrix< Size, Size, Precision > & | get_true_C_inv () |
| const Matrix< Size, Size, Precision > & | get_true_C_inv () const |
| Precision | get_residual () |
| void | clear () |
 Public Member Functions inherited from TooN::WLS< Size, Precision > | |
| WLS (int size=0) | |
| Default constructor or construct with the number of dimensions for the Dynamic case. | |
| void | clear () |
| Clear all the measurements and apply a constant regularisation term. | |
| void | add_prior (Precision val) |
| Applies a constant regularisation term. More... | |
| void | add_prior (const Vector< Size, Precision, B2 > &v) |
| Applies a regularisation term with a different strength for each parameter value. More... | |
| void | add_prior (const Matrix< Size, Size, Precision, B2 > &m) |
| Applies a whole-matrix regularisation term. More... | |
| void | add_mJ (Precision m, const Vector< Size, P2, B2 > &J, Precision weight=1) |
| Add a single measurement. More... | |
| void | add_mJ (const Vector< S2, Precision, VB > &m, const Matrix< S2, Size, Precision, B2 > &J, Precision weight=1) |
| Add a several measurements measurement. More... | |
| void | add_mJ (const Vector< N, Precision, B1 > &m, const Matrix< Size, N, Precision, B2 > &J, const Matrix< N, N, Precision, B3 > &invcov) |
| Add multiple measurements at once (much more efficiently) More... | |
| void | add_mJ_rows (const Vector< N, Precision, B1 > &m, const Matrix< N, Size, Precision, B2 > &J, const Matrix< N, N, Precision, B3 > &invcov) |
| Add multiple measurements at once (much more efficiently) More... | |
| void | add_sparse_mJ (const Precision m, const Vector< N, Precision, B1 > &J1, const int index1, const Precision weight=1) |
| Add a single measurement at once with a sparse Jacobian (much, much more efficiently) More... | |
| void | add_sparse_mJ_rows (const Vector< N, P1, B1 > &m, const Matrix< N, S1, P2, B2 > &J1, const int index1, const Matrix< N, N, P3, B3 > &invcov) |
| Add multiple measurements at once with a sparse Jacobian (much, much more efficiently) More... | |
| void | add_sparse_mJ_rows (const Vector< N, Precision, B1 > &m, const Matrix< N, S1, Precision, B2 > &J1, const int index1, const Matrix< N, S2, Precision, B3 > &J2, const int index2, const Matrix< N, N, Precision, B4 > &invcov) |
| Add multiple measurements at once with a sparse Jacobian (much, much more efficiently) More... | |
| void | compute () |
| Process all the measurements and compute the weighted least squares set of parameter values stores the result internally which can then be accessed by calling get_mu() | |
| void | operator+= (const WLS &meas) |
| Combine measurements from two WLS systems. More... | |
| Matrix< Size, Size, Precision > & | get_C_inv () |
| Returns the inverse covariance matrix. | |
| const Matrix< Size, Size, Precision > & | get_C_inv () const |
| Returns the inverse covariance matrix. | |
| Vector< Size, Precision > & | get_mu () |
| Returns the update. With no prior, this is the result of \(J^\dagger e\). | |
| const Vector< Size, Precision > & | get_mu () const |
| Returns the update. With no prior, this is the result of \(J^\dagger e\). | |
| Vector< Size, Precision > & | get_vector () |
| Returns the vector \(J^{\mathsf T} e\). | |
| const Vector< Size, Precision > & | get_vector () const |
| Returns the vector \(J^{\mathsf T} e\). | |
| Cholesky< Size, Precision > & | get_decomposition () |
| Return the decomposition object used to compute \((J^{\mathsf T} J + P)^{-1}\). | |
| const Cholesky< Size, Precision > & | get_decomposition () const |
| Return the decomposition object used to compute \((J^{\mathsf T} J + P)^{-1}\). | |
Detailed Description
template<int Size, typename Precision, template< typename RWPrecision > class Reweight>
class TooN::IRLS< Size, Precision, Reweight >
Performs iterative reweighted least squares.
- Parameters
-
Size the size Reweight The reweighting functor. This structure must provide reweight(), true-scale() and objective() methods. Existing examples are Robust I, Robust II and ILinear.
The documentation for this class was generated from the following file:
- TooN/irls.h
