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casa
$Rev:20696$
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casa
$Rev:20696$
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Linear least-squares fit using Singular Value Decomposition method. More...
#include <LinearFitSVD.h>
Public Member Functions | |
| LinearFitSVD () | |
| Create a fitter: the normal way to generate a fitter object. | |
| LinearFitSVD (const LinearFitSVD &other) | |
| Copy constructor (deep copy) | |
| LinearFitSVD & | operator= (const LinearFitSVD &other) |
| Assignment (deep copy) | |
| virtual | ~LinearFitSVD () |
| Destructor. | |
Linear least-squares fit using Singular Value Decomposition method.
Solves the linear least-squares fit problem using the singular value decomposition method.
The operation, calls and results are identical to those for the LinearFit class. The only difference is a collinearity default of 1e-8 rather than 0. The actual calculations do a singular value decomposition solution. A method exists to get the constraints used in solving for missing rank.
The creation of this class was driven by the need to provide users with a reliable least-squares fit method. "Numerical Recipes" recommends that singular value decomposition (SVD) method be always used for linear least-squares problems, because of its robustness. Not everybody agrees with this.
Definition at line 72 of file LinearFitSVD.h.
| casa::LinearFitSVD< T >::LinearFitSVD | ( | ) |
Create a fitter: the normal way to generate a fitter object.
Necessary data will be deduced from the Functional provided with setFunction()
| casa::LinearFitSVD< T >::LinearFitSVD | ( | const LinearFitSVD< T > & | other | ) |
Copy constructor (deep copy)
| virtual casa::LinearFitSVD< T >::~LinearFitSVD | ( | ) | [virtual] |
Destructor.
| LinearFitSVD& casa::LinearFitSVD< T >::operator= | ( | const LinearFitSVD< T > & | other | ) |
Assignment (deep copy)
1.8.0
