Performs Gauss-Newton weighted least squares computation. More...

#include <wls.h>

Public Member Functions
	WLS (int size=0)
void	clear ()
void	add_prior (Precision val)
template<class B2 >
void	add_prior (const Vector< Size, Precision, B2 > &v)
template<class B2 >
void	add_prior (const Matrix< Size, Size, Precision, B2 > &m)
template<class B2 >
void	add_mJ (Precision m, const Vector< Size, Precision, B2 > &J, Precision weight=1)
template<int N, class B1 , class B2 , class B3 >
void	add_mJ (const Vector< N, Precision, B1 > &m, const Matrix< Size, N, Precision, B2 > &J, const Matrix< N, N, Precision, B3 > &invcov)
template<int N, class B1 , class B2 , class B3 >
void	add_mJ_rows (const Vector< N, Precision, B1 > &m, const Matrix< N, Size, Precision, B2 > &J, const Matrix< N, N, Precision, B3 > &invcov)
template<int N, typename B1 >
void	add_sparse_mJ (const Precision m, const Vector< N, Precision, B1 > &J1, const int index1, const Precision weight=1)
template<int N, int S1, class P1 , class P2 , class P3 , class B1 , class B2 , class B3 >
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)
template<int N, int S1, int S2, class B1 , class B2 , class B3 , class B4 >
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)
void	compute ()
void	operator+= (const WLS &meas)
Matrix< Size, Size, Precision > &	get_C_inv ()
const Matrix< Size, Size, Precision > &	get_C_inv () const
Vector< Size, Precision > &	get_mu ()
const Vector< Size, Precision > &	get_mu () const
Vector< Size, Precision > &	get_vector ()
const Vector< Size, Precision > &	get_vector () const
Decomposition< Size, Precision > &	get_decomposition ()
const Decomposition< Size, Precision > &	get_decomposition () const

Detailed Description

template<int Size = Dynamic, class Precision = DefaultPrecision, template< int DecompSize, class DecompPrecision > class Decomposition = Cholesky>
class TooN::WLS< Size, Precision, Decomposition >

Performs Gauss-Newton weighted least squares computation.

Parameters:

Size	The number of dimensions in the system
Precision	The numerical precision used (double, float etc)
Decomposition	The class used to invert the inverse Covariance matrix (must have one integer size and one typename precision template arguments) this is Cholesky by default, but could also be SQSVD

Member Function Documentation

void add_prior ( Precision val )

Applies a constant regularisation term.

Equates to a prior that says all the parameters are zero with $\sigma^2 = \frac{1}{\text{val}}$ .

Parameters:

val	The strength of the prior

void add_prior ( const Vector< Size, Precision, B2 > & v )

Applies a regularisation term with a different strength for each parameter value.

Equates to a prior that says all the parameters are zero with $\sigma_i^2 = \frac{1}{\text{v}_i}$ .

Parameters:

v	The vector of priors

void add_prior ( const Matrix< Size, Size, Precision, B2 > & m )

Applies a whole-matrix regularisation term.

This is the same as adding the $m$ to the inverse covariance matrix.

Parameters:

m	The inverse covariance matrix to add

void add_mJ	(	Precision	m,
		const Vector< Size, Precision, B2 > &	J,
		Precision	weight = `1`
	)

Add a single measurement.

Parameters:

m	The value of the measurement
J	The Jacobian for the measurement $\frac{\partial\text{m}}{\partial\text{param}_i}$
weight	The inverse variance of the measurement (default = 1)

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)

Parameters:

m	The measurements to add
J	The Jacobian matrix $\frac{\partial\text{m}_i}{\partial\text{param}_j}$
invcov	The inverse covariance of the measurement values

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)

Parameters:

m	The measurements to add
J	The Jacobian matrix $\frac{\partial\text{m}_i}{\partial\text{param}_j}$
invcov	The inverse covariance of the measurement values

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)

Parameters:

m	The measurements to add
J1	The first block of the Jacobian matrix $\frac{\partial\text{m}_i}{\partial\text{param}_j}$
index1	starting index for the first block
invcov	The inverse covariance of the measurement values

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)

Parameters:

m	The measurements to add
J1	The first block of the Jacobian matrix $\frac{\partial\text{m}_i}{\partial\text{param}_j}$
index1	starting index for the first block
invcov	The inverse covariance of the measurement values

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)

Parameters:

m	The measurements to add
J1	The first block of the Jacobian matrix $\frac{\partial\text{m}_i}{\partial\text{param}_j}$
index1	starting index for the first block
J2	The second block of the Jacobian matrix $\frac{\partial\text{m}_i}{\partial\text{param}_j}$
index2	starting index for the second block
invcov	The inverse covariance of the measurement values

void operator+= ( const WLS< Size, Precision, Decomposition > & meas )

Combine measurements from two WLS systems.

Parameters:

meas	The measurements to combine with

Public Member Functions

Detailed Description

template<int Size = Dynamic, class Precision = DefaultPrecision, template< int DecompSize, class DecompPrecision > class Decomposition = Cholesky> class TooN::WLS< Size, Precision, Decomposition >

Member Function Documentation

template<int Size = Dynamic, class Precision = DefaultPrecision, template< int DecompSize, class DecompPrecision > class Decomposition = Cholesky>
class TooN::WLS< Size, Precision, Decomposition >