interface for the gausian mixture to dirac approximation More...

#include <gm_to_dirac_approx_i.h>

Inheritance diagram for gm_to_dirac_approx_i< T >:

Public Types
using	GSLVectorType = typename GSLTemplateTypeAlias< T >::VectorType

using	GSLVectorViewType = typename GSLTemplateTypeAlias< T >::VectorViewType

using	GSLMatrixType = typename GSLTemplateTypeAlias< T >::MatrixType

Public Member Functions
virtual bool	approximate (const T covDiag, size_t L, size_t N, size_t bMax, T x, const T wX, GslminimizerResult result, const ApproximateOptions &options)=0
	approximate using raw pointers

virtual void	modified_van_mises_distance_sq (const T covDiag, T distance, size_t L, size_t N, size_t bMax, T x, const T wX)=0
	calculate modified van mises distance based on standard normal deviation and x

virtual void	modified_van_mises_distance_sq_derivative (const T covDiag, T gradient, size_t L, size_t N, size_t bMax, T x, const T wX)=0
	calculate modified van mises distance based on standard normal deviation and x

virtual bool	approximate (const GSLVectorType covDiag, size_t L, size_t N, size_t bMax, GSLVectorType x, const GSLVectorType wX, GslminimizerResult result, const ApproximateOptions &options)=0
	approximate using gsl vectors

virtual void	modified_van_mises_distance_sq (const GSLVectorType covDiag, T distance, size_t L, size_t N, size_t bMax, GSLVectorType x, const GSLVectorType wX)=0
	calculate modified van mises distance based on standard normal deviation and x

virtual void	modified_van_mises_distance_sq_derivative (const GSLVectorType covDiag, GSLVectorType gradient, size_t L, size_t N, size_t bMax, GSLVectorType x, const GSLVectorType wX)=0
	calculate modified van mises distance based on standard normal deviation and x

virtual bool	approximate (const GSLVectorType covDiag, size_t L, size_t N, size_t bMax, GSLMatrixType x, const GSLVectorType wX, GslminimizerResult result, const ApproximateOptions &options)=0
	approximate using gsl matricies where possible

virtual void	modified_van_mises_distance_sq (const GSLVectorType covDiag, T distance, size_t L, size_t N, size_t bMax, GSLMatrixType x, const GSLVectorType wX)=0
	calculate modified van mises distance based on standard normal deviation and x

virtual void	modified_van_mises_distance_sq_derivative (const GSLVectorType covDiag, GSLMatrixType gradient, size_t L, size_t N, size_t bMax, GSLMatrixType x, const GSLVectorType wX)=0
	calculate modified van mises distance based on standard normal deviation and x

Detailed Description

template<typename T>
class gm_to_dirac_approx_i< T >

interface for the gausian mixture to dirac approximation

Template Parameters

T	type of the vector (float, double)

Member Function Documentation

◆ approximate() [1/3]

template<typename T >

virtual bool gm_to_dirac_approx_i< T >::approximate	(	const GSLVectorType *	covDiag,
		size_t	L,
		size_t	N,
		size_t	bMax,
		GSLMatrixType *	x,
		const GSLVectorType *	wX,
		GslminimizerResult *	result,
		const ApproximateOptions &	options
	)

pure virtual

approximate using gsl matricies where possible

Parameters

covDiag	covariance matrix diagonal
L	number of data points for apprioximation
N	dimension of the data
bMax	bMax
x	first guess for the approximation and return value
wX	weights for the x data points
result	minimizer result
options	options for minimizer

Returns: true, on success, false otherwise

Implemented in gm_to_dirac_short< T >, and gm_to_dirac_short< T >.

◆ approximate() [2/3]

template<typename T >

virtual bool gm_to_dirac_approx_i< T >::approximate	(	const GSLVectorType *	covDiag,
		size_t	L,
		size_t	N,
		size_t	bMax,
		GSLVectorType *	x,
		const GSLVectorType *	wX,
		GslminimizerResult *	result,
		const ApproximateOptions &	options
	)

pure virtual

approximate using gsl vectors

Parameters

covDiag	covariance matrix diagonal
L	number of data points for apprioximation
N	dimension of the data
bMax	bMax
x	first guess for the approximation and return value
wX	weights for the x data points
result	minimizer result
options	options for minimizer

Returns: true, on success, false otherwise

Implemented in gm_to_dirac_short< T >, and gm_to_dirac_short< T >.

◆ approximate() [3/3]

template<typename T >

virtual bool gm_to_dirac_approx_i< T >::approximate	(	const T *	covDiag,
		size_t	L,
		size_t	N,
		size_t	bMax,
		T *	x,
		const T *	wX,
		GslminimizerResult *	result,
		const ApproximateOptions &	options
	)

pure virtual

approximate using raw pointers

Parameters

covDiag	covariance matrix diagonal
L	number of data points for apprioximation
N	dimension of the data
bMax	bMax
x	first guess for the approximation and return value
result	minimizer result
options	options for minimizer

Returns: true, on success, false otherwise

Implemented in gm_to_dirac_short< T >.

◆ modified_van_mises_distance_sq() [1/3]

template<typename T >

virtual void gm_to_dirac_approx_i< T >::modified_van_mises_distance_sq	(	const GSLVectorType *	covDiag,
		T *	distance,
		size_t	L,
		size_t	N,
		size_t	bMax,
		GSLMatrixType *	x,
		const GSLVectorType *	wX
	)

pure virtual

calculate modified van mises distance based on standard normal deviation and x

Parameters

distance	pointer to distance value to be calculated
L	number of data points for approximation
N	dimension of the data
bMax	bMax
x	first guess for the approximation and return value
result	minimizer result
options	options for minimizer

Returns: true, on success, false otherwise

Implemented in gm_to_dirac_short< T >, and gm_to_dirac_short< T >.

◆ modified_van_mises_distance_sq() [2/3]

template<typename T >

virtual void gm_to_dirac_approx_i< T >::modified_van_mises_distance_sq	(	const GSLVectorType *	covDiag,
		T *	distance,
		size_t	L,
		size_t	N,
		size_t	bMax,
		GSLVectorType *	x,
		const GSLVectorType *	wX
	)

pure virtual

calculate modified van mises distance based on standard normal deviation and x

Parameters

distance	pointer to distance value to be calculated
L	number of data points for approximation
N	dimension of the data
bMax	bMax
x	first guess for the approximation and return value
result	minimizer result
options	options for minimizer

Returns: true, on success, false otherwise

Implemented in gm_to_dirac_short< T >, and gm_to_dirac_short< T >.

◆ modified_van_mises_distance_sq() [3/3]

template<typename T >

virtual void gm_to_dirac_approx_i< T >::modified_van_mises_distance_sq	(	const T *	covDiag,
		T *	distance,
		size_t	L,
		size_t	N,
		size_t	bMax,
		T *	x,
		const T *	wX
	)

pure virtual

calculate modified van mises distance based on standard normal deviation and x

Parameters

distance	pointer to distance value to be calculated
L	number of data points for approximation
N	dimension of the data
bMax	bMax
x	first guess for the approximation and return value
result	minimizer result
options	options for minimizer

Returns: true, on success, false otherwise

Implemented in gm_to_dirac_short< T >.

◆ modified_van_mises_distance_sq_derivative() [1/3]

template<typename T >

virtual void gm_to_dirac_approx_i< T >::modified_van_mises_distance_sq_derivative	(	const GSLVectorType *	covDiag,
		GSLMatrixType *	gradient,
		size_t	L,
		size_t	N,
		size_t	bMax,
		GSLMatrixType *	x,
		const GSLVectorType *	wX
	)

pure virtual

calculate modified van mises distance based on standard normal deviation and x

Parameters

gradient	pointer to gradient to be calculated
L	number of data points for approximation
N	dimension of the data
bMax	bMax
x	first guess for the approximation and return value
result	minimizer result
options	options for minimizer

Returns: true, on success, false otherwise

Implemented in gm_to_dirac_short< T >, and gm_to_dirac_short< T >.

◆ modified_van_mises_distance_sq_derivative() [2/3]

template<typename T >

virtual void gm_to_dirac_approx_i< T >::modified_van_mises_distance_sq_derivative	(	const GSLVectorType *	covDiag,
		GSLVectorType *	gradient,
		size_t	L,
		size_t	N,
		size_t	bMax,
		GSLVectorType *	x,
		const GSLVectorType *	wX
	)

pure virtual

calculate modified van mises distance based on standard normal deviation and x

Parameters

gradient	pointer to gradient to be calculated
L	number of data points for approximation
N	dimension of the data
bMax	bMax
x	first guess for the approximation and return value
result	minimizer result
options	options for minimizer

Returns: true, on success, false otherwise

Implemented in gm_to_dirac_short< T >, and gm_to_dirac_short< T >.

◆ modified_van_mises_distance_sq_derivative() [3/3]

template<typename T >

virtual void gm_to_dirac_approx_i< T >::modified_van_mises_distance_sq_derivative	(	const T *	covDiag,
		T *	gradient,
		size_t	L,
		size_t	N,
		size_t	bMax,
		T *	x,
		const T *	wX
	)

pure virtual

calculate modified van mises distance based on standard normal deviation and x

Parameters

gradient	pointer to gradient to be calculated
L	number of data points for approximation
N	dimension of the data
bMax	bMax
x	first guess for the approximation and return value
result	minimizer result
options	options for minimizer

Returns: true, on success, false otherwise

Implemented in gm_to_dirac_short< T >.

The documentation for this class was generated from the following file:

lib/gm_to_dirac/gm_to_dirac_approx_i.h

Public Types

Public Member Functions

Detailed Description

Member Function Documentation

◆ approximate() [1/3]

◆ approximate() [2/3]

◆ approximate() [3/3]

◆ modified_van_mises_distance_sq() [1/3]

◆ modified_van_mises_distance_sq() [2/3]

◆ modified_van_mises_distance_sq() [3/3]

◆ modified_van_mises_distance_sq_derivative() [1/3]

◆ modified_van_mises_distance_sq_derivative() [2/3]

◆ modified_van_mises_distance_sq_derivative() [3/3]