Inheritance diagram for gm_to_dirac_short_standard_normal_deviation< T >:

Public Types
using	GSLVectorType = typename gm_to_dirac_approx_standard_normal_distribution_i< T >::GSLVectorType

using	GSLMatrixType = typename gm_to_dirac_approx_standard_normal_distribution_i< T >::GSLMatrixType

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

using	GSLVectorViewType = typename GSLTemplateTypeAlias< T >::VectorViewType

using	GSLMatrixType = typename GSLTemplateTypeAlias< T >::MatrixType

Public Member Functions
bool	approximate (size_t L, size_t N, size_t bMax, T x, const T wX, GslminimizerResult *result=nullptr, const ApproximateOptions &options=ApproximateOptions{}) override
	approximate using raw pointers

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

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

bool	approximate (size_t L, size_t N, size_t bMax, GSLVectorType x, const GSLVectorType wX=nullptr, GslminimizerResult *result=nullptr, const ApproximateOptions &options=ApproximateOptions{}) override
	approximate using gsl vectors

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

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

bool	approximate (size_t L, size_t N, size_t bMax, GSLMatrixType x, const GSLVectorType wX=nullptr, GslminimizerResult *result=nullptr, const ApproximateOptions &options=ApproximateOptions{}) override
	approximate using gsl vectors

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

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

Member Function Documentation

◆ approximate() [1/3]

template<typename T >

bool gm_to_dirac_short_standard_normal_deviation< T >::approximate	(	size_t	L,
		size_t	N,
		size_t	bMax,
		GSLMatrixType *	x,
		const GSLVectorType *	wX = `nullptr`,
		GslminimizerResult *	result = `nullptr`,
		const ApproximateOptions &	options = `ApproximateOptions{}`
	)

overridevirtual

approximate using gsl vectors

Parameters

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

Implements gm_to_dirac_approx_standard_normal_distribution_i< T >.

◆ approximate() [2/3]

template<typename T >

bool gm_to_dirac_short_standard_normal_deviation< T >::approximate	(	size_t	L,
		size_t	N,
		size_t	bMax,
		GSLVectorType *	x,
		const GSLVectorType *	wX = `nullptr`,
		GslminimizerResult *	result = `nullptr`,
		const ApproximateOptions &	options = `ApproximateOptions{}`
	)

overridevirtual

approximate using gsl vectors

Parameters

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

Implements gm_to_dirac_approx_standard_normal_distribution_i< T >.

◆ approximate() [3/3]

template<typename T >

bool gm_to_dirac_short_standard_normal_deviation< T >::approximate	(	size_t	L,
		size_t	N,
		size_t	bMax,
		T *	x,
		const T *	wX,
		GslminimizerResult *	result = `nullptr`,
		const ApproximateOptions &	options = `ApproximateOptions{}`
	)

overridevirtual

approximate using raw pointers

Parameters

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

Implements gm_to_dirac_approx_standard_normal_distribution_i< T >.

◆ modified_van_mises_distance_sq() [1/3]

template<typename T >

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

overridevirtual

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

Implements gm_to_dirac_approx_standard_normal_distribution_i< T >.

◆ modified_van_mises_distance_sq() [2/3]

template<typename T >

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

overridevirtual

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

Implements gm_to_dirac_approx_standard_normal_distribution_i< T >.

◆ modified_van_mises_distance_sq() [3/3]

template<typename T >

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

overridevirtual

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

Implements gm_to_dirac_approx_standard_normal_distribution_i< T >.

◆ modified_van_mises_distance_sq_derivative() [1/3]

template<typename T >

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

overridevirtual

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

Implements gm_to_dirac_approx_standard_normal_distribution_i< T >.

◆ modified_van_mises_distance_sq_derivative() [2/3]

template<typename T >

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

overridevirtual

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

Implements gm_to_dirac_approx_standard_normal_distribution_i< T >.

◆ modified_van_mises_distance_sq_derivative() [3/3]

template<typename T >

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

overridevirtual

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

Implements gm_to_dirac_approx_standard_normal_distribution_i< T >.

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

lib/gm_to_dirac/gm_to_dirac_short_standard_normal_deviation.h
lib/gm_to_dirac/gm_to_dirac_short_standard_normal_deviation.cpp

Public Types

Public Member Functions

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]