a base class for LogLikelihood of independent Poisson variables where the mean values are linear combinations of the frames. More...

#include "stir_experimental/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData.h"

Inheritance diagram for stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >:

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Static Public Attributes
static const char *const	registered_name
	Name which will be used when parsing a GeneralisedObjectiveFunction object.

Protected Member Functions
bool	actual_subsets_are_approximately_balanced (std::string &warning_message) const
	Implementation of function that checks subset balancing.
virtual void	set_defaults ()
	Sets defaults for parsing.
virtual void	initialise_keymap ()
	sets parsing keys
virtual bool	post_processing ()
	This will be called at the end of the parsing.
virtual Succeeded	set_up_before_sensitivity (shared_ptr< const TargetT > const &target_sptr)=0
	set-up specifics for the derived class
void	compute_sensitivities ()
	compute subset and total sensitivity
virtual void	actual_compute_subset_gradient_without_penalty (TargetT &gradient, const TargetT &current_estimate, const int subset_num, const bool add_sensitivity)=0
	computes the subset gradient of the objective function without the penalty (optional: add subset sensitivity)
virtual double	actual_compute_objective_function_without_penalty (const TargetT &current_estimate, const int subset_num)=0
	Implementation of function that computes the objective function for the current subset.
virtual Succeeded	actual_add_multiplication_with_approximate_sub_Hessian_without_penalty (TargetT &output, const TargetT &input, const int subset_num) const
	Implementation of the function that multiplies the approximate sub-Hessian with a vector.
virtual Succeeded	actual_accumulate_sub_Hessian_times_input_without_penalty (TargetT &output, const TargetT &current_image_estimate, const TargetT &input, const int subset_num) const
	Implementation of the function computes the sub-Hessian and multiplies by a vector.
virtual void	set_key_values ()
	This will be called before parsing or parameter_info is called.

Protected Attributes
std::string	_input_filename
	Filename with input dynamic projection data.
shared_ptr< DynamicProjData >	_dyn_proj_data_sptr
	points to the object for the total input dynamic projection data
int	_max_segment_num_to_process
	the maximum absolute ring difference number to use in the reconstruction
int	_output_image_size_xy
	the output image size in x and y direction
int	_output_image_size_z
	the output image size in z direction
double	_zoom
	the zoom factor
double	_Xoffset
	offset in the x-direction
double	_Yoffset
	offset in the y-direction
double	_Zoffset
	offset in the z-direction
TimeFrameDefinitions	_frame_defs
std::string	_additive_dyn_proj_data_filename
	name of file in which additive projection data are stored
shared_ptr< DynamicProjData >	_additive_dyn_proj_data_sptr
	points to the additive projection data
shared_ptr< BinNormalisation >	_normalisation_sptr
shared_ptr< ProjectorByBinPair >	_projector_pair_ptr
	Stores the projectors that are used for the computations.
bool	_zero_seg0_end_planes
	signals whether to zero the data in the end planes of the projection data
int	num_subsets
bool	already_set_up
shared_ptr< GeneralisedPrior< TargetT > >	prior_sptr
KeyParser	parser

Additional Inherited Members
Public Member Functions inherited from stir::RegisteredParsingObject< PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >, GeneralisedObjectiveFunction< TargetT >, PoissonLogLikelihoodWithLinearModelForMean< TargetT > >
std::string	get_registered_name () const override
	Returns Derived::registered_name.
std::string	parameter_info () override
	Returns a string with all parameters and their values, in a form suitable for parsing again.
Public Member Functions inherited from stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >
void	compute_sub_gradient_without_penalty (TargetT &gradient, const TargetT &current_estimate, const int subset_num) override
	Compute the subset gradient of the (unregularised) objective function.
virtual void	compute_sub_gradient_without_penalty_plus_sensitivity (TargetT &gradient, const TargetT &current_estimate, const int subset_num)
	This should compute the subset gradient of the (unregularised) objective function plus the subset sensitivity.
Succeeded	set_up (shared_ptr< TargetT > const &target_sptr) override
	set-up sensitivity etc if possible
const TargetT &	get_sensitivity () const
	Get a const reference to the total sensitivity.
const TargetT &	get_subset_sensitivity (const int subset_num) const
	Get a const reference to the sensitivity for a subset.
virtual void	add_subset_sensitivity (TargetT &sensitivity, const int subset_num) const =0
	Add subset sensitivity to existing data.
bool	get_use_subset_sensitivities () const
	find out if subset_sensitivities are used
bool	get_recompute_sensitivity () const
	find current value of recompute_sensitivity
std::string	get_sensitivity_filename () const
	get filename to read (or write) the total sensitivity
std::string	get_subsensitivity_filenames () const
	get filename pattern to read (or write) the subset sensitivities
std::string	get_subsensitivity_filename (const int subset_num) const
	Return the filename for a particular subset.
void	set_recompute_sensitivity (const bool)
void	set_subset_sensitivity_sptr (const shared_ptr< TargetT > &, const int subset_num)
void	set_use_subset_sensitivities (const bool)
	See get_use_subset_sensitivities().
void	set_sensitivity_filename (const std::string &)
	set filename to read (or write) the total sensitivity
void	set_subsensitivity_filenames (const std::string &)
	set filename pattern to read (or write) the subset sensitivities
void	fill_nonidentifiable_target_parameters (TargetT &target, const float value) const override
Public Member Functions inherited from stir::GeneralisedObjectiveFunction< TargetT >
virtual TargetT *	construct_target_ptr () const =0
	Creates a suitable target as determined by the parameters.
virtual void	compute_sub_gradient (TargetT &gradient, const TargetT &current_estimate, const int subset_num)
	Compute the subset-gradient of the objective function at current_estimate.
virtual void	compute_gradient (TargetT &gradient, const TargetT &current_estimate)
	Compute the gradient of the objective function at the current_estimate.
virtual void	compute_gradient_without_penalty (TargetT &gradient, const TargetT &current_estimate)
	Compute the gradient of the unregularised objective function at the current_estimate.
virtual double	compute_objective_function_without_penalty (const TargetT &current_estimate, const int subset_num)
	Compute the value of the unregularised sub-objective function at the current_estimate.
virtual double	compute_objective_function_without_penalty (const TargetT &current_estimate)
	Compute the value of the unregularised objective function at the current_estimate.
double	compute_penalty (const TargetT &current_estimate, const int subset_num)
	Compute the value of the sub-penalty at the current_estimate.
double	compute_penalty (const TargetT &current_estimate)
	Compute the value of the penalty at the current_estimate.
double	compute_objective_function (const TargetT &current_estimate, const int subset_num)
	Compute the value of the sub-objective function at the current_estimate.
double	compute_objective_function (const TargetT &current_estimate)
	Compute the value of the objective function at the current_estimate.
double	compute_value (const TargetT &current_estimate)
	Alias for compute_objective_function(const TargetT&).
Succeeded	add_multiplication_with_approximate_sub_Hessian_without_penalty (TargetT &output, const TargetT &input, const int subset_num) const
	Functions that multiply the approximate (sub)Hessian with a \'vector\'.
Succeeded	add_multiplication_with_approximate_sub_Hessian (TargetT &output, const TargetT &input, const int subset_num) const
Succeeded	add_multiplication_with_approximate_Hessian_without_penalty (TargetT &output, const TargetT &input) const
Succeeded	add_multiplication_with_approximate_Hessian (TargetT &output, const TargetT &input) const
Succeeded	accumulate_Hessian_times_input (TargetT &output, const TargetT &current_image_estimate, const TargetT &input) const
	Functions that multiply the True (sub)Hessian with a \'vector\'.
Succeeded	accumulate_Hessian_times_input_without_penalty (TargetT &output, const TargetT &current_image_estimate, const TargetT &input) const
Succeeded	accumulate_sub_Hessian_times_input (TargetT &output, const TargetT &current_image_estimate, const TargetT &input, const int subset_num) const
Succeeded	accumulate_sub_Hessian_times_input_without_penalty (TargetT &output, const TargetT &current_image_estimate, const TargetT &input, const int subset_num) const
std::string	get_objective_function_values_report (const TargetT &current_estimate)
	Construct a string with info on the value of objective function with and without penalty.
int	get_num_subsets () const
	Return the number of subsets in-use.
virtual std::unique_ptr< ExamInfo >	get_exam_info_uptr_for_target () const
virtual int	set_num_subsets (const int num_subsets)=0
	Attempts to change the number of subsets.
bool	subsets_are_approximately_balanced () const
	Checks of the current subset scheme is approximately balanced.
bool	subsets_are_approximately_balanced (std::string &warning_message) const
	Checks of the current subset scheme is approximately balanced and constructs a warning message.
bool	prior_is_zero () const
	check if the prior is set (or the penalisation factor is 0)
GeneralisedPrior< TargetT > *const	get_prior_ptr () const
	Read-only access to the prior.
shared_ptr< GeneralisedPrior< TargetT > >	get_prior_sptr ()
void	set_prior_sptr (const shared_ptr< GeneralisedPrior< TargetT > > &)
	Change the prior.
virtual void	set_input_data (const shared_ptr< ExamData > &)=0
	set_input_data
virtual const ExamData &	get_input_data () const =0
	get input data
virtual void	set_additive_proj_data_sptr (const shared_ptr< ExamData > &)=0
	set_additive_proj_data_sptr
virtual void	set_normalisation_sptr (const shared_ptr< BinNormalisation > &)=0
	set_normalisation_sptr
Public Member Functions inherited from stir::ParsingObject
	ParsingObject (const ParsingObject &)
ParsingObject &	operator= (const ParsingObject &)
bool	parse (std::istream &f)
bool	parse (const char *const filename)
void	ask_parameters ()
Static Public Member Functions inherited from stir::RegisteredParsingObject< PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >, GeneralisedObjectiveFunction< TargetT >, PoissonLogLikelihoodWithLinearModelForMean< TargetT > >
static GeneralisedObjectiveFunction< TargetT > *	read_from_stream (std::istream *)
	Construct a new object (of type Derived) by parsing the istream.
Static Public Member Functions inherited from stir::RegisteredObject< GeneralisedObjectiveFunction< TargetT > >
static GeneralisedObjectiveFunction< TargetT > *	read_registered_object (std::istream *in, const std::string &registered_name)
	Construct a new object (of a type derived from Root, its actual type determined by the registered_name parameter) by parsing the istream.
static GeneralisedObjectiveFunction< TargetT > *	ask_type_and_parameters ()
	ask the user for the type, and then calls read_registered_object(0, type)
static void	list_registered_names (std::ostream &stream)
	List all possible registered names to the stream.
Protected Types inherited from stir::RegisteredObject< GeneralisedObjectiveFunction< TargetT > >
typedef GeneralisedObjectiveFunction< TargetT > ()	RootFactory(std::istream *)
	The type of a root factory is a function, taking an istream* as argument, and returning a Root*.
typedef FactoryRegistry< std::string, RootFactory, interfile_less >	RegistryType
	The type of the registry.
Static Protected Member Functions inherited from stir::RegisteredObject< GeneralisedObjectiveFunction< TargetT > >
static RegistryType &	registry ()
	Static function returning the registry.

Detailed Description

template<typename TargetT>
class stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >

a base class for LogLikelihood of independent Poisson variables where the mean values are linear combinations of the frames.

Parameters for parsing

Member Function Documentation

◆ actual_subsets_are_approximately_balanced()

template<typename TargetT>

bool stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >::actual_subsets_are_approximately_balanced ( std::string & warning_message ) const

protectedvirtual

Implementation of function that checks subset balancing.

See also: subsets_are_approximately_balanced(std::string&)

Developer\'s note

The reason we have this function is that overloading subsets_are_approximately_balanced(std::string&) in a derived class would hide subsets_are_approximately_balanced().

Implements stir::GeneralisedObjectiveFunction< TargetT >.

References initialise_keymap(), post_processing(), and set_defaults().

◆ set_defaults()

template<typename TargetT>

void stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >::set_defaults ( )

protectedvirtual

Sets defaults for parsing.

Resets sensitivity_filename and sensitivity_sptr and recompute_sensitivity to false.

Reimplemented from stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >.

References _additive_dyn_proj_data_filename, _additive_dyn_proj_data_sptr, _dyn_proj_data_sptr, _input_filename, _max_segment_num_to_process, _normalisation_sptr, _output_image_size_xy, _output_image_size_z, _projector_pair_ptr, _Xoffset, _Yoffset, _zero_seg0_end_planes, _Zoffset, _zoom, stir::ParsingObject::set_defaults(), and set_defaults().

Referenced by actual_subsets_are_approximately_balanced(), and set_defaults().

◆ initialise_keymap()

template<typename TargetT>

void stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >::initialise_keymap ( )

protectedvirtual

sets parsing keys

Has to be called by initialise_keymap in the leaf-class

Reimplemented from stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >.

References _additive_dyn_proj_data_filename, _input_filename, _max_segment_num_to_process, _normalisation_sptr, _output_image_size_xy, _output_image_size_z, _projector_pair_ptr, _zero_seg0_end_planes, _Zoffset, _zoom, stir::ParsingObject::initialise_keymap(), and initialise_keymap().

Referenced by actual_subsets_are_approximately_balanced(), and initialise_keymap().

◆ post_processing()

template<typename TargetT>

bool stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >::post_processing ( )

protectedvirtual

This will be called at the end of the parsing.

Returns: false if everything OK, true if not

Reimplemented from stir::PoissonLogLikelihoodWithLinearModelForMean< TargetT >.

References _additive_dyn_proj_data_filename, _additive_dyn_proj_data_sptr, _dyn_proj_data_sptr, _input_filename, _output_image_size_xy, _output_image_size_z, _zoom, stir::ParsingObject::post_processing(), post_processing(), and stir::warning().

Referenced by actual_subsets_are_approximately_balanced(), and post_processing().

Member Data Documentation

◆ _input_filename

template<typename TargetT>

std::string stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >::_input_filename

protected

Filename with input dynamic projection data.

Referenced by initialise_keymap(), post_processing(), and set_defaults().

◆ _dyn_proj_data_sptr

template<typename TargetT>

shared_ptr<DynamicProjData> stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >::_dyn_proj_data_sptr

protected

points to the object for the total input dynamic projection data

Referenced by post_processing(), and set_defaults().

◆ _max_segment_num_to_process

template<typename TargetT>

int stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >::_max_segment_num_to_process

protected

the maximum absolute ring difference number to use in the reconstruction

convention: if -1, use get_max_segment_num()

Referenced by initialise_keymap(), and set_defaults().

◆ _output_image_size_xy

template<typename TargetT>

int stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >::_output_image_size_xy

protected

the output image size in x and y direction

convention: if -1, use a size such that the whole FOV is covered

Referenced by initialise_keymap(), post_processing(), and set_defaults().

◆ _output_image_size_z

template<typename TargetT>

int stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >::_output_image_size_z

protected

the output image size in z direction

convention: if -1, use default as provided by VoxelsOnCartesianGrid constructor

Referenced by initialise_keymap(), post_processing(), and set_defaults().

◆ _zoom

template<typename TargetT>

double stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >::_zoom

protected

the zoom factor

Referenced by initialise_keymap(), post_processing(), and set_defaults().

◆ _Xoffset

template<typename TargetT>

double stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >::_Xoffset

protected

offset in the x-direction

Referenced by set_defaults().

◆ _Yoffset

template<typename TargetT>

double stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >::_Yoffset

protected

offset in the y-direction

Referenced by set_defaults().

◆ _Zoffset

template<typename TargetT>

double stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >::_Zoffset

protected

offset in the z-direction

Referenced by initialise_keymap(), and set_defaults().

◆ _additive_dyn_proj_data_filename

template<typename TargetT>

std::string stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >::_additive_dyn_proj_data_filename

protected

name of file in which additive projection data are stored

Referenced by initialise_keymap(), post_processing(), and set_defaults().

◆ _additive_dyn_proj_data_sptr

template<typename TargetT>

shared_ptr<DynamicProjData> stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >::_additive_dyn_proj_data_sptr

protected

points to the additive projection data

the projection data in this file is bin-wise added to forward projection results

Referenced by post_processing(), and set_defaults().

◆ _normalisation_sptr

template<typename TargetT>

shared_ptr<BinNormalisation> stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >::_normalisation_sptr

protected

the normalisation or/and attenuation data

Referenced by initialise_keymap(), and set_defaults().

◆ _projector_pair_ptr

template<typename TargetT>

shared_ptr<ProjectorByBinPair> stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >::_projector_pair_ptr

protected

Stores the projectors that are used for the computations.

Referenced by initialise_keymap(), and set_defaults().

◆ _zero_seg0_end_planes

template<typename TargetT>

bool stir::PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData< TargetT >::_zero_seg0_end_planes

protected

signals whether to zero the data in the end planes of the projection data

Referenced by initialise_keymap(), and set_defaults().

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

/home/kris/devel/STIRdistrib/STIR/src/include/stir_experimental/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData.h
/home/kris/devel/STIRdistrib/STIR/src/experimental/recon_buildblock/PoissonLogLikelihoodWithLinearModelForMeanAndDynamicProjData.cxx

Static Public Attributes

Protected Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ actual_subsets_are_approximately_balanced()

◆ set_defaults()

◆ initialise_keymap()

◆ post_processing()

Member Data Documentation

◆ _input_filename

◆ _dyn_proj_data_sptr

◆ _max_segment_num_to_process

◆ _output_image_size_xy

◆ _output_image_size_z

◆ _zoom

◆ _Xoffset

◆ _Yoffset

◆ _Zoffset

◆ _additive_dyn_proj_data_filename

◆ _additive_dyn_proj_data_sptr

◆ _normalisation_sptr

◆ _projector_pair_ptr

◆ _zero_seg0_end_planes