A collection of Sources comprising a ParametricModel of Data. More...

#include <model.h>

Inheritance diagram for lsst::gauss2d::fit::Model< T, Image, Indices, Mask >:

Public Types
typedef GaussianEvaluator< T, Image, Indices >	Evaluator

typedef std::map< std::reference_wrapper< const Channel >, std::vector< std::unique_ptr< const Gaussians > > >	GaussiansMap

typedef Data< T, Image, Mask >	ModelData

typedef ModelData::Observation	Observation

Public Member Functions
	Model (std::shared_ptr< const ModelData > data, PsfModels &psfmodels, Sources &sources, Priors &priors)

void	add_extra_param_map (const Channel &channel, ExtraParamMap &map_extra, const GradParamMap &map_grad, ParameterMap &offsets) const override

void	add_extra_param_factors (const Channel &channel, ExtraParamFactors &factors) const override

void	add_grad_param_map (const Channel &channel, GradParamMap &map, ParameterMap &offsets) const override

void	add_grad_param_factors (const Channel &channel, GradParamFactors &factors) const override

std::vector< double >	compute_loglike_grad (bool include_prior=true, bool print=false, bool verify=false, double findiff_frac=1e-5, double findiff_add=1e-5, double rtol=1e-3, double atol=1e-8)

std::unique_ptr< Image >	compute_hessian (bool transformed=false, bool include_prior=true, std::optional< HessianOptions > options=std::nullopt, bool print=false)

std::vector< double >	evaluate (bool print=false, bool normalize_loglike=false)

double	evaluate_observation (size_t idx)

std::shared_ptr< const ModelData >	get_data () const
	Return _data.

EvaluatorMode	get_mode () const

std::unique_ptr< const lsst::gauss2d::Gaussians >	get_gaussians (const Channel &channel) const override

std::vector< double >	get_loglike_const_terms ()

size_t	get_n_gaussians (const Channel &channel) const override
	Return the number of Gaussian sub-components controlled by this model.

std::vector< std::shared_ptr< Image > >	get_outputs () const
	Return _outputs (output Image instances for each Observation in _data)

std::vector< std::pair< ParamBaseCRef, size_t > >	get_offsets_parameters () const

ParamRefs &	get_parameters (ParamRefs &params, ParamFilter *filter=nullptr) const override

ParamCRefs &	get_parameters_const (ParamCRefs &params, ParamFilter *filter=nullptr) const override
	Same as get_parameters(), but for const refs.

ParamRefs &	get_parameters_observation (ParamRefs &params, size_t idx, ParamFilter *filter=nullptr) const
	Same as get_parameters(), but for a single Observation with index idx in _data.

ParamCRefs &	get_parameters_observation_const (ParamCRefs &params, size_t idx, ParamFilter *filter=nullptr) const
	Same as get_parameters_const(), but for a single Observation with index idx in _data.

Priors	get_priors () const
	Return _priors, the list of Prior instances.

PsfModels	get_psfmodels () const
	Return _psfmodels, the list of PsfModel instances for each Observation in _data.

Sources	get_sources () const
	Return _sources, the list of Source instances for each Observation in _data.

void	set_extra_param_factors (const Channel &channel, ExtraParamFactors &factors, size_t index) const override

void	set_grad_param_factors (const Channel &channel, GradParamFactors &factors, size_t index) const override

void	setup_evaluators (EvaluatorMode mode=EvaluatorMode::image, std::vector< std::vector< std::shared_ptr< Image > > > outputs={}, std::vector< std::shared_ptr< Image > > residuals={}, std::vector< std::shared_ptr< Image > > outputs_prior={}, std::shared_ptr< Image > residuals_prior=nullptr, bool force=false, bool print=false)

size_t	size () const
	Get the size of this->_data.

std::string	repr (bool name_keywords=false, std::string_view namespace_separator=Object::CC_NAMESPACE_SEPARATOR) const override

std::string	str () const override

std::vector< std::string >	verify_jacobian (double findiff_frac=1e-5, double findiff_add=1e-5, double rtol=1e-3, double atol=1e-3, double max_ll_diff=0)

Public Member Functions inherited from lsst::gauss2d::fit::Parametric
ParamRefs	get_parameters_new (ParamFilter *filter=nullptr) const
	Same as get_parameters(), but returning a new vector.

ParamCRefs	get_parameters_const_new (ParamFilter *filter=nullptr) const
	Same as get_parameters_const(), but returning a new vector.

Detailed Description

template<typename T, typename Image, typename Indices, typename Mask>
class lsst::gauss2d::fit::Model< T, Image, Indices, Mask >

A collection of Sources comprising a ParametricModel of Data.

Template Parameters

T	The type of the Image (usually float or double).
Image	The class of image used in Data.
Indices	The class of index map used in Data.
Mask	The class of mask used in Data.

Constructor & Destructor Documentation

◆ Model()

template<typename T, typename Image, typename Indices, typename Mask>

lsst::gauss2d::fit::Model< T, Image, Indices, Mask >::Model	(	std::shared_ptr< const ModelData >	data,
		PsfModels &	psfmodels,
		Sources &	sources,
		Priors &	priors )

inlineexplicit

Construct a Model instance from Data, PsfModels, Sources and Priors.

Parameters

data	The data to model.
psfmodels	A vector of PSF models, ordered to match each Observation in data.
sources	A vector of Source models.
priors	A vector of Prior likelihoods.

Member Function Documentation

◆ add_extra_param_factors()

template<typename T, typename Image, typename Indices, typename Mask>

void lsst::gauss2d::fit::Model< T, Image, Indices, Mask >::add_extra_param_factors	(	const Channel &	channel,
		ExtraParamFactors &	factors ) const

inlineoverridevirtual

Add extra Parameter gradient factors to an existing vector.

Parameters

channel	The Channel to add factors for.
factors	The ExtraParamFactors to add to.

Implements lsst::gauss2d::fit::ParametricModel.

◆ add_extra_param_map()

template<typename T, typename Image, typename Indices, typename Mask>

void lsst::gauss2d::fit::Model< T, Image, Indices, Mask >::add_extra_param_map	(	const Channel &	channel,
		ExtraParamMap &	map_extra,
		const GradParamMap &	map_grad,
		ParameterMap &	offsets ) const

inlineoverridevirtual

Add extra Parameter indices to a map.

Parameters

channel	The Channel to add indices for.
map_extra	The ExtraParamMap to add to.
map_grad	The completed GradParamMap.
offsets	A map of index offsets for Parameters that have already been added.

Implements lsst::gauss2d::fit::ParametricModel.

◆ add_grad_param_factors()

template<typename T, typename Image, typename Indices, typename Mask>

void lsst::gauss2d::fit::Model< T, Image, Indices, Mask >::add_grad_param_factors	(	const Channel &	channel,
		GradParamFactors &	factors ) const

inlineoverridevirtual

Add Parameter gradient factors to an existing map.

Parameters

channel	The Channel to add factors for.
factors	The GradParamFactors to add to.

Implements lsst::gauss2d::fit::ParametricModel.

◆ add_grad_param_map()

template<typename T, typename Image, typename Indices, typename Mask>

void lsst::gauss2d::fit::Model< T, Image, Indices, Mask >::add_grad_param_map	(	const Channel &	channel,
		GradParamMap &	map,
		ParameterMap &	offsets ) const

inlineoverridevirtual

Add Parameter gradient indices to an existing map.

Parameters

channel	The Channel to add indices for.
map	The map to add to.
offsets	A map of index offsets for Parameters that have already been added.

Implements lsst::gauss2d::fit::ParametricModel.

◆ compute_hessian()

template<typename T, typename Image, typename Indices, typename Mask>

std::unique_ptr< Image > lsst::gauss2d::fit::Model< T, Image, Indices, Mask >::compute_hessian	(	bool	transformed = false,
		bool	include_prior = true,
		std::optional< HessianOptions >	options = std::nullopt,
		bool	print = false )

inline

Compute the Hessian matrix (second order partial derivatives) of the log likehood.

Parameters

transformed	Whether the matrix should be computed for transformed parameters or not If not, parameter transforms are temporarily removed.
include_prior	Whether to include the prior likelihood(s) in the Hessian.
options	Options for computing the Hessian via finite differencing of loglikelihood gradients. If null, the Hessian is estimated as J^T J (where J is the Jacobian).

Returns: The Hessian matrix for all free parameters.

◆ compute_loglike_grad()

template<typename T, typename Image, typename Indices, typename Mask>

std::vector< double > lsst::gauss2d::fit::Model< T, Image, Indices, Mask >::compute_loglike_grad	(	bool	include_prior = true,
		bool	print = false,
		bool	verify = false,
		double	findiff_frac = 1e-5,
		double	findiff_add = 1e-5,
		double	rtol = 1e-3,
		double	atol = 1e-8 )

inline

Compute the gradient (partial first derivative) of the log-likelihood for each free parameter.

Parameters

include_prior	Whether to include the prior likelihood(s) in the gradients.
print	Whether to print diagnostic/debugging information.
verify	Whether to verify the values by comparing to finite differences.
findiff_frac	The value of the finite difference increment, as a fraction of the parameter value.
findiff_add	The minimum value of the finite difference increment.
rtol	The allowed relative tolerance in the Jacobian as compared to the finite difference.
atol	The allowed absolute tolerance in the Jacobian as compared to the finite difference.

Returns: The gradient of the log likelihoods for the free parameters of the model, in the order returned by get_parameters.

◆ evaluate()

template<typename T, typename Image, typename Indices, typename Mask>

std::vector< double > lsst::gauss2d::fit::Model< T, Image, Indices, Mask >::evaluate	(	bool	print = false,
		bool	normalize_loglike = false )

inline

Evaluate the model for every Observation in _data.

Parameters

print	Whether to print diagnostic statements to stdout.
normalize_loglike	Whether to include the normalizing (variance-dependent) term in the log likelihood. If false, the log likelihood for a model with no residuals is 0.

Returns: The log likelihood of each observation, followed by the summed log likelihood of the priors.

◆ evaluate_observation()

template<typename T, typename Image, typename Indices, typename Mask>

double lsst::gauss2d::fit::Model< T, Image, Indices, Mask >::evaluate_observation ( size_t idx )

inline

Evaluate a single observation with the given index in _data.

Parameters

idx	The numeric index of the observation.

Returns: The log likelihood of the model for that observation.

◆ get_gaussians()

template<typename T, typename Image, typename Indices, typename Mask>

std::unique_ptr< const lsst::gauss2d::Gaussians > lsst::gauss2d::fit::Model< T, Image, Indices, Mask >::get_gaussians ( const Channel & channel ) const

inlineoverridevirtual

Return the vector of Gaussian sub-components controlled by this model.

Parameters

channel The Channel to return Gaussians for.

Returns: The Gaussians controlled by this model.

Implements lsst::gauss2d::fit::ParametricModel.

◆ get_loglike_const_terms()

template<typename T, typename Image, typename Indices, typename Mask>

std::vector< double > lsst::gauss2d::fit::Model< T, Image, Indices, Mask >::get_loglike_const_terms ( )

inline

Get the constant (variance-dependent) terms of the log likelihood for each observation.

Returns: A vector with the constant terms for each observation, and a final term for the priors.

◆ get_n_gaussians()

template<typename T, typename Image, typename Indices, typename Mask>

size_t lsst::gauss2d::fit::Model< T, Image, Indices, Mask >::get_n_gaussians ( const Channel & channel ) const

inlineoverridevirtual

Return the number of Gaussian sub-components controlled by this model.

Implements lsst::gauss2d::fit::ParametricModel.

◆ get_parameters()

template<typename T, typename Image, typename Indices, typename Mask>

ParamRefs & lsst::gauss2d::fit::Model< T, Image, Indices, Mask >::get_parameters	(	ParamRefs &	params,
		ParamFilter *	filter = nullptr ) const

inlineoverridevirtual

Add Parameter refs matching the filter to a vector, in order.

Parameters

params	The vector to add to.
filter	The filter to apply to this Object's parameters.

Returns: A ref to params (for method chaining)

Implements lsst::gauss2d::fit::Parametric.

◆ get_parameters_const()

template<typename T, typename Image, typename Indices, typename Mask>

ParamCRefs & lsst::gauss2d::fit::Model< T, Image, Indices, Mask >::get_parameters_const	(	ParamCRefs &	params,
		ParamFilter *	filter = nullptr ) const

inlineoverridevirtual

Same as get_parameters(), but for const refs.

Implements lsst::gauss2d::fit::Parametric.

◆ set_extra_param_factors()

template<typename T, typename Image, typename Indices, typename Mask>

void lsst::gauss2d::fit::Model< T, Image, Indices, Mask >::set_extra_param_factors	(	const Channel &	channel,
		ExtraParamFactors &	factors,
		size_t	index ) const

inlineoverridevirtual

Set extra Parameter gradient factors in an existing map.

Parameters

channel	The Channel to set factors for.
factors	The ExtraParamFactors to set factors for.
index	The index to begin setting factors at.

Implements lsst::gauss2d::fit::ParametricModel.

◆ set_grad_param_factors()

template<typename T, typename Image, typename Indices, typename Mask>

void lsst::gauss2d::fit::Model< T, Image, Indices, Mask >::set_grad_param_factors	(	const Channel &	channel,
		GradParamFactors &	factors,
		size_t	index ) const

inlineoverridevirtual

Set Parameter gradient factors in an existing map.

Parameters

channel	The Channel to set factors for.
factors	The GradParamFactors to set factors for.
index	The index to begin setting factors at.

Implements lsst::gauss2d::fit::ParametricModel.

◆ setup_evaluators()

template<typename T, typename Image, typename Indices, typename Mask>

void lsst::gauss2d::fit::Model< T, Image, Indices, Mask >::setup_evaluators	(	EvaluatorMode	mode = EvaluatorMode::image,
		std::vector< std::vector< std::shared_ptr< Image > > >	outputs = {},
		std::vector< std::shared_ptr< Image > >	residuals = {},
		std::vector< std::shared_ptr< Image > >	outputs_prior = {},
		std::shared_ptr< Image >	residuals_prior = nullptr,
		bool	force = false,
		bool	print = false )

inline

Setup Evaluator instances for every Observation in _data using the given EvaluatorMode.

Parameters

mode	The EvaluatorMode to use for all Evaluator instances.
outputs	A vector of vectors of Image outputs for each Evaluator (created if empty and needed).
residuals	A vector of residual Images for each Evaluator (created if empty and needed).
outputs_prior	A vector of prior output (Jacobian) Images for each Evaluator (created if empty and needed).
residuals_prior	A vector of prior residual Images for each Evaluator (created if empty and needed).
force	Whether to force setting up even if already set up in the same mode
print	Whether to print diagnostic statements to stdout.

Note: Different modes require different sized vectors for outputs EvaluatorMode::jacobian requires one Image per free parameter per Observation. EvaluatorMode::loglike_grad requires only one Image(n_rows=1, n_cols=n_params_free)

◆ verify_jacobian()

template<typename T, typename Image, typename Indices, typename Mask>

std::vector< std::string > lsst::gauss2d::fit::Model< T, Image, Indices, Mask >::verify_jacobian	(	double	findiff_frac = 1e-5,
		double	findiff_add = 1e-5,
		double	rtol = 1e-3,
		double	atol = 1e-3,
		double	max_ll_diff = 0 )

inline

Verify that the Jacobian is correct by comparing to finite differences.

Parameters

findiff_frac	The value of the finite difference increment, as a fraction of the parameter value.
findiff_add	The minimum value of the finite difference increment.
rtol	The allowed relative tolerance in the Jacobian as compared to the finite difference.
atol	The allowed absolute tolerance in the Jacobian as compared to the finite difference.
max_ll_diff	The maximum allowed difference between equivalent log-likelihood evaluations. Must be >= 0 and should only be a few orders of magnitude larger than machine epsilon.

Returns: A vector of error messages, one for each Parameter that failed verification.

Note: Verification is done using isclose(), which is modelled after Python's numpy.isclose.

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

include/lsst/gauss2d/fit/model.h

Public Types

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ Model()

Member Function Documentation

◆ add_extra_param_factors()

◆ add_extra_param_map()

◆ add_grad_param_factors()

◆ add_grad_param_map()

◆ compute_hessian()

◆ compute_loglike_grad()

◆ evaluate()

◆ evaluate_observation()

◆ get_gaussians()

◆ get_loglike_const_terms()

◆ get_n_gaussians()

◆ get_parameters()

◆ get_parameters_const()

◆ set_extra_param_factors()

◆ set_grad_param_factors()

◆ setup_evaluators()

◆ verify_jacobian()