Namespaces
	cmodel

	common

	detail

	display

	optimizer

	pixelFitRegion

	priors

	psf

	version

Classes
class	AdaptiveImportanceSampler
	Sampler class that performs Monte Carlo sampling, while iteratively updating the analytic distribution from which points are drawn. More...

class	CModelAlgorithm
	Main public interface class for CModel algorithm. More...

struct	CModelControl
	The main control object for CModel, containing parameters for the final linear fit and aggregating the other control objects. More...

struct	CModelResult
	Master result object for CModel, containing results for the final linear fit and three nested CModelStageResult objects for the results of the previous stages. More...

struct	CModelStageControl
	Nested control object for CModel that configures one of the three ("initial", "exp", "dev") nonlinear fitting stages. More...

struct	CModelStageResult
	Result object for a single nonlinear fitting stage of the CModel algorithm. More...

class	DoubleShapeletPsfApproxAlgorithm
	An algorithm that fits a 2-component shapelet approximation to the PSF model. More...

class	DoubleShapeletPsfApproxControl
	Control object used to configure a 2-shapelet fit to a PSF model; see DoubleShapeletPsfApproxAlgorithm. More...

class	EpochFootprint
	An image at one epoch of a galaxy, plus associated info. More...

class	GeneralPsfFitter
	Class for fitting multishapelet models to PSF images. More...

class	GeneralPsfFitterAlgorithm

class	GeneralPsfFitterComponentControl
	Control object used to define one piece of multishapelet fit to a PSF model; see GeneralPsfFitterControl. More...

class	GeneralPsfFitterControl
	Control object used to configure a multishapelet fit to a PSF model; see GeneralPsfFitter. More...

class	ImportanceSamplerControl
	Control object for one iteration of adaptive importance sampling. More...

class	Likelihood
	Base class for optimizer/sampler likelihood functions that compute likelihood at a point. More...

struct	LocalUnitTransform
	A local mapping between two UnitSystems. More...

class	Mixture

class	MixtureComponent
	A weighted Student's T or Gaussian distribution used as a component in a Mixture. More...

class	MixturePrior
	A prior that's flat in amplitude parameters, and uses a Mixture for nonlinear parameters. More...

class	MixtureUpdateRestriction
	Helper class used to define restrictions to the form of the component parameters in Mixture::updateEM. More...

class	Model
	Abstract base class and concrete factories that define multi-shapelet galaxy models. More...

class	MultiModel
	A concrete Model class that simply concatenates several other Models. More...

class	MultiShapeletPsfLikelihood
	Likelihood object used to fit multishapelet models to PSF model images; mostly for internal use by GeneralPsfFitter. More...

class	Optimizer
	A numerical optimizer customized for least-squares problems with Bayesian priors. More...

class	OptimizerControl
	Configuration object for Optimizer. More...

class	OptimizerHistoryRecorder

class	OptimizerObjective
	Base class for objective functions for Optimizer. More...

class	PixelFitRegion

struct	PixelFitRegionControl

class	Prior
	Base class for Bayesian priors. More...

class	Sampler

class	SamplingObjective

class	SemiEmpiricalPrior
	A piecewise prior motivated by both real distributions and practical considerations. More...

struct	SemiEmpiricalPriorControl

class	SoftenedLinearPrior
	A prior that's linear in radius and flat in ellipticity, with a cubic roll-off at the edges. More...

struct	SoftenedLinearPriorControl

class	TruncatedGaussian
	Represents a multidimensional Gaussian function truncated at zero. More...

class	TruncatedGaussianEvaluator
	Helper class for evaluating the -log of a TruncatedGaussian. More...

class	TruncatedGaussianLogEvaluator
	Helper class for evaluating the -log of a TruncatedGaussian. More...

class	TruncatedGaussianSampler
	Helper class for drawing samples from a TruncatedGaussian. More...

struct	UnitSystem
	A simple struct that combines a Wcs and a PhotoCalib. More...

class	UnitTransformedLikelihood
	A concrete Likelihood class that does not require its parameters and data to be in the same UnitSystem. More...

class	UnitTransformedLikelihoodControl
	Control object used to initialize a UnitTransformedLikelihood. More...

Typedefs
typedef std::vector< boost::shared_ptr< Model > >	ModelVector


typedef float	Pixel
	Typedefs to be used for pixel values. More...


typedef double	Scalar
	Typedefs to be used for probability and parameter values. More...

typedef Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic >	Matrix
	Typedefs to be used for probability and parameter values. More...

typedef Eigen::Matrix< Scalar, Eigen::Dynamic, 1 >	Vector
	Typedefs to be used for probability and parameter values. More...

typedef afw::table::Key< Scalar >	ScalarKey
	Typedefs to be used for probability and parameter values. More...

typedef afw::table::Key< afw::table::Array< Scalar > >	ArrayKey
	Typedefs to be used for probability and parameter values. More...

Functions
void	solveTrustRegion (ndarray::Array< Scalar, 1, 1 > const &x, ndarray::Array< Scalar const, 2, 1 > const &F, ndarray::Array< Scalar const, 1, 1 > const &g, double r, double tolerance)
	Solve a symmetric quadratic matrix equation with a ball constraint. More...

Typedef Documentation

◆ ArrayKey

typedef afw::table::Key< afw::table::Array<Scalar> > lsst::meas::modelfit::ArrayKey

Typedefs to be used for probability and parameter values.

Definition at line 48 of file common.h.

◆ Matrix

typedef Eigen::Matrix<Scalar,Eigen::Dynamic,Eigen::Dynamic> lsst::meas::modelfit::Matrix

Typedefs to be used for probability and parameter values.

Definition at line 45 of file common.h.

◆ ModelVector

typedef std::vector<boost::shared_ptr< Model > > lsst::meas::modelfit::ModelVector

Definition at line 39 of file Model.h.

◆ Pixel

typedef float lsst::meas::modelfit::Pixel

Typedefs to be used for pixel values.

Examples:: imageStatistics.cc, maskedImage1.cc, and maskedImage2.cc.

Definition at line 37 of file common.h.

◆ Scalar

typedef double lsst::meas::modelfit::Scalar

Typedefs to be used for probability and parameter values.

Definition at line 44 of file common.h.

◆ ScalarKey

typedef afw::table::Key<Scalar> lsst::meas::modelfit::ScalarKey

Typedefs to be used for probability and parameter values.

Definition at line 47 of file common.h.

◆ Vector

typedef Eigen::Matrix<Scalar,Eigen::Dynamic,1> lsst::meas::modelfit::Vector

Typedefs to be used for probability and parameter values.

Definition at line 46 of file common.h.

Function Documentation

◆ solveTrustRegion()

void lsst::meas::modelfit::solveTrustRegion	(	ndarray::Array< Scalar, 1, 1 > const &	x,
		ndarray::Array< Scalar const, 2, 1 > const &	F,
		ndarray::Array< Scalar const, 1, 1 > const &	g,
		double	r,
		double	tolerance
	)

Solve a symmetric quadratic matrix equation with a ball constraint.

This computes a near-exact solution to the "trust region subproblem" necessary in trust-region-based nonlinear optimizers:

$\min_x{\quad g^T x + \frac{1}{2}x^T F x}\quad\quad\quad \text{s.t.} ||x|| \le r$

The tolerance parameter sets how close to $r$ we require the norm of the solution to be when it lies on the constraint, as a fraction of $r$ itself.

This implementation is based on the algorithm described in Section 4.3 of "Nonlinear Optimization" by Nocedal and Wright.

Namespaces

Classes

Typedefs

Functions

Typedef Documentation

◆ ArrayKey

◆ Matrix

◆ ModelVector

◆ Pixel

◆ Scalar

◆ ScalarKey

◆ Vector

Function Documentation

◆ solveTrustRegion()