lsst::meas::modelfit Namespace Reference

cmodel

common

detail

display

optimizer

pixelFitRegion

priors

psf

version

## Classes

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 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

typedef Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > Vector

typedef afw::table::Key< ScalarScalarKey

typedef afw::table::Key< afw::table::Array< Scalar > > ArrayKey

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

## 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...

## ◆ ArrayKey

Definition at line 48 of file common.h.

## ◆ Matrix

 typedef Eigen::Matrix lsst::meas::modelfit::Matrix

Definition at line 45 of file common.h.

## ◆ ModelVector

 typedef std::vector > lsst::meas::modelfit::ModelVector

Definition at line 41 of file Model.h.

## ◆ Pixel

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

Typedefs to be used for pixel values.

Examples

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

Definition at line 47 of file common.h.

## ◆ Vector

 typedef Eigen::Matrix lsst::meas::modelfit::Vector

Definition at line 46 of file common.h.

## ◆ 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.