LSST Applications g0aad566f14+98bdd3b178,g0dd44d6229+e0c2448475,g2079a07aa2+86d27d4dc4,g2305ad1205+28192cebb5,g2bbee38e9b+047b288a59,g337abbeb29+047b288a59,g33d1c0ed96+047b288a59,g3a166c0a6a+047b288a59,g3d1719c13e+98bdd3b178,g487adcacf7+08349b74a1,g4be5004598+98bdd3b178,g50ff169b8f+96c6868917,g52b1c1532d+585e252eca,g591dd9f2cf+742623b69a,g63cd9335cc+585e252eca,g858d7b2824+98bdd3b178,g88963caddf+0cb8e002cc,g88c60e1ba4+10d1129650,g99cad8db69+26979ce5a5,g9ddcbc5298+9a081db1e4,ga1e77700b3+a912195c07,gae0086650b+585e252eca,gb0e22166c9+60f28cb32d,gb1fda7adca+90d62c6f66,gb3a676b8dc+b4feba26a1,gb4b16eec92+950562e714,gba4ed39666+c2a2e4ac27,gbb8dafda3b+2fe9a6a885,gc120e1dc64+0553034b2f,gc28159a63d+047b288a59,gc3e9b769f7+f8a3f20ab6,gcf0d15dbbd+e0c2448475,gdaeeff99f8+f9a426f77a,gdb0af172c8+f61b8374e1,ge79ae78c31+047b288a59,w.2024.20
LSST Data Management Base Package
Loading...
Searching...
No Matches
Public Member Functions | List of all members
lsst::meas::modelfit::AdaptiveImportanceSampler Class Reference

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

#include <AdaptiveImportanceSampler.h>

Inheritance diagram for lsst::meas::modelfit::AdaptiveImportanceSampler:
lsst::meas::modelfit::Sampler

Public Member Functions

 AdaptiveImportanceSampler (afw::table::Schema &sampleSchema, std::shared_ptr< afw::math::Random > rng, std::map< int, ImportanceSamplerControl > const &ctrls, bool doSaveIterations=false)
 Construct a new sampler.
 
void run (SamplingObjective const &objective, std::shared_ptr< Mixture > proposal, afw::table::BaseCatalog &samples) const override
 
double computeNormalizedPerplexity (afw::table::BaseCatalog const &samples) const
 
double computeEffectiveSampleSizeFraction (afw::table::BaseCatalog const &samples) const
 

Detailed Description

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

Between the iterations defined in the control object, the prior is applied to the samples, and the mixture distribution is updated using expectation-maximization to match the samples.

Definition at line 70 of file AdaptiveImportanceSampler.h.

Constructor & Destructor Documentation

◆ AdaptiveImportanceSampler()

lsst::meas::modelfit::AdaptiveImportanceSampler::AdaptiveImportanceSampler ( afw::table::Schema & sampleSchema,
std::shared_ptr< afw::math::Random > rng,
std::map< int, ImportanceSamplerControl > const & ctrls,
bool doSaveIterations = false )

Construct a new sampler.

Parameters
[in,out]sampleSchemaSchema for the catalog of samples filled by the Sampler; will be modified to include sampler-specific fields.
[in]rngRandom number generator to use to generate samples.
[in]ctrlsVector of control objects that define the iterations.
[in]doSaveIterationsWhether to save intermediate SampleSets and associated proposal distributions.

Member Function Documentation

◆ computeEffectiveSampleSizeFraction()

double lsst::meas::modelfit::AdaptiveImportanceSampler::computeEffectiveSampleSizeFraction ( afw::table::BaseCatalog const & samples) const

◆ computeNormalizedPerplexity()

double lsst::meas::modelfit::AdaptiveImportanceSampler::computeNormalizedPerplexity ( afw::table::BaseCatalog const & samples) const

◆ run()

void lsst::meas::modelfit::AdaptiveImportanceSampler::run ( SamplingObjective const & objective,
std::shared_ptr< Mixture > proposal,
afw::table::BaseCatalog & samples ) const
overridevirtual

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