lsst.meas.modelfit  22.0.1-1-g1b65d06+20f06bd13f
AdaptiveImportanceSampler.h
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23 
24 #ifndef LSST_MEAS_MODELFIT_AdaptiveImportanceSampler_h_INCLUDED
25 #define LSST_MEAS_MODELFIT_AdaptiveImportanceSampler_h_INCLUDED
26 
27 #include <map>
28 
29 #include "lsst/pex/config.h"
30 #include "lsst/afw/table/Schema.h"
33 
34 namespace lsst { namespace meas { namespace modelfit {
35 
42 public:
43  LSST_CONTROL_FIELD(nSamples, int, "Number of Monte Carlo samples to draw");
44  LSST_CONTROL_FIELD(nUpdateSteps, int, "Number of Expectation-Maximization update iterations");
45  LSST_CONTROL_FIELD(tau1, double, "Damping parameter for E-M update (see Mixture::updateEM)");
46  LSST_CONTROL_FIELD(tau2, double, "Damping parameter for E-M update (see Mixture::updateEM)");
47  LSST_CONTROL_FIELD(
48  targetPerplexity, double,
49  "Minimum value for normalized perplexity after this iteration; if the actual value is less "
50  "than this, this iteration will be repeated up to maxRepeat times until the target is met. "
51  "In addition, if any previous iteration meets this target, this iteration will be skipped."
52  );
53  LSST_CONTROL_FIELD(
54  maxRepeat, int,
55  "Maximum number of times this iteration will be repeated to meet the perplexityTarget"
56  );
57 
59  nSamples(2000), nUpdateSteps(2), tau1(1E-4), tau2(0.5), targetPerplexity(1.0), maxRepeat(0)
60  {}
61 };
62 
71 public:
72 
84  afw::table::Schema & sampleSchema,
87  bool doSaveIterations=false
88  );
89 
90  void run(
91  SamplingObjective const & objective,
92  PTR(Mixture) proposal,
93  afw::table::BaseCatalog & samples
94  ) const override;
95 
97 
99 
100 private:
101  bool _doSaveIterations;
102  PTR(afw::math::Random) _rng;
104  afw::table::Key<Scalar> _weightKey;
105  afw::table::Key<Scalar> _objectiveKey;
106  afw::table::Key<Scalar> _proposalKey;
108  afw::table::Key<int> _iterCtrlKey;
109  afw::table::Key<int> _iterRepeatKey;
110 };
111 
112 }}} // namespace lsst::meas::modelfit
113 
114 #endif // !LSST_MEAS_MODELFIT_AdaptiveImportanceSampler_h_INCLUDED
#define PTR(...)
Sampler class that performs Monte Carlo sampling, while iteratively updating the analytic distributio...
double computeNormalizedPerplexity(afw::table::BaseCatalog const &samples) const
double computeEffectiveSampleSizeFraction(afw::table::BaseCatalog const &samples) const
void run(SamplingObjective const &objective, boost::shared_ptr< Mixture > proposal, afw::table::BaseCatalog &samples) const override
AdaptiveImportanceSampler(afw::table::Schema &sampleSchema, boost::shared_ptr< afw::math::Random > rng, std::map< int, ImportanceSamplerControl > const &ctrls, bool doSaveIterations=false)
Construct a new sampler.
Control object for one iteration of adaptive importance sampling.
double targetPerplexity
"Minimum value for normalized perplexity after this iteration; if the actual value is less " "than th...
int nUpdateSteps
"Number of Expectation-Maximization update iterations" ;
double tau2
"Damping parameter for E-M update (see Mixture::updateEM)" ;
int maxRepeat
"Maximum number of times this iteration will be repeated to meet the perplexityTarget" ;
double tau1
"Damping parameter for E-M update (see Mixture::updateEM)" ;
int nSamples
"Number of Monte Carlo samples to draw" ;