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LSST Data Management Base Package
MixturePrior.h
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23 
24 #ifndef LSST_MEAS_MODELFIT_MixturePrior_h_INCLUDED
25 #define LSST_MEAS_MODELFIT_MixturePrior_h_INCLUDED
26 
29 
30 namespace lsst { namespace meas { namespace modelfit {
31 
35 class MixturePrior : public Prior {
36 public:
37 
38  explicit MixturePrior(PTR(Mixture const) mixture, std::string const & tag="");
39 
42  ndarray::Array<Scalar const,1,1> const & nonlinear,
43  ndarray::Array<Scalar const,1,1> const & amplitudes
44  ) const override;
45 
48  ndarray::Array<Scalar const,1,1> const & nonlinear,
49  ndarray::Array<Scalar const,1,1> const & amplitudes,
50  ndarray::Array<Scalar,1,1> const & nonlinearGradient,
51  ndarray::Array<Scalar,1,1> const & amplitudeGradient,
52  ndarray::Array<Scalar,2,1> const & nonlinearHessian,
53  ndarray::Array<Scalar,2,1> const & amplitudeHessian,
54  ndarray::Array<Scalar,2,1> const & crossHessian
55  ) const override;
56 
59  Vector const & gradient, Matrix const & hessian,
60  ndarray::Array<Scalar const,1,1> const & nonlinear
61  ) const override;
62 
65  Vector const & gradient, Matrix const & hessian,
66  ndarray::Array<Scalar const,1,1> const & nonlinear,
67  ndarray::Array<Scalar,1,1> const & amplitudes
68  ) const override;
69 
72  Vector const & gradient, Matrix const & fisher,
73  ndarray::Array<Scalar const,1,1> const & nonlinear,
74  afw::math::Random & rng,
75  ndarray::Array<Scalar,2,1> const & amplitudes,
76  ndarray::Array<Scalar,1,1> const & weights,
77  bool multiplyWeights=false
78  ) const override;
79 
87 
88  PTR(Mixture const) getMixture() const { return _mixture; }
89 
90 private:
91  PTR(Mixture const) _mixture;
92 };
93 
94 }}} // namespace lsst::meas::modelfit
95 
96 #endif // !LSST_MEAS_MODELFIT_MixturePrior_h_INCLUDED
table::Key< table::Array< double > > amplitudes
#define PTR(...)
Definition: base.h:41
A class that can be used to generate sequences of random numbers according to a number of different a...
Definition: Random.h:57
A prior that's flat in amplitude parameters, and uses a Mixture for nonlinear parameters.
Definition: MixturePrior.h:35
void evaluateDerivatives(ndarray::Array< Scalar const, 1, 1 > const &nonlinear, ndarray::Array< Scalar const, 1, 1 > const &amplitudes, ndarray::Array< Scalar, 1, 1 > const &nonlinearGradient, ndarray::Array< Scalar, 1, 1 > const &amplitudeGradient, ndarray::Array< Scalar, 2, 1 > const &nonlinearHessian, ndarray::Array< Scalar, 2, 1 > const &amplitudeHessian, ndarray::Array< Scalar, 2, 1 > const &crossHessian) const override
Evaluate the derivatives of the prior at the given point in nonlinear and amplitude space.
Scalar marginalize(Vector const &gradient, Matrix const &hessian, ndarray::Array< Scalar const, 1, 1 > const &nonlinear) const override
Return the -log amplitude integral of the prior*likelihood product.
MixturePrior(boost::shared_ptr< Mixture const > mixture, std::string const &tag="")
Scalar maximize(Vector const &gradient, Matrix const &hessian, ndarray::Array< Scalar const, 1, 1 > const &nonlinear, ndarray::Array< Scalar, 1, 1 > const &amplitudes) const override
Compute the amplitude vector that maximizes the prior x likelihood product.
static MixtureUpdateRestriction const & getUpdateRestriction()
Return a MixtureUpdateRestriction appropriate for (e1,e2,r) data.
boost::shared_ptr< Mixture const > getMixture() const
Definition: MixturePrior.h:88
void drawAmplitudes(Vector const &gradient, Matrix const &fisher, ndarray::Array< Scalar const, 1, 1 > const &nonlinear, afw::math::Random &rng, ndarray::Array< Scalar, 2, 1 > const &amplitudes, ndarray::Array< Scalar, 1, 1 > const &weights, bool multiplyWeights=false) const override
Draw a set of Monte Carlo amplitude vectors.
Scalar evaluate(ndarray::Array< Scalar const, 1, 1 > const &nonlinear, ndarray::Array< Scalar const, 1, 1 > const &amplitudes) const override
Evaluate the prior at the given point in nonlinear and amplitude space.
Helper class used to define restrictions to the form of the component parameters in Mixture::updateEM...
Definition: Mixture.h:111
Base class for Bayesian priors.
Definition: Prior.h:36
Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > Vector
Definition: common.h:46
Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > Matrix
Definition: common.h:45
double Scalar
Typedefs to be used for probability and parameter values.
Definition: common.h:44
A base class for image defects.