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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
30namespace lsst { namespace meas { namespace modelfit {
31
35class MixturePrior : public Prior {
36public:
37
38 explicit MixturePrior(std::shared_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,
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 std::shared_ptr<Mixture const> getMixture() const { return _mixture; }
89
90private:
92};
93
94}}} // namespace lsst::meas::modelfit
95
96#endif // !LSST_MEAS_MODELFIT_MixturePrior_h_INCLUDED
table::Key< table::Array< double > > amplitudes
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.
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.
static MixtureUpdateRestriction const & getUpdateRestriction()
Return a MixtureUpdateRestriction appropriate for (e1,e2,r) data.
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.
std::shared_ptr< Mixture const > getMixture() const
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.
MixturePrior(std::shared_ptr< Mixture const > mixture, std::string const &tag="")
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