LSSTApplications
20.0.0
LSSTDataManagementBasePackage
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24 #ifndef LSST_MEAS_MODELFIT_MixturePrior_h_INCLUDED
25 #define LSST_MEAS_MODELFIT_MixturePrior_h_INCLUDED
30 namespace lsst {
namespace meas {
namespace modelfit {
42 ndarray::Array<Scalar const,1,1>
const & nonlinear,
43 ndarray::Array<Scalar const,1,1>
const &
amplitudes
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
60 ndarray::Array<Scalar const,1,1>
const & nonlinear
66 ndarray::Array<Scalar const,1,1>
const & nonlinear,
73 ndarray::Array<Scalar const,1,1>
const & nonlinear,
76 ndarray::Array<Scalar,1,1>
const & weights,
77 bool multiplyWeights=
false
96 #endif // !LSST_MEAS_MODELFIT_MixturePrior_h_INCLUDED
Base class for Bayesian priors.
double Scalar
Typedefs to be used for probability and parameter values.
Helper class used to define restrictions to the form of the component parameters in Mixture::updateEM...
static MixtureUpdateRestriction const & getUpdateRestriction()
Return a MixtureUpdateRestriction appropriate for (e1,e2,r) data.
boost::shared_ptr< Mixture const > getMixture() const
A prior that's flat in amplitude parameters, and uses a Mixture for nonlinear parameters.
Scalar maximize(Vector const &gradient, Matrix const &hessian, ndarray::Array< Scalar const, 1, 1 > const &nonlinear, ndarray::Array< Scalar, 1, 1 > const &litudes) const override
Compute the amplitude vector that maximizes the prior x likelihood product.
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="")
Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > Vector
void evaluateDerivatives(ndarray::Array< Scalar const, 1, 1 > const &nonlinear, ndarray::Array< Scalar const, 1, 1 > const &litudes, ndarray::Array< Scalar, 1, 1 > const &nonlinearGradient, ndarray::Array< Scalar, 1, 1 > const &litudeGradient, ndarray::Array< Scalar, 2, 1 > const &nonlinearHessian, ndarray::Array< Scalar, 2, 1 > const &litudeHessian, ndarray::Array< Scalar, 2, 1 > const &crossHessian) const override
Evaluate the derivatives of the prior at the given point in nonlinear and amplitude space.
A base class for image defects.
Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > Matrix
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...
Scalar evaluate(ndarray::Array< Scalar const, 1, 1 > const &nonlinear, ndarray::Array< Scalar const, 1, 1 > const &litudes) const override
Evaluate the prior at the given point in nonlinear and amplitude space.
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 &litudes, ndarray::Array< Scalar, 1, 1 > const &weights, bool multiplyWeights=false) const override
Draw a set of Monte Carlo amplitude vectors.