lsst.meas.modelfit  15.0-3-g150fc43+8
SemiEmpiricalPrior.h
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23 
24 #ifndef LSST_MEAS_MODELFIT_SemiEmpiricalPrior_h_INCLUDED
25 #define LSST_MEAS_MODELFIT_SemiEmpiricalPrior_h_INCLUDED
26 
27 #include "lsst/pex/config.h"
29 
30 namespace lsst { namespace meas { namespace modelfit {
31 
33 
35  ellipticitySigma, double,
36  "Width of exponential ellipticity distribution (conformal shear units)."
37  );
38 
40  ellipticityCore, double,
41  "Softened core width for ellipticity distribution (conformal shear units)."
42  );
43 
45  logRadiusMinOuter, double,
46  "Minimum ln(radius)."
47  );
48 
50  logRadiusMinInner, double,
51  "ln(radius) at which the softened cutoff begins towards the minimum"
52  );
53 
55  logRadiusMu, double,
56  "Mean of the Student's T distribution used for ln(radius) at large radius, and the transition "
57  "point between a flat distribution and the Student's T."
58  );
59 
61  logRadiusSigma, double,
62  "Width of the Student's T distribution in ln(radius)."
63  );
64 
66  logRadiusNu, double,
67  "Number of degrees of freedom for the Student's T distribution on ln(radius)."
68  );
69 
71  ellipticitySigma(0.3), ellipticityCore(0.001),
72  logRadiusMinOuter(-6.001), logRadiusMinInner(-6.0),
73  logRadiusMu(-1.0), logRadiusSigma(0.45), logRadiusNu(50.0)
74  {}
75 
77  void validate() const;
78 
79 };
80 
84 class SemiEmpiricalPrior : public Prior {
85 public:
86 
88 
89  explicit SemiEmpiricalPrior(Control const & ctrl=Control());
90 
92  Scalar evaluate(
93  ndarray::Array<Scalar const,1,1> const & nonlinear,
94  ndarray::Array<Scalar const,1,1> const & amplitudes
95  ) const override;
96 
98  void evaluateDerivatives(
99  ndarray::Array<Scalar const,1,1> const & nonlinear,
100  ndarray::Array<Scalar const,1,1> const & amplitudes,
101  ndarray::Array<Scalar,1,1> const & nonlinearGradient,
102  ndarray::Array<Scalar,1,1> const & amplitudeGradient,
103  ndarray::Array<Scalar,2,1> const & nonlinearHessian,
104  ndarray::Array<Scalar,2,1> const & amplitudeHessian,
105  ndarray::Array<Scalar,2,1> const & crossHessian
106  ) const override;
107 
109  Scalar marginalize(
110  Vector const & gradient, Matrix const & hessian,
111  ndarray::Array<Scalar const,1,1> const & nonlinear
112  ) const override;
113 
115  Scalar maximize(
116  Vector const & gradient, Matrix const & hessian,
117  ndarray::Array<Scalar const,1,1> const & nonlinear,
118  ndarray::Array<Scalar,1,1> const & amplitudes
119  ) const override;
120 
122  void drawAmplitudes(
123  Vector const & gradient, Matrix const & fisher,
124  ndarray::Array<Scalar const,1,1> const & nonlinear,
125  afw::math::Random & rng,
126  ndarray::Array<Scalar,2,1> const & amplitudes,
127  ndarray::Array<Scalar,1,1> const & weights,
128  bool multiplyWeights=false
129  ) const override;
130 
131 private:
132 
133  struct Impl;
134 
135  PTR(Impl) _impl;
136 };
137 
138 }}} // namespace lsst::meas::modelfit
139 
140 #endif // !LSST_MEAS_MODELFIT_SemiEmpiricalPrior_h_INCLUDED
A piecewise prior motivated by both real distributions and practical considerations.
double Scalar
Typedefs to be used for probability and parameter values.
Definition: common.h:44
double logRadiusMu
"Mean of the Student&#39;s T distribution used for ln(radius) at large radius, and the transition " "poin...
#define PTR(...)
#define LSST_CONTROL_FIELD(NAME, TYPE, DOC)
double logRadiusSigma
"Width of the Student&#39;s T distribution in ln(radius)." ;
Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > Matrix
Typedefs to be used for probability and parameter values.
Definition: common.h:45
Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > Vector
Typedefs to be used for probability and parameter values.
Definition: common.h:46
void validate() const
Raise InvalidParameterException if the configuration options are invalid.
Base class for Bayesian priors.
Definition: Prior.h:36
double logRadiusNu
"Number of degrees of freedom for the Student&#39;s T distribution on ln(radius)." ;
double ellipticityCore
"Softened core width for ellipticity distribution (conformal shear units)." ;
double ellipticitySigma
"Width of exponential ellipticity distribution (conformal shear units)." ;
double logRadiusMinInner
"ln(radius) at which the softened cutoff begins towards the minimum" ;
double logRadiusMinOuter
"Minimum ln(radius)." ;