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LSST Data Management Base Package
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SoftenedLinearPrior.h
Go to the documentation of this file.
1// -*- lsst-c++ -*-
2/*
3 * LSST Data Management System
4 * Copyright 2008-2013 LSST Corporation.
5 *
6 * This product includes software developed by the
7 * LSST Project (http://www.lsst.org/).
8 *
9 * This program is free software: you can redistribute it and/or modify
10 * it under the terms of the GNU General Public License as published by
11 * the Free Software Foundation, either version 3 of the License, or
12 * (at your option) any later version.
13 *
14 * This program is distributed in the hope that it will be useful,
15 * but WITHOUT ANY WARRANTY; without even the implied warranty of
16 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17 * GNU General Public License for more details.
18 *
19 * You should have received a copy of the LSST License Statement and
20 * the GNU General Public License along with this program. If not,
21 * see <http://www.lsstcorp.org/LegalNotices/>.
22 */
23
24#ifndef LSST_MEAS_MODELFIT_SoftenedLinearPrior_h_INCLUDED
25#define LSST_MEAS_MODELFIT_SoftenedLinearPrior_h_INCLUDED
26
27#include "lsst/pex/config.h"
29
30namespace lsst { namespace meas { namespace modelfit {
31
33
35 ellipticityMaxOuter, double,
36 "Maximum ellipticity magnitude (conformal shear units)"
37 );
38
40 ellipticityMaxInner, double,
41 "Ellipticity magnitude (conformal shear units) at which the softened cutoff begins"
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 logRadiusMaxOuter, double,
56 "Maximum ln(radius)"
57 );
58
60 logRadiusMaxInner, double,
61 "ln(radius) at which the softened cutoff begins towards the maximum"
62 );
63
66 "The ratio P(logRadiusMinInner)/P(logRadiusMaxInner)"
67 );
68
75
76};
77
81class SoftenedLinearPrior : public Prior {
82public:
83
85
86 explicit SoftenedLinearPrior(Control const & ctrl=Control());
87
90 ndarray::Array<Scalar const,1,1> const & nonlinear,
91 ndarray::Array<Scalar const,1,1> const & amplitudes
92 ) const override;
93
96 ndarray::Array<Scalar const,1,1> const & nonlinear,
97 ndarray::Array<Scalar const,1,1> const & amplitudes,
98 ndarray::Array<Scalar,1,1> const & nonlinearGradient,
99 ndarray::Array<Scalar,1,1> const & amplitudeGradient,
100 ndarray::Array<Scalar,2,1> const & nonlinearHessian,
101 ndarray::Array<Scalar,2,1> const & amplitudeHessian,
102 ndarray::Array<Scalar,2,1> const & crossHessian
103 ) const override;
104
107 Vector const & gradient, Matrix const & hessian,
108 ndarray::Array<Scalar const,1,1> const & nonlinear
109 ) const override;
110
113 Vector const & gradient, Matrix const & hessian,
114 ndarray::Array<Scalar const,1,1> const & nonlinear,
115 ndarray::Array<Scalar,1,1> const & amplitudes
116 ) const override;
117
120 Vector const & gradient, Matrix const & fisher,
121 ndarray::Array<Scalar const,1,1> const & nonlinear,
122 afw::math::Random & rng,
123 ndarray::Array<Scalar,2,1> const & amplitudes,
124 ndarray::Array<Scalar,1,1> const & weights,
125 bool multiplyWeights=false
126 ) const override;
127
128 Control const & getControl() const { return _ctrl; }
129
130private:
131
132 Scalar _evaluate(ndarray::Array<Scalar const,1,1> const & nonlinear) const;
133
134 Control _ctrl;
135 double _logRadiusP1; // probability value at ln(radius) = ctrl.logRadiusMinInner
136 double _logRadiusSlope;
137 double _logRadiusMinRampFraction;
138 double _logRadiusMaxRampFraction;
139 double _ellipticityMaxRampFraction;
140 Eigen::Matrix<double,4,1,Eigen::DontAlign> _logRadiusPoly1;
141 Eigen::Matrix<double,4,1,Eigen::DontAlign> _logRadiusPoly2;
142 Eigen::Matrix<double,4,1,Eigen::DontAlign> _ellipticityPoly;
143};
144
145}}} // namespace lsst::meas::modelfit
146
147#endif // !LSST_MEAS_MODELFIT_SoftenedLinearPrior_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
Base class for Bayesian priors.
Definition Prior.h:36
A prior that's linear in radius and flat in ellipticity, with a cubic roll-off at the edges.
SoftenedLinearPrior(Control const &ctrl=Control())
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 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.
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.
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.
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.
#define LSST_CONTROL_FIELD(NAME, TYPE, DOC)
A preprocessor macro used to define fields in C++ "control object" structs.
Definition config.h:43
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
double logRadiusMinMaxRatio
"The ratio P(logRadiusMinInner)/P(logRadiusMaxInner)" ;
double ellipticityMaxInner
"Ellipticity magnitude (conformal shear units) at which the softened cutoff begins" ;
double logRadiusMinInner
"ln(radius) at which the softened cutoff begins towards the minimum" ;
double logRadiusMaxInner
"ln(radius) at which the softened cutoff begins towards the maximum" ;
double ellipticityMaxOuter
"Maximum ellipticity magnitude (conformal shear units)" ;