LSSTApplications  17.0+124,17.0+14,17.0+73,18.0.0+37,18.0.0+80,18.0.0-4-g68ffd23+4,18.1.0-1-g0001055+12,18.1.0-1-g03d53ef+5,18.1.0-1-g1349e88+55,18.1.0-1-g2505f39+44,18.1.0-1-g5315e5e+4,18.1.0-1-g5e4b7ea+14,18.1.0-1-g7e8fceb+4,18.1.0-1-g85f8cd4+48,18.1.0-1-g8ff0b9f+4,18.1.0-1-ga2c679d+1,18.1.0-1-gd55f500+35,18.1.0-10-gb58edde+2,18.1.0-11-g0997b02+4,18.1.0-13-gfe4edf0b+12,18.1.0-14-g259bd21+21,18.1.0-19-gdb69f3f+2,18.1.0-2-g5f9922c+24,18.1.0-2-gd3b74e5+11,18.1.0-2-gfbf3545+32,18.1.0-26-g728bddb4+5,18.1.0-27-g6ff7ca9+2,18.1.0-3-g52aa583+25,18.1.0-3-g8ea57af+9,18.1.0-3-gb69f684+42,18.1.0-3-gfcaddf3+6,18.1.0-32-gd8786685a,18.1.0-4-gf3f9b77+6,18.1.0-5-g1dd662b+2,18.1.0-5-g6dbcb01+41,18.1.0-6-gae77429+3,18.1.0-7-g9d75d83+9,18.1.0-7-gae09a6d+30,18.1.0-9-gc381ef5+4,w.2019.45
LSSTDataManagementBasePackage
Likelihood.h
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23 
24 #ifndef LSST_MEAS_MODELFIT_Likelihood_h_INCLUDED
25 #define LSST_MEAS_MODELFIT_Likelihood_h_INCLUDED
26 
27 #include "ndarray_fwd.h"
28 
29 #include "lsst/pex/exceptions.h"
32 
33 namespace lsst { namespace meas { namespace modelfit {
34 
70 {
71 public:
72 
74  int getDataDim() const { return _data.getSize<0>(); }
75 
77  int getAmplitudeDim() const { return _model->getAmplitudeDim(); }
78 
80  int getNonlinearDim() const { return _model->getNonlinearDim(); }
81 
83  int getFixedDim() const { return _model->getFixedDim(); }
84 
86  ndarray::Array<Scalar const,1,1> getFixed() const { return _fixed; }
87 
89  ndarray::Array<Pixel const,1,1> getData() const { return _data; }
90 
92  ndarray::Array<Pixel const,1,1> getUnweightedData() const { return _unweightedData; }
93 
99  ndarray::Array<Pixel const,1,1> getWeights() const { return _weights; }
100 
102  ndarray::Array<Pixel const,1,1> getVariance() const { return _variance; }
103 
105  PTR(Model) getModel() const { return _model; }
106 
120  virtual void computeModelMatrix(
121  ndarray::Array<Pixel,2,-1> const & modelMatrix,
122  ndarray::Array<Scalar const,1,1> const & nonlinear,
123  bool doApplyWeights=true
124  ) const = 0;
125 
126  virtual ~Likelihood() {}
127 
128  // No copying
129  Likelihood ( const Likelihood & ) = delete;
130  Likelihood & operator= ( const Likelihood & ) = delete;
131 
132  // No moving
133  Likelihood ( Likelihood && ) = delete;
134  Likelihood & operator= ( Likelihood && ) = delete;
135 
136 protected:
137 
138  Likelihood(PTR(Model) model, ndarray::Array<Scalar const,1,1> const & fixed) :
139  _model(model), _fixed(fixed) {
141  fixed.getSize<0>(), static_cast<std::size_t>(model->getFixedDim()),
143  "Fixed parameter vector size (%d) does not match Model fixed parameter dimensionality (%d)"
144  );
145  }
146 
148  ndarray::Array<Scalar const,1,1> _fixed;
149  ndarray::Array<Pixel,1,1> _data;
150  ndarray::Array<Pixel,1,1> _unweightedData;
151  ndarray::Array<Pixel,1,1> _variance;
152  ndarray::Array<Pixel,1,1> _weights;
153 };
154 
155 }}} // namespace lsst::meas::modelfit
156 
157 #endif // !LSST_MEAS_MODELFIT_Likelihood_h_INCLUDED
virtual void computeModelMatrix(ndarray::Array< Pixel, 2,-1 > const &modelMatrix, ndarray::Array< Scalar const, 1, 1 > const &nonlinear, bool doApplyWeights=true) const =0
Evaluate the model for the given vector of nonlinear parameters.
ndarray::Array< Pixel const, 1, 1 > getWeights() const
Return the vector of weights applied to data points and model matrix rows.
Definition: Likelihood.h:99
ndarray::Array< Pixel, 1, 1 > _unweightedData
Definition: Likelihood.h:150
ndarray::Array< Pixel const, 1, 1 > getData() const
Return the vector of weighted, scaled data points .
Definition: Likelihood.h:89
Likelihood & operator=(const Likelihood &)=delete
#define LSST_THROW_IF_NE(N1, N2, EXC_CLASS, MSG)
Check whether the given values are equal, and throw an LSST Exception if they are not...
Definition: asserts.h:38
int getFixedDim() const
Return the number of fixed nonlinear parameters (set on Likelihood construction)
Definition: Likelihood.h:83
Reports attempts to exceed implementation-defined length limits for some classes. ...
Definition: Runtime.h:76
ndarray::Array< Pixel const, 1, 1 > getUnweightedData() const
Return the vector of unweighted data points .
Definition: Likelihood.h:92
Abstract base class and concrete factories that define multi-shapelet galaxy models.
Definition: Model.h:56
Likelihood(const Likelihood &)=delete
ndarray::Array< Scalar const, 1, 1 > getFixed() const
Return the vector of fixed nonlinear parameters.
Definition: Likelihood.h:86
A base class for image defects.
Likelihood(boost::shared_ptr< Model > model, ndarray::Array< Scalar const, 1, 1 > const &fixed)
Definition: Likelihood.h:138
ndarray::Array< Pixel, 1, 1 > _data
Definition: Likelihood.h:149
ndarray::Array< Pixel, 1, 1 > _weights
Definition: Likelihood.h:152
int getFixedDim() const
Return the number of fixed nonlinear parameters.
Definition: Model.h:130
Base class for optimizer/sampler likelihood functions that compute likelihood at a point...
Definition: Likelihood.h:69
ndarray::Array< Scalar const, 1, 1 > _fixed
Definition: Likelihood.h:148
int getNonlinearDim() const
Return the number of nonlinear parameters (which parameterize the model matrix)
Definition: Likelihood.h:80
boost::shared_ptr< Model > getModel() const
Return an object that defines the model and its parameters.
Definition: Likelihood.h:105
ndarray::Array< Pixel, 1, 1 > _variance
Definition: Likelihood.h:151
int getAmplitudeDim() const
Return the number of linear parameters (columns of the model matrix)
Definition: Likelihood.h:77
ndarray::Array< Pixel const, 1, 1 > getVariance() const
Return the vector of per-data-point variances.
Definition: Likelihood.h:102
boost::shared_ptr< Model > _model
Definition: Likelihood.h:147
int getDataDim() const
Return the number of data points.
Definition: Likelihood.h:74
#define PTR(...)
Definition: base.h:41