LSSTApplications  17.0+11,17.0+34,17.0+56,17.0+57,17.0+59,17.0+7,17.0-1-g377950a+33,17.0.1-1-g114240f+2,17.0.1-1-g4d4fbc4+28,17.0.1-1-g55520dc+49,17.0.1-1-g5f4ed7e+52,17.0.1-1-g6dd7d69+17,17.0.1-1-g8de6c91+11,17.0.1-1-gb9095d2+7,17.0.1-1-ge9fec5e+5,17.0.1-1-gf4e0155+55,17.0.1-1-gfc65f5f+50,17.0.1-1-gfc6fb1f+20,17.0.1-10-g87f9f3f+1,17.0.1-11-ge9de802+16,17.0.1-16-ga14f7d5c+4,17.0.1-17-gc79d625+1,17.0.1-17-gdae4c4a+8,17.0.1-2-g26618f5+29,17.0.1-2-g54f2ebc+9,17.0.1-2-gf403422+1,17.0.1-20-g2ca2f74+6,17.0.1-23-gf3eadeb7+1,17.0.1-3-g7e86b59+39,17.0.1-3-gb5ca14a,17.0.1-3-gd08d533+40,17.0.1-30-g596af8797,17.0.1-4-g59d126d+4,17.0.1-4-gc69c472+5,17.0.1-6-g5afd9b9+4,17.0.1-7-g35889ee+1,17.0.1-7-gc7c8782+18,17.0.1-9-gc4bbfb2+3,w.2019.22
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
#define PTR(...)
Definition: base.h:41
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