LSST Applications  21.0.0-147-g0e635eb1+1acddb5be5,22.0.0+052faf71bd,22.0.0+1ea9a8b2b2,22.0.0+6312710a6c,22.0.0+729191ecac,22.0.0+7589c3a021,22.0.0+9f079a9461,22.0.1-1-g7d6de66+b8044ec9de,22.0.1-1-g87000a6+536b1ee016,22.0.1-1-g8e32f31+6312710a6c,22.0.1-10-gd060f87+016f7cdc03,22.0.1-12-g9c3108e+df145f6f68,22.0.1-16-g314fa6d+c825727ab8,22.0.1-19-g93a5c75+d23f2fb6d8,22.0.1-19-gb93eaa13+aab3ef7709,22.0.1-2-g8ef0a89+b8044ec9de,22.0.1-2-g92698f7+9f079a9461,22.0.1-2-ga9b0f51+052faf71bd,22.0.1-2-gac51dbf+052faf71bd,22.0.1-2-gb66926d+6312710a6c,22.0.1-2-gcb770ba+09e3807989,22.0.1-20-g32debb5+b8044ec9de,22.0.1-23-gc2439a9a+fb0756638e,22.0.1-3-g496fd5d+09117f784f,22.0.1-3-g59f966b+1e6ba2c031,22.0.1-3-g849a1b8+f8b568069f,22.0.1-3-gaaec9c0+c5c846a8b1,22.0.1-32-g5ddfab5d3+60ce4897b0,22.0.1-4-g037fbe1+64e601228d,22.0.1-4-g8623105+b8044ec9de,22.0.1-5-g096abc9+d18c45d440,22.0.1-5-g15c806e+57f5c03693,22.0.1-7-gba73697+57f5c03693,master-g6e05de7fdc+c1283a92b8,master-g72cdda8301+729191ecac,w.2021.39
LSST Data Management Base Package
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 
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(std::shared_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
#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
Base class for optimizer/sampler likelihood functions that compute likelihood at a point.
Definition: Likelihood.h:70
ndarray::Array< Pixel, 1, 1 > _variance
Definition: Likelihood.h:151
ndarray::Array< Pixel const, 1, 1 > getVariance() const
Return the vector of per-data-point variances.
Definition: Likelihood.h:102
ndarray::Array< Pixel const, 1, 1 > getData() const
Return the vector of weighted, scaled data points .
Definition: Likelihood.h:89
Likelihood(const Likelihood &)=delete
Likelihood & operator=(const Likelihood &)=delete
ndarray::Array< Scalar const, 1, 1 > getFixed() const
Return the vector of fixed nonlinear parameters.
Definition: Likelihood.h:86
std::shared_ptr< Model > _model
Definition: Likelihood.h:147
int getDataDim() const
Return the number of data points.
Definition: Likelihood.h:74
ndarray::Array< Pixel, 1, 1 > _data
Definition: Likelihood.h:149
Likelihood(Likelihood &&)=delete
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< Scalar const, 1, 1 > _fixed
Definition: Likelihood.h:148
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 > getUnweightedData() const
Return the vector of unweighted data points .
Definition: Likelihood.h:92
int getNonlinearDim() const
Return the number of nonlinear parameters (which parameterize the model matrix)
Definition: Likelihood.h:80
ndarray::Array< Pixel, 1, 1 > _unweightedData
Definition: Likelihood.h:150
std::shared_ptr< Model > getModel() const
Return an object that defines the model and its parameters.
Definition: Likelihood.h:105
ndarray::Array< Pixel, 1, 1 > _weights
Definition: Likelihood.h:152
int getAmplitudeDim() const
Return the number of linear parameters (columns of the model matrix)
Definition: Likelihood.h:77
int getFixedDim() const
Return the number of fixed nonlinear parameters (set on Likelihood construction)
Definition: Likelihood.h:83
Likelihood(std::shared_ptr< Model > model, ndarray::Array< Scalar const, 1, 1 > const &fixed)
Definition: Likelihood.h:138
Reports attempts to exceed implementation-defined length limits for some classes.
Definition: Runtime.h:76
A base class for image defects.