queso-0.53.0
GaussianLikelihoodFullCovariance.C
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1 //-----------------------------------------------------------------------bl-
2 //--------------------------------------------------------------------------
3 //
4 // QUESO - a library to support the Quantification of Uncertainty
5 // for Estimation, Simulation and Optimization
6 //
7 // Copyright (C) 2008-2015 The PECOS Development Team
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23 //-----------------------------------------------------------------------el-
24 
25 #include <cmath>
26 
27 #include <queso/GslVector.h>
28 #include <queso/GslMatrix.h>
29 #include <queso/VectorSet.h>
30 #include <queso/GaussianLikelihoodFullCovariance.h>
31 
32 namespace QUESO {
33 
34 template<class V, class M>
36  const char * prefix, const VectorSet<V, M> & domainSet,
37  const V & observations, const M & covariance, double covarianceCoefficient)
38  : BaseGaussianLikelihood<V, M>(prefix, domainSet, observations),
39  m_covarianceCoefficient(covarianceCoefficient),
40  m_covariance(covariance)
41 {
42  if (covariance.numRowsLocal() != observations.sizeLocal()) {
43  queso_error_msg("Covariance matrix not same size as observation vector");
44  }
45 }
46 
47 template<class V, class M>
49 {
50 }
51 
52 template<class V, class M>
53 double
55  const V * domainDirection, V * gradVector, M * hessianMatrix,
56  V * hessianEffect) const
57 {
58  return std::exp(this->lnValue(domainVector, domainDirection, gradVector,
59  hessianMatrix, hessianEffect));
60 }
61 
62 template<class V, class M>
63 double
65  const V * domainDirection, V * gradVector, M * hessianMatrix,
66  V * hessianEffect) const
67 {
68  V modelOutput(this->m_observations, 0, 0); // At least it's not a copy
69  V weightedMisfit(this->m_observations, 0, 0); // At least it's not a copy
70 
71  this->evaluateModel(domainVector, domainDirection, modelOutput, gradVector,
72  hessianMatrix, hessianEffect);
73 
74  // Compute misfit G(x) - y
75  modelOutput -= this->m_observations;
76 
77  // Solve \Sigma u = G(x) - y for u
78  this->m_covariance.invertMultiply(modelOutput, weightedMisfit);
79 
80  // Compute (G(x) - y)^T \Sigma^{-1} (G(x) - y)
81  modelOutput *= weightedMisfit;
82 
83  // This is square of 2-norm
84  double norm2_squared = modelOutput.sumOfComponents();
85 
86  return -0.5 * norm2_squared / (this->m_covarianceCoefficient);
87 }
88 
89 } // End namespace QUESO
90 
A templated class for handling sets.
Definition: VectorSet.h:52
#define queso_error_msg(msg)
Definition: asserts.h:47
GaussianLikelihoodFullCovariance(const char *prefix, const VectorSet< V, M > &domainSet, const V &observations, const M &covariance, double covarianceCoefficient=1.0)
Default constructor.
virtual double actualValue(const V &domainVector, const V *domainDirection, V *gradVector, M *hessianMatrix, V *hessianEffect) const
Actual value of the scalar function.
Base class for canned Gaussian likelihoods.
A class that represents a Gaussian likelihood with full covariance.
virtual double lnValue(const V &domainVector, const V *domainDirection, V *gradVector, M *hessianMatrix, V *hessianEffect) const
Logarithm of the value of the scalar function.

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