27 #include <queso/GslVector.h>
28 #include <queso/GslMatrix.h>
29 #include <queso/VectorSet.h>
30 #include <queso/GaussianLikelihoodBlockDiagonalCovariance.h>
34 template<
class V,
class M>
39 m_covarianceCoefficients(covariance.numBlocks(), 1.0),
40 m_covariance(covariance)
42 unsigned int totalDim = 0;
48 if (totalDim != observations.sizeLocal()) {
49 queso_error_msg(
"Covariance matrix not same dimension as observation vector");
53 template<
class V,
class M>
58 template<
class V,
class M>
63 return this->m_covarianceCoefficients[i];
66 template<
class V,
class M>
71 return this->m_covarianceCoefficients[i];
74 template<
class V,
class M>
77 const V & domainVector,
const V * domainDirection, V * gradVector,
78 M * hessianMatrix, V * hessianEffect)
const
80 return std::exp(this->lnValue(domainVector, domainDirection, gradVector,
81 hessianMatrix, hessianEffect));
84 template<
class V,
class M>
87 const V & domainVector,
const V * domainDirection, V * gradVector,
88 M * hessianMatrix, V * hessianEffect)
const
90 V modelOutput(this->m_observations, 0, 0);
91 V weightedMisfit(this->m_observations, 0, 0);
93 this->evaluateModel(domainVector, domainDirection, modelOutput, gradVector,
94 hessianMatrix, hessianEffect);
97 modelOutput -= this->m_observations;
100 this->m_covariance.invertMultiply(modelOutput, weightedMisfit);
103 unsigned int offset = 0;
106 for (
unsigned int i = 0; i < this->m_covariance.numBlocks(); i++) {
108 unsigned int blockDim = this->m_covariance.getBlock(i).numRowsLocal();
109 for (
unsigned int j = 0; j < blockDim; j++) {
111 modelOutput[offset+j] /= this->m_covarianceCoefficients[i];
117 modelOutput *= weightedMisfit;
119 double norm2_squared = modelOutput.sumOfComponents();
121 return -0.5 * norm2_squared;
A class representing a Gaussian likelihood with block-diagonal covariance matrix. ...
unsigned int numBlocks() const
Return the number of blocks in the block diagonal matrix.
#define queso_error_msg(msg)
A templated class for handling sets.
const GslBlockMatrix & m_covariance
const double & getBlockCoefficient(unsigned int i) const
Get (const) multiplicative coefficient for block i.
unsigned int numRowsLocal() const
Returns the local row dimension of this matrix.
virtual double lnValue(const V &domainVector, const V *domainDirection, V *gradVector, M *hessianMatrix, V *hessianEffect) const
Logarithm of the value of the scalar function.
GaussianLikelihoodBlockDiagonalCovariance(const char *prefix, const VectorSet< V, M > &domainSet, const V &observations, const GslBlockMatrix &covariance)
Default constructor.
GslMatrix & getBlock(unsigned int i) const
Return block i in the block diagonal matrix.
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.
double & blockCoefficient(unsigned int i)
Get (non-const) multiplicative coefficient for block i.
Class for representing block matrices using GSL library.
virtual ~GaussianLikelihoodBlockDiagonalCovariance()
Destructor.