GenericJointPdf<V,M>::GenericJointPdf(
const char* prefix,
const BaseScalarFunction<V,M>& scalarFunction)
:
BaseJointPdf<V,M>(((std::string)(prefix)+"gen").c_str(),scalarFunction.domainSet()),
m_scalarFunction(scalarFunction)
{
}
ScalarFunctionSynchronizer<V,M>::ScalarFunctionSynchronizer(
const BaseScalarFunction<V,M>& inputFunction,
const V& auxVec)
: m_env(inputFunction.domainSet().env()),
m_scalarFunction(inputFunction),
m_bayesianJointPdfPtr(dynamic_cast<const BayesianJointPdf<V,M>* >(&m_scalarFunction)),
m_auxVec(auxVec)
{
}
GslOptimizer::GslOptimizer(
const BaseScalarFunction<GslVector, GslMatrix> & objectiveFunction)
: BaseOptimizer(),
m_objectiveFunction(objectiveFunction),
m_initialPoint(new GslVector(objectiveFunction.domainSet().
vectorSpace().zeroVector())),
m_minimizer(new GslVector(this->m_objectiveFunction.domainSet().
vectorSpace().zeroVector())),
m_solver_type(BFGS2),
m_fstep_size(this->m_objectiveFunction.domainSet().vectorSpace().zeroVector()),
m_fdfstep_size(getFdfstepSize()),
m_line_tol(getLineTolerance())
{
// We initialize the minimizer to GSL_NAN just in case the optimization fails
m_minimizer->cwSet(GSL_NAN);
// Set to documented default value.
m_fstep_size.cwSet(getFstepSize());
// Set solver type to the one set in the options object
setSolverType(getSolverType());
}
StatisticalInverseProblem<P_V,P_M>::StatisticalInverseProblem(
/*! The prefix */ const char* prefix,
/*! Options (if no input file) */ const SipOptionsValues* alternativeOptionsValues, // dakota
/*! The prior RV */ const BaseVectorRV <P_V,P_M>& priorRv,
/*! The likelihood function */ const BaseScalarFunction<P_V,P_M>& likelihoodFunction,
/*! The posterior RV */ GenericVectorRV <P_V,P_M>& postRv)
:
m_env (priorRv.env()),
m_priorRv (priorRv),
m_likelihoodFunction (likelihoodFunction),
m_postRv (postRv),
m_solutionDomain (NULL),
m_solutionPdf (NULL),
m_subSolutionMdf (NULL),
m_subSolutionCdf (NULL),
m_solutionRealizer (NULL),
m_mhSeqGenerator (NULL),
m_mlSampler (NULL),
m_chain (NULL),
m_logLikelihoodValues (NULL),
m_logTargetValues (NULL),
m_optionsObj (alternativeOptionsValues),
m_seedWithMAPEstimator (false),
m_userDidNotProvideOptions(false)
{
#ifdef QUESO_MEMORY_DEBUGGING
std::cout << "Entering Sip" << std::endl;
#endif
if (m_env.subDisplayFile()) {
*m_env.subDisplayFile() << "Entering StatisticalInverseProblem<P_V,P_M>::constructor()"
<< ": prefix = " << prefix
<< ", alternativeOptionsValues = " << alternativeOptionsValues
<< ", m_env.optionsInputFileName() = " << m_env.optionsInputFileName()
<< std::endl;
}
// If NULL, we create one
if (m_optionsObj == NULL) {
SipOptionsValues * tempOptions = new SipOptionsValues(&m_env, prefix);
// We did this dance because scanOptionsValues is not a const method, but
// m_optionsObj is a pointer to const
m_optionsObj = tempOptions;
// We set this flag so we don't delete the user-created object when it
// comes time to deconstruct
m_userDidNotProvideOptions = true;
}
if (m_optionsObj->m_help != "") {
if (m_env.subDisplayFile()) {
*m_env.subDisplayFile() << (*m_optionsObj) << std::endl;
}
}
#ifdef QUESO_MEMORY_DEBUGGING
std::cout << "In Sip, finished scanning options" << std::endl;
#endif
queso_require_equal_to_msg(priorRv.imageSet().vectorSpace().dimLocal(), likelihoodFunction.domainSet().vectorSpace().dimLocal(), "'priorRv' and 'likelihoodFunction' are related to vector spaces of different dimensions");
queso_require_equal_to_msg(priorRv.imageSet().vectorSpace().dimLocal(), postRv.imageSet().vectorSpace().dimLocal(), "'priorRv' and 'postRv' are related to vector spaces of different dimensions");
if (m_env.subDisplayFile()) {
*m_env.subDisplayFile() << "Leaving StatisticalInverseProblem<P_V,P_M>::constructor()"
<< ": prefix = " << m_optionsObj->m_prefix
<< std::endl;
}
return;
}
