#include <SPDEOp.hpp>
Public Member Functions | |
| ASPDEOp (const IPrecisionOp *const popKriging=nullptr, const IProj *const projInKriging=nullptr, const ASimulable *invNoise=nullptr, const IPrecisionOp *const popSimu=nullptr, const IProj *const projInSimu=nullptr, APreconditioner *precond=nullptr) | |
| virtual | ~ASPDEOp () |
| virtual | stdev (const 1 &dat, Id nMC=1, Id seed=134343, const IProj *projK=nullptr, const IProj *projS=nullptr) const |
| Computing Standard deviation of the estimation error using MonteCarlo on conditional simulations. | |
| Id | getSize () const override |
| Id | getSizeSimu () const |
| Id | getIterations () const |
| double | getError () const |
| void | setMaxIterations (Id n) |
| void | setTolerance (double tol) |
| kriging (const 1 &dat, const IProj *proj=nullptr) const | |
| krigingWithGuess (const 1 &dat, const 1 &guess) const | |
| computeDriftCoeffs (const 1 &Z, const MatrixDense &driftMat, bool verbose=false) const | |
| simCond (const 1 &dat, const IProj *projK=nullptr, const IProj *projS=nullptr) const | |
| simCondGibbs (const 1 &dat, const 1 &gibbsUpperBound, const 1 &gibbsLowerBound, const IProj *projK=nullptr, const IProj *projS=nullptr, Id nIter=5, bool useCache=true) const | |
| Conditional simulation that allow for inequality constraints defined by 'gibbsLowerBound' and 'gibbsUpperBound'. Inequality are only defined when gibbsLowerBound[i_data] != gibbsLowerBound[i_data]. | |
| void | clearGibbsCache () const |
| Clear Gibbs cache. | |
| simNonCond (const IProj *proj=nullptr) const | |
| getRangeEigenVal (Id ndiscr=100) const | |
| const IPrecisionOp * | getQKriging () const |
| const IProj * | getProjKriging () const |
| const ASimulable * | getInvNoise () const |
| const IPrecisionOp * | getQSimu () const |
| const IProj * | getProjInSimu () const |
| Id | krigingWithGuess (const constvect inv, const constvect guess, vect out) const |
| void | evalInvCov (const constvect inv, vect result) const |
| void | simCond (const constvect data, vect outv) const |
| void | simNonCond (vect outv) const |
| virtual double | computeLogDetOp (Id nbsimu=1) const |
| double | computeQuadratic (const 1 &x) const |
| double | computeTotalLogDet (Id nMC=5, Id seed=13132) const |
| double | computeLogDetQ (Id nMC=5) const |
| double | computeLogDetInvNoise () const |
| void | setVerbose (bool v) |
| void | setSolverVerbose (bool v) |
| double | getMaxEigenValProj () const |
| evalInverse (const 1 &vecin) | |
Public Member Functions inherited from gstlrn::ALinearOp | |
| ALinearOp () | |
| ALinearOp (const ALinearOp &m)=default | |
| ALinearOp (ALinearOp &&m)=default | |
| ALinearOp & | operator= (const ALinearOp &m)=default |
| ALinearOp & | operator= (ALinearOp &&m)=default |
| virtual | ~ALinearOp ()=default |
| Id | evalDirect (const 1 &inv, 1 &outv) const |
| evalDirect (const 1 &in) const | |
| virtual void | multiplyByValueAndAddDiagonal (double v1=1., double v2=0.) const |
| virtual void | resetModif () const |
| void | setUseFactor (bool usefactor) |
| Id | evalDirect (constvect inv, vect outv) const |
| Id | addToDest (const constvect inv, vect outv) const |
| Id | addToDest (const ::Eigen::VectorXd &inv, ::Eigen::VectorXd &outv) const |
Static Public Member Functions | |
| static Id | centerDataByDriftMat (1 &Z, const MatrixDense &driftMat, const 1 &driftCoeffs) |
| static Id | centerDataByMeanVec (1 &Z, const 1 &meanVec) |
| gstlrn::ASPDEOp::ASPDEOp | ( | const IPrecisionOp *const | popKriging = nullptr, |
| const IProj *const | projInKriging = nullptr, |
||
| const ASimulable * | invNoise = nullptr, |
||
| const IPrecisionOp *const | popSimu = nullptr, |
||
| const IProj *const | projInSimu = nullptr, |
||
| APreconditioner * | precond = nullptr |
||
| ) |
|
virtual |
|
static |
|
static |
| void gstlrn::ASPDEOp::clearGibbsCache | ( | ) | const |
Clear Gibbs cache.
| gstlrn::ASPDEOp::computeDriftCoeffs | ( | const 1 & | Z, |
| const MatrixDense & | driftMat, | ||
| bool | verbose = false |
||
| ) | const |
| double gstlrn::ASPDEOp::computeLogDetInvNoise | ( | ) | const |
|
virtual |
Reimplemented in gstlrn::SPDEOpMatrix.
| double gstlrn::ASPDEOp::computeLogDetQ | ( | Id | nMC = 5 | ) | const |
| double gstlrn::ASPDEOp::computeQuadratic | ( | const 1 & | x | ) | const |
| gstlrn::ASPDEOp::evalInverse | ( | const 1 & | vecin | ) |
|
inline |
|
inline |
|
inline |
| double gstlrn::ASPDEOp::getMaxEigenValProj | ( | ) | const |
|
inline |
|
inline |
|
inline |
|
inline |
| gstlrn::ASPDEOp::getRangeEigenVal | ( | Id | ndiscr = 100 | ) | const |
|
overridevirtual |
Implements gstlrn::ALinearOp.
| Id gstlrn::ASPDEOp::getSizeSimu | ( | ) | const |
| gstlrn::ASPDEOp::kriging | ( | const 1 & | dat, |
| const IProj * | proj = nullptr |
||
| ) | const |
| gstlrn::ASPDEOp::krigingWithGuess | ( | const 1 & | dat, |
| const 1 & | guess | ||
| ) | const |
|
inline |
|
inline |
|
inline |
|
inline |
| gstlrn::ASPDEOp::simCond | ( | const 1 & | dat, |
| const IProj * | projK = nullptr, |
||
| const IProj * | projS = nullptr |
||
| ) | const |
| gstlrn::ASPDEOp::simCondGibbs | ( | const 1 & | dat, |
| const 1 & | gibbsLowerBound, | ||
| const 1 & | gibbsUpperBound, | ||
| const IProj * | projK = nullptr, |
||
| const IProj * | projS = nullptr, |
||
| Id | nIter = 5, |
||
| bool | useCache = true |
||
| ) | const |
Conditional simulation that allow for inequality constraints defined by 'gibbsLowerBound' and 'gibbsUpperBound'. Inequality are only defined when gibbsLowerBound[i_data] != gibbsLowerBound[i_data].
| dat | Vector of Data |
| gibbsLowerBound | Vector of lower bound for inequalities |
| gibbsUpperBound | Vector of upper bound for inequalities |
| projK | Projection Matrix used for Kriging |
| projS | Projection matrix used for Simulations |
| nIter | Number of Monte-Carlo simulations |
| useCache | a boolean TRUE means use the previous conditional simulations as initial Data |
| gstlrn::ASPDEOp::simNonCond | ( | const IProj * | proj = nullptr | ) | const |
| void gstlrn::ASPDEOp::simNonCond | ( | vect | outv | ) | const |
|
virtual |
Computing Standard deviation of the estimation error using MonteCarlo on conditional simulations.
| dat | Vector of Data |
| nMC | Number of Monte-Carlo simulations |
| seed | Random seed for the Monte-Carlo simulations |
| projK | Projection Matrix used for Kriging |
| projS | Projection matrix used for Simulations |
Reimplemented in gstlrn::SPDEOpMatrix.