Class which, starting from an experimental variogram, enables fitting the various parameters of a Covariance part of a Model. More...
#include <ModelOptimVMap.hpp>
Class which, starting from an experimental variogram, enables fitting the various parameters of a Covariance part of a Model.
Public Member Functions | |
ModelOptimVMap (ModelGeneric *model, const Constraints *constraints=nullptr, const ModelOptimParam &mop=ModelOptimParam()) | |
ModelOptimVMap (const ModelOptimVMap &m) | |
ModelOptimVMap & | operator= (const ModelOptimVMap &m) |
virtual | ~ModelOptimVMap () |
double | computeCost (bool verbose=false) override |
void | evalGrad (vect res) override |
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AModelOptim (ModelGeneric *model=nullptr, bool verbose=false) | |
void | setEnvironment (const MatrixSymmetric &vars, double href) |
AModelOptim & | operator= (const AModelOptim &r) |
void | setAuthorizedAnalyticalGradients (bool authorized) |
bool | getAuthorizedAnalyticalGradients () const |
virtual | ~AModelOptim () |
void | setGradients (std::vector< std::function< double(const std::vector< double > &)> > &gradients) |
void | setVerbose (bool verbose=false, bool trace=false) |
double | eval (const std::vector< double > &x) |
void | run () |
void | resetIter () |
Static Public Member Functions | |
static ModelOptimVMap * | createForOptim (ModelGeneric *model, const DbGrid *dbmap, const Constraints *constraints=nullptr, const ModelOptimParam &mop=ModelOptimParam()) |
gstlrn::ModelOptimVMap::ModelOptimVMap | ( | ModelGeneric * | model, |
const Constraints * | constraints = nullptr , |
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const ModelOptimParam & | mop = ModelOptimParam() |
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gstlrn::ModelOptimVMap::ModelOptimVMap | ( | const ModelOptimVMap & | m | ) |
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virtual |
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overridevirtual |
Implements gstlrn::AModelOptim.
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static |
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overridevirtual |
Reimplemented from gstlrn::AModelOptim.
ModelOptimVMap & gstlrn::ModelOptimVMap::operator= | ( | const ModelOptimVMap & | m | ) |