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    SG++-Doxygen-Documentation
    
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keep applying marginalize to function until it's reduced to only 1 dimension More...
#include <OperationRosenblattTransformationPolyBoundary.hpp>
  
 Public Member Functions | |
| void | doTransformation (base::DataVector *alpha, base::DataMatrix *points, base::DataMatrix *pointscdf) | 
| Transformation with mixed starting dimensions.  More... | |
| void | doTransformation (base::DataVector *alpha, base::DataMatrix *points, base::DataMatrix *pointscdf, size_t dim_start) | 
| Transformation with specified starting dimension.  More... | |
| OperationRosenblattTransformationPolyBoundary (base::Grid *grid) | |
| virtual | ~OperationRosenblattTransformationPolyBoundary () | 
  Public Member Functions inherited from sgpp::datadriven::OperationRosenblattTransformation | |
| OperationRosenblattTransformation () | |
| virtual | ~OperationRosenblattTransformation () | 
Protected Member Functions | |
| virtual double | doTransformation1D (base::Grid *grid1d, base::DataVector *alpha1d, double coord1d) | 
| void | doTransformation_in_next_dim (base::Grid *g_in, base::DataVector *a_in, size_t dim_x, base::DataVector *coords1d, base::DataVector *cdfs1d, size_t &curr_dim) | 
| void | doTransformation_start_dimX (base::Grid *g_in, base::DataVector *a_in, size_t dim_start, base::DataVector *coords1d, base::DataVector *cdfs1d) | 
Protected Attributes | |
| base::Grid * | grid | 
keep applying marginalize to function until it's reduced to only 1 dimension
      
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References alpha, doTransformation(), and python.leja::points.
      
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Transformation with mixed starting dimensions.
| alpha | Coefficient vector for current grid | 
| points | Input Matrix | 
| pointscdf | Output Matrix | 
Implements sgpp::datadriven::OperationRosenblattTransformation.
References sgpp::op_factory::createOperationDensityMargTo1D(), doTransformation1D(), doTransformation_start_dimX(), sgpp::base::DataMatrix::get(), sgpp::base::Grid::getDimension(), sgpp::base::DataMatrix::getNrows(), sgpp::base::DataMatrix::getRow(), grid, python.statsfileInfo::i, sgpp::base::DataMatrix::set(), and sgpp::base::DataMatrix::setRow().
Referenced by ~OperationRosenblattTransformationPolyBoundary().
      
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  virtual | 
Transformation with specified starting dimension.
| alpha | Coefficient vector for current grid | 
| points | Input Matrix | 
| pointscdf | Output Matrix | 
| dim_start | Starting dimension | 
Implements sgpp::datadriven::OperationRosenblattTransformation.
References sgpp::op_factory::createOperationDensityMargTo1D(), doTransformation1D(), doTransformation_start_dimX(), sgpp::base::DataMatrix::get(), sgpp::base::DataMatrix::getNcols(), sgpp::base::DataMatrix::getNrows(), sgpp::base::DataMatrix::getRow(), grid, python.statsfileInfo::i, sgpp::base::DataMatrix::set(), and sgpp::base::DataMatrix::setRow().
      
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References sgpp::op_factory::createOperationDensityConditional(), sgpp::op_factory::createOperationDensityMargTo1D(), sgpp::datadriven::OperationDensityConditional::doConditional(), doTransformation1D(), sgpp::base::DataVector::get(), sgpp::base::Grid::getDimension(), sgpp::base::DataVector::getSize(), sgpp::datadriven::OperationDensityMargTo1D::margToDimX(), and sgpp::base::DataVector::set().
Referenced by doTransformation_start_dimX().
      
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  protected | 
References doTransformation_in_next_dim(), and sgpp::base::DataVector::getSize().
Referenced by doTransformation().
      
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Referenced by python.uq.learner.Interpolant.Interpolant::doLearningIteration(), doTransformation(), python.learner.Classifier.Classifier::evalError(), python.uq.learner.Interpolant.Interpolant::evalError(), python.uq.learner.SimulationLearner.SimulationLearner::getCollocationNodes(), python.uq.learner.SimulationLearner.SimulationLearner::getGrid(), python.uq.learner.SimulationLearner.SimulationLearner::getLearner(), python.uq.learner.Regressor.Regressor::learnData(), python.uq.learner.Regressor.Regressor::learnDataWithFolding(), python.uq.learner.Regressor.Regressor::learnDataWithTest(), python.learner.Classifier.Classifier::refineGrid(), python.learner.Regressor.Regressor::refineGrid(), python.uq.learner.Regressor.Regressor::refineGrid(), python.uq.learner.SimulationLearner.SimulationLearner::refineGrid(), python.learner.Classifier.Classifier::updateResults(), python.learner.Regressor.Regressor::updateResults(), and python.uq.learner.Regressor.Regressor::updateResults().