SG++
sgpp::datadriven::PiecewiseConstantSmoothedRegressionMetaLearner Class Reference

#include <PiecewiseConstantSmoothedMetaLearner.hpp>

Public Member Functions

double calculateMSE (base::Grid &grid, base::DataVector &alpha, base::DataMatrix &testSubset, base::DataVector &valuesTestSubset, bool verbose=false)
 
double optimizeLambdaLog (size_t kFold, size_t maxLevel, double fastApproximationMSE, size_t fastApproximationMaxLevel)
 
void optimizeLambdaLog (size_t kFold, size_t maxLevel, double fastApproximationMSE, size_t fastApproximationMaxLevel, std::shared_ptr< base::Grid > &bestGrid, std::shared_ptr< base::DataVector > &bestAlpha, double &lambdaOpt)
 
 PiecewiseConstantSmoothedRegressionMetaLearner (bool verbose, base::DataMatrix &trainingDataSet, base::DataVector &valuesDataSet, base::RegularGridConfiguration gridConfig, base::AdpativityConfiguration adaptConfig, solver::SLESolverConfiguration solverConfig, datadriven::RegularizationConfiguration regularizationConfig)
 
void train (base::DataMatrix &train, base::DataVector &trainValues, double lambda, double fastApproximationMSE, size_t fastApproximationMaxLevel, std::shared_ptr< base::Grid > &grid, std::shared_ptr< base::DataVector > &alpha)
 Does the learning step on a given grid, training set and regularization parameter lambda. More...
 

Constructor & Destructor Documentation

sgpp::datadriven::PiecewiseConstantSmoothedRegressionMetaLearner::PiecewiseConstantSmoothedRegressionMetaLearner ( bool  verbose,
base::DataMatrix trainingDataSet,
base::DataVector valuesDataSet,
base::RegularGridConfiguration  gridConfig,
base::AdpativityConfiguration  adaptConfig,
solver::SLESolverConfiguration  solverConfig,
datadriven::RegularizationConfiguration  regularizationConfig 
)

Member Function Documentation

double sgpp::datadriven::PiecewiseConstantSmoothedRegressionMetaLearner::calculateMSE ( base::Grid grid,
base::DataVector alpha,
base::DataMatrix testSubset,
base::DataVector valuesTestSubset,
bool  verbose = false 
)
double sgpp::datadriven::PiecewiseConstantSmoothedRegressionMetaLearner::optimizeLambdaLog ( size_t  kFold,
size_t  maxLevel,
double  fastApproximationMSE,
size_t  fastApproximationMaxLevel 
)
void sgpp::datadriven::PiecewiseConstantSmoothedRegressionMetaLearner::optimizeLambdaLog ( size_t  kFold,
size_t  maxLevel,
double  fastApproximationMSE,
size_t  fastApproximationMaxLevel,
std::shared_ptr< base::Grid > &  bestGrid,
std::shared_ptr< base::DataVector > &  bestAlpha,
double &  lambdaOpt 
)

References optimizeLambdaLog(), and train().

void sgpp::datadriven::PiecewiseConstantSmoothedRegressionMetaLearner::train ( base::DataMatrix train,
base::DataVector trainValues,
double  lambda,
double  fastApproximationMSE,
size_t  fastApproximationMaxLevel,
std::shared_ptr< base::Grid > &  grid,
std::shared_ptr< base::DataVector > &  alpha 
)

Does the learning step on a given grid, training set and regularization parameter lambda.

Parameters
trainsample set
trainValuestraining values
lambdaregularization parameter
fastApproximationMSEmse for stopping piecewise constant approximation tree creation
fastApproximationMaxLevelmaximum level for stopping piecewise constant approximation tree creation
gridgrid
alphagrid coefficients

References sgpp::base::Grid::createLinearBoundaryGrid(), sgpp::base::Grid::createLinearGrid(), sgpp::base::Grid::getGenerator(), sgpp::datadriven::PiecewiseConstantRegression::Node::getMSE(), grid(), sgpp::datadriven::OperationPiecewiseConstantRegression::hierarchize(), sgpp::base::RegularGridConfiguration::level_, sgpp::base::Linear, sgpp::base::LinearBoundary, sgpp::base::LinearL0Boundary, sgpp::base::GridGenerator::regular(), sgpp::datadriven::LearnerPiecewiseConstantSmoothedRegression::train(), and sgpp::base::RegularGridConfiguration::type_.

Referenced by optimizeLambdaLog().


The documentation for this class was generated from the following files: