sgpp::datadriven::ClassificationLearner Class Reference

The ClassificationLearner class Solves a classification problem. More...

#include <ClassificationLearner.hpp>

Public Member Functions
	ClassificationLearner (sgpp::base::RegularGridConfiguration gridConfig, sgpp::base::AdaptivityConfiguration adaptivityConfig, sgpp::solver::SLESolverConfiguration solverConfig, sgpp::solver::SLESolverConfiguration finalSolverConfig, sgpp::datadriven::RegularizationConfiguration regularizationConfig, std::vector< std::vector< size_t >> terms)
	ClassificationLearner. More...
	ClassificationLearner (sgpp::base::RegularGridConfiguration gridConfig, sgpp::base::AdaptivityConfiguration adaptivityConfig, sgpp::solver::SLESolverConfiguration solverConfig, sgpp::solver::SLESolverConfiguration finalSolverConfig, sgpp::datadriven::RegularizationConfiguration regularizationConfig)
	ClassificationLearner. More...
double	getAccuracy (sgpp::base::DataMatrix &data, const sgpp::base::DataVector &y)
	getAccuracy More...
size_t	getGridSize () const
	getGridSize More...
sgpp::base::DataVector	predict (sgpp::base::DataMatrix &data)
	predict More...
std::pair< sgpp::base::DataVector, sgpp::base::DataVector >	predictWithCertainty (sgpp::base::DataMatrix &data)
	predict More...
void	train (sgpp::base::DataMatrix &trainDataset, sgpp::base::DataVector &classes)
	train fits a sparse grid regression model. More...

Detailed Description

The ClassificationLearner class Solves a classification problem.

Constructor & Destructor Documentation

ClassificationLearner() [1/2]

sgpp::datadriven::ClassificationLearner::ClassificationLearner	(	sgpp::base::RegularGridConfiguration	gridConfig,
		sgpp::base::AdaptivityConfiguration	adaptivityConfig,
		sgpp::solver::SLESolverConfiguration	solverConfig,
		sgpp::solver::SLESolverConfiguration	finalSolverConfig,
		sgpp::datadriven::RegularizationConfiguration	regularizationConfig,
		std::vector< std::vector< size_t >>	terms
	)

ClassificationLearner.

Parameters

gridConfig
adaptivityConfig
solverConfig	is the solver used during each adaptivity step
finalSolverConfig	is the solver used to build the final model
regularizationConfig
terms	is a vector that contains all desired interaction terms. For example, if we want to include grid points that model an interaction between the first and the second predictor, we would include the vector [1,2] in terms.

ClassificationLearner() [2/2]

sgpp::datadriven::ClassificationLearner::ClassificationLearner	(	sgpp::base::RegularGridConfiguration	gridConfig,
		sgpp::base::AdaptivityConfiguration	adaptivityConfig,
		sgpp::solver::SLESolverConfiguration	solverConfig,
		sgpp::solver::SLESolverConfiguration	finalSolverConfig,
		sgpp::datadriven::RegularizationConfiguration	regularizationConfig
	)

ClassificationLearner.

Parameters

gridConfig
adaptivityConfig
solverConfig	is the solver used during each adaptivity step
finalSolverConfig	is the solver used to build the final model
regularizationConfig

Member Function Documentation

getAccuracy()

double sgpp::datadriven::ClassificationLearner::getAccuracy	(	sgpp::base::DataMatrix &	data,
		const sgpp::base::DataVector &	y
	)

getAccuracy

Parameters

data	is the design matrix
y	is the target

Returns

the accuracy of the prediction of the model for the matrix data

References sgpp::base::DataVector::getSize(), python.statsfileInfo::i, and predict().

getGridSize()

size_t sgpp::datadriven::ClassificationLearner::getGridSize

(

)

const

getGridSize

Returns

the grid size

predict()

sgpp::base::DataVector sgpp::datadriven::ClassificationLearner::predict

(

sgpp::base::DataMatrix &

data

)

predict

Parameters

data	are observations

Returns

the predicted target for matrix data

References sgpp::base::DataMatrix::getNrows(), and python.statsfileInfo::i.

Referenced by getAccuracy().

predictWithCertainty()

std::pair< sgpp::base::DataVector, sgpp::base::DataVector > sgpp::datadriven::ClassificationLearner::predictWithCertainty

(

sgpp::base::DataMatrix &

data

)

predict

Parameters

data	are observations

Returns

the predicted target for matrix data and the certainty of the model, that the observation belongs to the predicted class.

References python.statsfileInfo::data, sgpp::base::DataMatrix::getNrows(), and python.statsfileInfo::i.

train()

void sgpp::datadriven::ClassificationLearner::train	(	sgpp::base::DataMatrix &	trainDataset,
		sgpp::base::DataVector &	classes
	)

train fits a sparse grid regression model.

Parameters

trainDataset	is the design matrix
classes	is the (continuous) target

References sgpp::base::DataVector::getSize(), and python.statsfileInfo::i.

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

• datadriven/src/sgpp/datadriven/application/ClassificationLearner.hpp
• datadriven/src/sgpp/datadriven/application/ClassificationLearner.cpp