Fitter object that encapsulates density based classification using instances of ModelFittingDensityEstimation for each class. More...

#include <ModelFittingClassification.hpp>

Inheritance diagram for sgpp::datadriven::ModelFittingClassification:

Public Member Functions
double	evaluate (const DataVector &sample) override
	Predict the class of a data sample based on the density of the sample for each model. More...

void	evaluate (DataMatrix &samples, DataVector &results) override
	Predicts the class for a set of data points based on the learned densities for each class. More...

void	fit (Dataset &dataset) override
	Fits the models for all classes based on the data given in the dataset parameter. More...

	ModelFittingClassification (const FitterConfigurationClassification &config)
	Constructor. More...

bool	refine () override
	Improve the accuracy of the classification by refining the grids of each class. More...

void	reset () override
	Resets the state of the entire model. More...

void	update (Dataset &dataset) override
	Updates the models for each class based on new data (streaming or batch learning) More...

Public Member Functions inherited from sgpp::datadriven::ModelFittingBase
const FitterConfiguration &	getFitterConfiguration () const
	Get the configuration of the fitter object. More...

	ModelFittingBase ()
	Default constructor. More...

	ModelFittingBase (const ModelFittingBase &rhs)=delete
	Copy constructor - we cannot deep copy all member variables yet. More...

	ModelFittingBase (ModelFittingBase &&rhs)=default
	Move constructor. More...

ModelFittingBase &	operator= (const ModelFittingBase &rhs)=delete
	Copy assign operator - we cannot deep copy all member variables yet. More...

ModelFittingBase &	operator= (ModelFittingBase &&rhs)=default
	Move assign operator. More...

virtual	~ModelFittingBase ()=default
	virtual destructor. More...

Additional Inherited Members
Public Attributes inherited from sgpp::datadriven::ModelFittingBase
bool	verboseSolver
	Whether the Solver produces output or not. More...

Protected Member Functions inherited from sgpp::datadriven::ModelFittingBase
Grid *	buildGrid (const RegularGridConfiguration &gridConfig) const
	Factory member function that generates a grid from configuration. More...

SLESolver *	buildSolver (const SLESolverConfiguration &config) const
	Factory member function to build the solver for the least squares regression problem according to the config. More...

void	reconfigureSolver (SLESolver &solver, const SLESolverConfiguration &config) const
	Configure solver based on the desired configuration. More...

Protected Attributes inherited from sgpp::datadriven::ModelFittingBase
std::unique_ptr< FitterConfiguration >	config
	Configuration object for the fitter. More...

Dataset *	dataset
	Pointer to sgpp::datadriven::Dataset. More...

std::unique_ptr< SLESolver >	solver
	Solver for the learning problem. More...

Detailed Description

Fitter object that encapsulates density based classification using instances of ModelFittingDensityEstimation for each class.

Constructor & Destructor Documentation

◆ ModelFittingClassification()

sgpp::datadriven::ModelFittingClassification::ModelFittingClassification ( const FitterConfigurationClassification & config )

explicit

Constructor.

Parameters

config configuration object that specifies grid, refinement, and regularization

References sgpp::datadriven::ModelFittingBase::config.

Member Function Documentation

◆ evaluate() [1/2]

double sgpp::datadriven::ModelFittingClassification::evaluate ( const DataVector & sample )

overridevirtual

Predict the class of a data sample based on the density of the sample for each model.

Parameters

sample the sample point to classify

Returns: the predicted class label

Implements sgpp::datadriven::ModelFittingBase.

References sgpp::datadriven::ModelFittingBase::config, and friedman::p.

Referenced by evaluate().

◆ evaluate() [2/2]

void sgpp::datadriven::ModelFittingClassification::evaluate	(	DataMatrix &	samples,
		DataVector &	results
	)

overridevirtual

Predicts the class for a set of data points based on the learned densities for each class.

Parameters

samples	matrix where each row represents a data sample
results	vector to output the predicted classes

Implements sgpp::datadriven::ModelFittingBase.

References evaluate(), sgpp::base::DataMatrix::getNcols(), sgpp::base::DataMatrix::getNrows(), sgpp::base::DataMatrix::getRow(), python.statsfileInfo::i, sgpp::base::DataVector::set(), and analyse_erg::tmp.

◆ fit()

void sgpp::datadriven::ModelFittingClassification::fit ( Dataset & dataset )

overridevirtual

Fits the models for all classes based on the data given in the dataset parameter.

Parameters

dataset the training dataset that is used to fit the models

Implements sgpp::datadriven::ModelFittingBase.

References sgpp::datadriven::CG, sgpp::datadriven::ModelFittingBase::config, sgpp::datadriven::ModelFittingBase::dataset, sgpp::datadriven::Decomposition, create_dataset::default, sgpp::datadriven::Dataset::getData(), sgpp::datadriven::FitterConfiguration::getDensityEstimationConfig(), sgpp::datadriven::Dataset::getTargets(), sgpp::base::AdaptivityConfiguration::levelPenalize, sgpp::base::AdaptivityConfiguration::noPoints_, sgpp::base::AdaptivityConfiguration::precomputeEvaluations, sgpp::base::AdaptivityConfiguration::refinementFunctorType, reset(), sgpp::base::AdaptivityConfiguration::scalingCoefficients, sgpp::base::AdaptivityConfiguration::threshold_, sgpp::datadriven::DensityEstimationConfiguration::type_, python.utils.pca_normalize_dataset::u, and update().

◆ refine()

bool sgpp::datadriven::ModelFittingClassification::refine ( )

overridevirtual

Improve the accuracy of the classification by refining the grids of each class.

Returns: true if refinement could be performed for any grid based on the refinement configuration, else false.

Implements sgpp::datadriven::ModelFittingBase.

References sgpp::datadriven::ModelFittingBase::config, sgpp::base::AdaptivityConfiguration::numRefinements_, sgpp::datadriven::MultiGridRefinementFunctor::preComputeEvaluations(), sgpp::base::AdaptivityConfiguration::precomputeEvaluations, sgpp::datadriven::MultipleClassRefinementFunctor::refine(), sgpp::base::AdaptivityConfiguration::refinementFunctorType, and sgpp::datadriven::MultiGridRefinementFunctor::setGridIndex().

◆ reset()

void sgpp::datadriven::ModelFittingClassification::reset ( )

overridevirtual

Resets the state of the entire model.

Implements sgpp::datadriven::ModelFittingBase.

Referenced by fit().

◆ update()

void sgpp::datadriven::ModelFittingClassification::update ( Dataset & dataset )

overridevirtual

Updates the models for each class based on new data (streaming or batch learning)

Parameters

dataset the new data

Implements sgpp::datadriven::ModelFittingBase.

References sgpp::datadriven::ModelFittingBase::dataset, sgpp::base::DataVector::get(), sgpp::datadriven::Dataset::getData(), sgpp::datadriven::Dataset::getDimension(), sgpp::base::DataMatrix::getNrows(), sgpp::datadriven::Dataset::getNumberInstances(), sgpp::base::DataMatrix::getRow(), sgpp::datadriven::Dataset::getTargets(), python.statsfileInfo::i, friedman::p, parabolasimple::samples, and analyse_erg::tmp.

Referenced by fit().

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

datadriven/src/sgpp/datadriven/datamining/modules/fitting/ModelFittingClassification.hpp
datadriven/src/sgpp/datadriven/datamining/modules/fitting/ModelFittingClassification.cpp

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ ModelFittingClassification()

Member Function Documentation

◆ evaluate() [1/2]

◆ evaluate() [2/2]

◆ fit()

◆ refine()

◆ reset()

◆ update()