The KDEModel provides an abstraction for the KDE class, abstracting away the KernelType and TreeType parameters and allowing those to be specified at runtime. More...

#include <kde_model.hpp>

Public Types
enum	TreeTypes { KD_TREE, BALL_TREE, COVER_TREE, OCTREE, R_TREE }

enum	KernelTypes { GAUSSIAN_KERNEL, EPANECHNIKOV_KERNEL, LAPLACIAN_KERNEL, SPHERICAL_KERNEL, TRIANGULAR_KERNEL }

Public Member Functions
	KDEModel (const double bandwidth=1.0, const double relError=KDEDefaultParams::relError, const double absError=KDEDefaultParams::absError, const KernelTypes kernelType=KernelTypes::GAUSSIAN_KERNEL, const TreeTypes treeType=TreeTypes::KD_TREE, const bool monteCarlo=KDEDefaultParams::mode, const double mcProb=KDEDefaultParams::mcProb, const size_t initialSampleSize=KDEDefaultParams::initialSampleSize, const double mcEntryCoef=KDEDefaultParams::mcEntryCoef, const double mcBreakCoef=KDEDefaultParams::mcBreakCoef)
	Initialize KDEModel. More...

	KDEModel (const KDEModel &other)
	Copy constructor of the given model.

	KDEModel (KDEModel &&other)
	Move constructor of the given model. Takes ownership of the model.

KDEModel &	operator= (const KDEModel &other)
	Copy the given model. More...

KDEModel &	operator= (KDEModel &&other)
	Take ownership of the contents of the given model. More...

	~KDEModel ()
	Destroy the KDEModel object.

template<typename Archive >
void	serialize (Archive &ar, const uint32_t version)
	Serialize the KDE model.

double	Bandwidth () const
	Get the bandwidth of the kernel.

void	Bandwidth (const double newBandwidth)
	Modify the bandwidth of the kernel.

double	RelativeError () const
	Get the relative error tolerance.

void	RelativeError (const double newRelError)
	Modify the relative error tolerance.

double	AbsoluteError () const
	Get the absolute error tolerance.

void	AbsoluteError (const double newAbsError)
	Modify the absolute error tolerance.

TreeTypes	TreeType () const
	Get the tree type of the model.

TreeTypes &	TreeType ()
	Modify the tree type of the model.

KernelTypes	KernelType () const
	Get the kernel type of the model.

KernelTypes &	KernelType ()
	Modify the kernel type of the model.

bool	MonteCarlo () const
	Get whether the model is using Monte Carlo estimations or not.

void	MonteCarlo (const bool newMonteCarlo)
	Modify whether the model is using Monte Carlo estimations or not.

double	MCProbability () const
	Get Monte Carlo probability of error being bounded by relative error.

void	MCProbability (const double newMCProb)
	Modify Monte Carlo probability of error being bounded by relative error.

size_t	MCInitialSampleSize () const
	Get the initial sample size for Monte Carlo estimations.

void	MCInitialSampleSize (const size_t newSampleSize)
	Modify the initial sample size for Monte Carlo estimations.

double	MCEntryCoefficient () const
	Get Monte Carlo entry coefficient.

void	MCEntryCoefficient (const double newEntryCoef)
	Modify Monte Carlo entry coefficient.

double	MCBreakCoefficient () const
	Get Monte Carlo break coefficient.

void	MCBreakCoefficient (const double newBreakCoef)
	Modify Monte Carlo break coefficient.

KDEMode	Mode () const
	Get the mode of the model.

KDEMode &	Mode ()
	Modify the mode of the model.

void	InitializeModel ()
	Initialize the KDE model.

void	BuildModel (arma::mat &&referenceSet)
	Build the KDE model with the given parameters and then trains it with the given reference data. More...

void	Evaluate (arma::mat &&querySet, arma::vec &estimations)
	Perform kernel density estimation on the given query set. More...

void	Evaluate (arma::vec &estimations)
	Perform kernel density estimation on the reference set. More...

Detailed Description

The KDEModel provides an abstraction for the KDE class, abstracting away the KernelType and TreeType parameters and allowing those to be specified at runtime.

This class is written for the sake of the kde binding, but it is not necessarily restricted to that usage.

Constructor & Destructor Documentation

◆ KDEModel()

mlpack::kde::KDEModel::KDEModel	(	const double	bandwidth = `1.0`,
		const double	relError = `KDEDefaultParams::relError`,
		const double	absError = `KDEDefaultParams::absError`,
		const KernelTypes	kernelType = `KernelTypes::GAUSSIAN_KERNEL`,
		const TreeTypes	treeType = `TreeTypes::KD_TREE`,
		const bool	monteCarlo = `KDEDefaultParams::mode`,
		const double	mcProb = `KDEDefaultParams::mcProb`,
		const size_t	initialSampleSize = `KDEDefaultParams::initialSampleSize`,
		const double	mcEntryCoef = `KDEDefaultParams::mcEntryCoef`,
		const double	mcBreakCoef = `KDEDefaultParams::mcBreakCoef`
	)

Initialize KDEModel.

Initialize the KDEModel with the given parameters.

Parameters

bandwidth	Bandwidth to use for the kernel.
relError	Maximum relative error tolerance for each point in the model. For example, 0.05 means that each value must be within 5% of the true KDE value.
absError	Maximum absolute error tolerance for each point in the model. For example, 0.1 means that for each point the value can have a maximum error of 0.1 units.
kernelType	Type of kernel to use.
treeType	Type of tree to use.
monteCarlo	Whether to use Monte Carlo estimations when possible.
mcProb	of a Monte Carlo estimation to be bounded by relative error tolerance.
initialSampleSize	Initial sample size for Monte Carlo estimations.
mcEntryCoef	Coefficient to control how much larger does the amount of node descendants has to be compared to the initial sample size in order for it to be a candidate for Monte Carlo estimations.
mcBreakCoef	Coefficient to control what fraction of the node's descendants evaluated is the limit before Monte Carlo estimation recurses.

Member Function Documentation

◆ BuildModel()

void mlpack::kde::KDEModel::BuildModel ( arma::mat && referenceSet )

Build the KDE model with the given parameters and then trains it with the given reference data.

Takes possession of the reference set to avoid a copy, so the reference set will not be usable after this.

Parameters

referenceSet Set of reference points.

◆ Evaluate() [1/2]

void mlpack::kde::KDEModel::Evaluate	(	arma::mat &&	querySet,
		arma::vec &	estimations
	)

Perform kernel density estimation on the given query set.

Takes possession of the query set to avoid a copy, so the query set will not be usable after this. If possible, it returns normalized estimations.

Precondition: The model has to be previously created with BuildModel.

Parameters

querySet	Set of query points.
estimations	Vector where the results will be stored in the same order as the query points.

◆ Evaluate() [2/2]

void mlpack::kde::KDEModel::Evaluate ( arma::vec & estimations )

Perform kernel density estimation on the reference set.

If possible, it returns normalized estimations.

Precondition: The model has to be previously created with BuildModel.

Parameters

estimations Vector where the results will be stored in the same order as the query points.

◆ operator=() [1/2]

KDEModel & mlpack::kde::KDEModel::operator= ( const KDEModel & other )

Copy the given model.

Parameters

other KDEModel to copy.

◆ operator=() [2/2]

KDEModel & mlpack::kde::KDEModel::operator= ( KDEModel && other )

Take ownership of the contents of the given model.

Parameters

other KDEModel to take ownership of.

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

src/mlpack/methods/kde/kde_model.hpp
src/mlpack/methods/kde/kde_model.cpp
src/mlpack/methods/kde/kde_model_impl.hpp

Public Types

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ KDEModel()

Member Function Documentation

◆ BuildModel()

◆ Evaluate() [1/2]

◆ Evaluate() [2/2]

◆ operator=() [1/2]

◆ operator=() [2/2]