The KDE class is a template class for performing Kernel Density Estimations. More...

Inheritance diagram for KDE< KernelType, MetricType, MatType, TreeType, DualTreeTraversalType, SingleTreeTraversalType >:

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Public Types
typedef TreeType< MetricType, kde::KDEStat, MatType >	Tree
	Convenience typedef. More...

Public Member Functions
	KDE (const double relError=KDEDefaultParams::relError, const double absError=KDEDefaultParams::absError, KernelType kernel=KernelType(), const KDEMode mode=KDEDefaultParams::mode, MetricType metric=MetricType(), const bool monteCarlo=KDEDefaultParams::monteCarlo, const double mcProb=KDEDefaultParams::mcProb, const size_t initialSampleSize=KDEDefaultParams::initialSampleSize, const double mcEntryCoef=KDEDefaultParams::mcEntryCoef, const double mcBreakCoef=KDEDefaultParams::mcBreakCoef)
	Initialize KDE object using custom instantiated Metric and Kernel objects. More...

	KDE (const KDE &other)
	Construct KDE object as a copy of the given model. More...

	KDE (KDE &&other)
	Construct KDE object taking ownership of the given model. More...

	~KDE ()
	Destroy the KDE object. More...

double	AbsoluteError () const
	Get absolute error tolerance. More...

void	AbsoluteError (const double newError)
	Modify absolute error tolerance (0 <= newError). More...

void	Evaluate (MatType querySet, arma::vec &estimations)
	Estimate density of each point in the query set given the data of the reference set. More...

void	Evaluate (Tree *queryTree, const std::vector< size_t > &oldFromNewQueries, arma::vec &estimations)
	Estimate density of each point in the query set given the data of an already created query tree. More...

void	Evaluate (arma::vec &estimations)
	Estimate density of each point in the reference set given the data of the reference set. More...

bool	IsTrained () const
	Check whether KDE model is trained or not. More...

const KernelType &	Kernel () const
	Get the kernel. More...

KernelType &	Kernel ()
	Modify the kernel. More...

double	MCBreakCoef () const
	Get Monte Carlo break coefficient. More...

void	MCBreakCoef (const double newCoef)
	Modify Monte Carlo break coefficient. (0 < newCoef <= 1). More...

double	MCEntryCoef () const
	Get Monte Carlo entry coefficient. More...

void	MCEntryCoef (const double newCoef)
	Modify Monte Carlo entry coefficient. (newCoef >= 1). More...

size_t	MCInitialSampleSize () const
	Get Monte Carlo initial sample size. More...

size_t &	MCInitialSampleSize ()
	Modify Monte Carlo initial sample size. More...

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

void	MCProb (const double newProb)
	Modify Monte Carlo probability of error being bounded by relative error. More...

const MetricType &	Metric () const
	Get the metric. More...

MetricType &	Metric ()
	Modify the metric. More...

KDEMode	Mode () const
	Get the mode of KDE. More...

KDEMode &	Mode ()
	Modify the mode of KDE. More...

bool	MonteCarlo () const
	Get whether Monte Carlo estimations are being used or not. More...

bool &	MonteCarlo ()
	Modify whether Monte Carlo estimations are being used or not. More...

KDE &	operator= (const KDE &other)
	Copy a KDE model. More...

KDE &	operator= (KDE &&other)
	Move a KDE model. More...

bool	OwnsReferenceTree () const
	Check whether reference tree is owned by the KDE model. More...

Tree *	ReferenceTree ()
	Get the reference tree. More...

double	RelativeError () const
	Get relative error tolerance. More...

void	RelativeError (const double newError)
	Modify relative error tolerance (0 <= newError <= 1). More...

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

void	Train (MatType referenceSet)
	Trains the KDE model. More...

void	Train (Tree referenceTree, std::vector< size_t > oldFromNewReferences)
	Trains the KDE model. More...

Detailed Description

template<typename KernelType = kernel::GaussianKernel, typename MetricType = mlpack::metric::EuclideanDistance, typename MatType = arma::mat, template< typename TreeMetricType, typename TreeStatType, typename TreeMatType > class TreeType = tree::KDTree, template< typename RuleType > class DualTreeTraversalType = TreeType<MetricType, kde::KDEStat, MatType>::template DualTreeTraverser, template< typename RuleType > class SingleTreeTraversalType = TreeType<MetricType, kde::KDEStat, MatType>::template SingleTreeTraverser>
class mlpack::kde::KDE< KernelType, MetricType, MatType, TreeType, DualTreeTraversalType, SingleTreeTraversalType >

The KDE class is a template class for performing Kernel Density Estimations.

In statistics, kernel density estimation is a way to estimate the probability density function of a variable in a non parametric way. This implementation performs this estimation using a tree-independent dual-tree algorithm. Details about this algorithm are available in KDERules.

Template Parameters

KernelType	Kernel function to use for KDE calculations.
MetricType	Metric to use for KDE calculations.
MatType	Type of data to use.
TreeType	Type of tree to use; must satisfy the TreeType policy API.
DualTreeTraversalType	Type of dual-tree traversal to use.
SingleTreeTraversalType	Type of single-tree traversal to use.

Definition at line 88 of file kde.hpp.

Member Typedef Documentation

◆ Tree

typedef TreeType<MetricType, kde::KDEStat, MatType> Tree

Convenience typedef.

Definition at line 92 of file kde.hpp.

Constructor & Destructor Documentation

◆ KDE() [1/3]

KDE	(	const double	relError = `KDEDefaultParams::relError`,
		const double	absError = `KDEDefaultParams::absError`,
		KernelType	kernel = `KernelType()`,
		const KDEMode	mode = `KDEDefaultParams::mode`,
		MetricType	metric = `MetricType()`,
		const bool	monteCarlo = `KDEDefaultParams::monteCarlo`,
		const double	mcProb = `KDEDefaultParams::mcProb`,
		const size_t	initialSampleSize = `KDEDefaultParams::initialSampleSize`,
		const double	mcEntryCoef = `KDEDefaultParams::mcEntryCoef`,
		const double	mcBreakCoef = `KDEDefaultParams::mcBreakCoef`
	)

Initialize KDE object using custom instantiated Metric and Kernel objects.

Parameters

relError	Relative error tolerance of the model.
absError	Absolute error tolerance of the model.
kernel	Instantiated kernel object.
mode	Mode for the algorithm.
metric	Instantiated metric object.
monteCarlo	Whether to use Monte Carlo estimations when possible.
mcProb	Probability 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.

◆ KDE() [2/3]

KDE ( const KDE< KernelType, MetricType, MatType, TreeType, DualTreeTraversalType, SingleTreeTraversalType > & other )

Construct KDE object as a copy of the given model.

This may be computationally intensive!

Parameters

other KDE object to copy.

◆ KDE() [3/3]

KDE ( KDE< KernelType, MetricType, MatType, TreeType, DualTreeTraversalType, SingleTreeTraversalType > && other )

Construct KDE object taking ownership of the given model.

Parameters

other KDE object to take ownership of.

◆ ~KDE()

~KDE ( )

Destroy the KDE object.

If this object created any trees, they will be deleted. If you created the trees then you have to delete them yourself.

Member Function Documentation

◆ AbsoluteError() [1/2]

double AbsoluteError ( ) const

inline

Get absolute error tolerance.

Definition at line 250 of file kde.hpp.

◆ AbsoluteError() [2/2]

void AbsoluteError ( const double newError )

Modify absolute error tolerance (0 <= newError).

◆ Evaluate() [1/3]

void Evaluate	(	MatType	querySet,
		arma::vec &	estimations
	)

Estimate density of each point in the query set given the data of the reference set.

The result is stored in an estimations vector. Estimations might not be normalized.

Dimension of each point in the query set must match the dimension of each point in the reference set.
Use std::move if the query set is no longer needed.

Precondition: The model has to be previously trained.

Parameters

querySet	Set of query points to get the density of.
estimations	Object which will hold the density of each query point.

◆ Evaluate() [2/3]

void Evaluate	(	Tree *	queryTree,
		const std::vector< size_t > &	oldFromNewQueries,
		arma::vec &	estimations
	)

Estimate density of each point in the query set given the data of an already created query tree.

The result is stored in an estimations vector. Estimations might not be normalized.

Dimension of each point in the queryTree dataset must match the dimension of each point in the reference set.
Use std::move if the query tree is no longer needed.

Precondition: The model has to be previously trained and mode has to be dual-tree.

Parameters

queryTree	Tree of query points to get the density of.
oldFromNewQueries	Mappings of query points to the tree dataset.
estimations	Object which will hold the density of each query point.

◆ Evaluate() [3/3]

void Evaluate ( arma::vec & estimations )

Estimate density of each point in the reference set given the data of the reference set.

It does not compute the estimation of a point with itself. The result is stored in an estimations vector. Estimations might not be normalized.

Precondition: The model has to be previously trained.

Parameters

estimations Object which will hold the density of each reference point.

◆ IsTrained()

bool IsTrained ( ) const

inline

Check whether KDE model is trained or not.

Definition at line 259 of file kde.hpp.

◆ Kernel() [1/2]

const KernelType& Kernel ( ) const

inline

Get the kernel.

Definition at line 229 of file kde.hpp.

◆ Kernel() [2/2]

KernelType& Kernel ( )

inline

Modify the kernel.

Definition at line 232 of file kde.hpp.

◆ MCBreakCoef() [1/2]

double MCBreakCoef ( ) const

inline

Get Monte Carlo break coefficient.

Definition at line 293 of file kde.hpp.

◆ MCBreakCoef() [2/2]

void MCBreakCoef ( const double newCoef )

Modify Monte Carlo break coefficient. (0 < newCoef <= 1).

◆ MCEntryCoef() [1/2]

double MCEntryCoef ( ) const

inline

Get Monte Carlo entry coefficient.

Definition at line 287 of file kde.hpp.

◆ MCEntryCoef() [2/2]

void MCEntryCoef ( const double newCoef )

Modify Monte Carlo entry coefficient. (newCoef >= 1).

◆ MCInitialSampleSize() [1/2]

size_t MCInitialSampleSize ( ) const

inline

Get Monte Carlo initial sample size.

Definition at line 281 of file kde.hpp.

◆ MCInitialSampleSize() [2/2]

size_t& MCInitialSampleSize ( )

inline

Modify Monte Carlo initial sample size.

Definition at line 284 of file kde.hpp.

◆ MCProb() [1/2]

double MCProb ( ) const

inline

Get Monte Carlo probability of error being bounded by relative error.

Definition at line 274 of file kde.hpp.

◆ MCProb() [2/2]

void MCProb ( const double newProb )

Modify Monte Carlo probability of error being bounded by relative error.

(0 <= newProb < 1).

◆ Metric() [1/2]

const MetricType& Metric ( ) const

inline

Get the metric.

Definition at line 235 of file kde.hpp.

◆ Metric() [2/2]

MetricType& Metric ( )

inline

Modify the metric.

Definition at line 238 of file kde.hpp.

◆ Mode() [1/2]

KDEMode Mode ( ) const

inline

Get the mode of KDE.

Definition at line 262 of file kde.hpp.

◆ Mode() [2/2]

KDEMode& Mode ( )

inline

Modify the mode of KDE.

Definition at line 265 of file kde.hpp.

◆ MonteCarlo() [1/2]

bool MonteCarlo ( ) const

inline

Get whether Monte Carlo estimations are being used or not.

Definition at line 268 of file kde.hpp.

◆ MonteCarlo() [2/2]

bool& MonteCarlo ( )

inline

Modify whether Monte Carlo estimations are being used or not.

Definition at line 271 of file kde.hpp.

◆ operator=() [1/2]

KDE& operator= ( const KDE< KernelType, MetricType, MatType, TreeType, DualTreeTraversalType, SingleTreeTraversalType > & other )

Copy a KDE model.

Parameters

other KDE model to copy.

◆ operator=() [2/2]

KDE& operator= ( KDE< KernelType, MetricType, MatType, TreeType, DualTreeTraversalType, SingleTreeTraversalType > && other )

Move a KDE model.

Parameters

other KDE model to copy.

◆ OwnsReferenceTree()

bool OwnsReferenceTree ( ) const

inline

Check whether reference tree is owned by the KDE model.

Definition at line 256 of file kde.hpp.

◆ ReferenceTree()

Tree* ReferenceTree ( )

inline

Get the reference tree.

Definition at line 241 of file kde.hpp.

◆ RelativeError() [1/2]

double RelativeError ( ) const

inline

Get relative error tolerance.

Definition at line 244 of file kde.hpp.

◆ RelativeError() [2/2]

void RelativeError ( const double newError )

Modify relative error tolerance (0 <= newError <= 1).

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t	version
	)

Serialize the model.

◆ Train() [1/2]

void Train ( MatType referenceSet )

Trains the KDE model.

It builds a tree using a reference set.

Use std::move if the reference set is no longer needed.

Parameters

referenceSet Set of reference data.

◆ Train() [2/2]

void Train	(	Tree *	referenceTree,
		std::vector< size_t > *	oldFromNewReferences
	)

Trains the KDE model.

Sets the reference tree to an already created tree.

If TreeTraits<TreeType>::RearrangesDataset is false then it is possible to use an empty oldFromNewReferences vector.

Parameters

referenceTree	Built reference tree.
oldFromNewReferences	Permutations of reference points obtained during tree generation.

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

/home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/kde/kde.hpp

Public Types

Public Member Functions

Detailed Description

Member Typedef Documentation

◆ Tree

Constructor & Destructor Documentation

◆ KDE() [1/3]

◆ KDE() [2/3]

◆ KDE() [3/3]

◆ ~KDE()

Member Function Documentation

◆ AbsoluteError() [1/2]

◆ AbsoluteError() [2/2]

◆ Evaluate() [1/3]

◆ Evaluate() [2/3]

◆ Evaluate() [3/3]

◆ IsTrained()

◆ Kernel() [1/2]

◆ Kernel() [2/2]

◆ MCBreakCoef() [1/2]

◆ MCBreakCoef() [2/2]

◆ MCEntryCoef() [1/2]

◆ MCEntryCoef() [2/2]

◆ MCInitialSampleSize() [1/2]

◆ MCInitialSampleSize() [2/2]

◆ MCProb() [1/2]

◆ MCProb() [2/2]

◆ Metric() [1/2]

◆ Metric() [2/2]

◆ Mode() [1/2]

◆ Mode() [2/2]

◆ MonteCarlo() [1/2]

◆ MonteCarlo() [2/2]

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ OwnsReferenceTree()

◆ ReferenceTree()

◆ RelativeError() [1/2]

◆ RelativeError() [2/2]

◆ serialize()

◆ Train() [1/2]

◆ Train() [2/2]