Naive Bayes classifier. More...

#include <shark/Models/NBClassifier.h>

Inheritance diagram for shark::NBClassifier< InputType, OutputType >:

Public Types
typedef base_type::BatchInputType	BatchInputType

typedef base_type::BatchOutputType	BatchOutputType

typedef std::vector< double >	ClassPriorsType
	Type of class distribution. More...

typedef boost::shared_ptr< AbstractDistribution >	AbstractDistPtr

typedef std::vector< std::vector< AbstractDistPtr > >	FeatureDistributionsType
	Type of features distribution. More...

typedef std::pair< std::size_t, std::size_t >	DistSizeType
	Size of distribution in format of (number of classes, number of features) More...

Public Types inherited from shark::AbstractModel< InputType, OutputType >
enum	Feature

typedef InputType	InputType
	Defines the input type of the model. More...

typedef OutputType	OutputType
	Defines the output type of the model. More...

typedef Batch< InputType >::type	BatchInputType
	defines the batch type of the input type. More...

typedef Batch< OutputType >::type	BatchOutputType
	defines the batch type of the output type More...

typedef TypedFlags< Feature >	Features

typedef TypedFeatureNotAvailableException< Feature >	FeatureNotAvailableException

Public Member Functions
	NBClassifier (std::size_t classSize, std::size_t featureSize)

	NBClassifier (FeatureDistributionsType const &featureDists)

std::string	name () const
	From INameable: return the class name. More...

AbstractDistribution &	getFeatureDist (std::size_t classIndex, std::size_t featureIndex) const

DistSizeType	getDistSize () const

boost::shared_ptr< State >	createState () const
	Creates an internal state of the model. More...

void	eval (BatchInputType const &patterns, BatchOutputType &outputs, State &state) const
	see AbstractModel::eval More...

void	setClassPrior (OutputType classToAdd, double probability)

RealVector	parameterVector () const
	This model does not have any parameters. More...

void	setParameterVector (const RealVector &param)
	This model does not have any parameters. More...

Public Member Functions inherited from shark::AbstractModel< InputType, OutputType >
	AbstractModel ()

virtual	~AbstractModel ()

const Features &	features () const

virtual void	updateFeatures ()

bool	hasFirstParameterDerivative () const
	Returns true when the first parameter derivative is implemented. More...

bool	hasSecondParameterDerivative () const
	Returns true when the second parameter derivative is implemented. More...

bool	hasFirstInputDerivative () const
	Returns true when the first input derivative is implemented. More...

bool	hasSecondInputDerivative () const
	Returns true when the second parameter derivative is implemented. More...

bool	isSequential () const

virtual void	read (InArchive &archive)
	From ISerializable, reads a model from an archive. More...

virtual void	write (OutArchive &archive) const
	writes a model to an archive More...

virtual void	eval (BatchInputType const &patterns, BatchOutputType &outputs) const
	Standard interface for evaluating the response of the model to a batch of patterns. More...

virtual void	eval (InputType const &pattern, OutputType &output) const
	Standard interface for evaluating the response of the model to a single pattern. More...

Data< OutputType >	operator() (Data< InputType > const &patterns) const
	Model evaluation as an operator for a whole dataset. This is a convenience function. More...

OutputType	operator() (InputType const &pattern) const
	Model evaluation as an operator for a single pattern. This is a convenience function. More...

BatchOutputType	operator() (BatchInputType const &patterns) const
	Model evaluation as an operator for a single pattern. This is a convenience function. More...

virtual void	weightedParameterDerivative (BatchInputType const &pattern, BatchOutputType const &coefficients, State const &state, RealVector &derivative) const
	calculates the weighted sum of derivatives w.r.t the parameters. More...

virtual void	weightedParameterDerivative (BatchInputType const &pattern, BatchOutputType const &coefficients, Batch< RealMatrix >::type const &errorHessian, State const &state, RealVector &derivative, RealMatrix &hessian) const
	calculates the weighted sum of derivatives w.r.t the parameters More...

virtual void	weightedInputDerivative (BatchInputType const &pattern, BatchOutputType const &coefficients, State const &state, BatchInputType &derivative) const
	calculates the weighted sum of derivatives w.r.t the inputs More...

virtual void	weightedInputDerivative (BatchInputType const &pattern, BatchOutputType const &coefficients, typename Batch< RealMatrix >::type const &errorHessian, State const &state, RealMatrix &derivative, Batch< RealMatrix >::type &hessian) const
	calculates the weighted sum of derivatives w.r.t the inputs More...

virtual void	weightedDerivatives (BatchInputType const &patterns, BatchOutputType const &coefficients, State const &state, RealVector &parameterDerivative, BatchInputType &inputDerivative) const
	calculates weighted input and parameter derivative at the same time More...

Public Member Functions inherited from shark::IParameterizable
virtual	~IParameterizable ()

virtual std::size_t	numberOfParameters () const
	Return the number of parameters. More...

Public Member Functions inherited from shark::INameable
virtual	~INameable ()

Public Member Functions inherited from shark::ISerializable
virtual	~ISerializable ()
	Virtual d'tor. More...

void	load (InArchive &archive, unsigned int version)
	Versioned loading of components, calls read(...). More...

void	save (OutArchive &archive, unsigned int version) const
	Versioned storing of components, calls write(...). More...

	BOOST_SERIALIZATION_SPLIT_MEMBER ()

Protected Attributes

FeatureDistributionsType	m_featureDistributions

ClassPriorsType	m_classPriors

Protected Attributes inherited from shark::AbstractModel< InputType, OutputType >
Features	m_features

Detailed Description

template<class InputType = RealVector, class OutputType = unsigned int>
class shark::NBClassifier< InputType, OutputType >

Naive Bayes classifier.

This model summarizes a Naive Bayes classifier, which assumes that the data X is generated by a mixture of class-conditional (i.e., dependent on the value of the class variable Y) distributions. Furthermore, the Naive Bayes assumption introduces the additional constraint that the attribute values Xi are independent of one another within each of these mixture components.

Definition at line 56 of file NBClassifier.h.

Member Typedef Documentation

◆ AbstractDistPtr

template<class InputType = RealVector, class OutputType = unsigned int>

typedef boost::shared_ptr<AbstractDistribution> shark::NBClassifier< InputType, OutputType >::AbstractDistPtr

Definition at line 72 of file NBClassifier.h.

◆ BatchInputType

template<class InputType = RealVector, class OutputType = unsigned int>

typedef base_type::BatchInputType shark::NBClassifier< InputType, OutputType >::BatchInputType

Definition at line 66 of file NBClassifier.h.

◆ BatchOutputType

template<class InputType = RealVector, class OutputType = unsigned int>

typedef base_type::BatchOutputType shark::NBClassifier< InputType, OutputType >::BatchOutputType

Definition at line 67 of file NBClassifier.h.

◆ ClassPriorsType

template<class InputType = RealVector, class OutputType = unsigned int>

typedef std::vector<double> shark::NBClassifier< InputType, OutputType >::ClassPriorsType

Type of class distribution.

Definition at line 70 of file NBClassifier.h.

◆ DistSizeType

template<class InputType = RealVector, class OutputType = unsigned int>

typedef std::pair<std::size_t, std::size_t> shark::NBClassifier< InputType, OutputType >::DistSizeType

Size of distribution in format of (number of classes, number of features)

Definition at line 78 of file NBClassifier.h.

◆ FeatureDistributionsType

template<class InputType = RealVector, class OutputType = unsigned int>

typedef std::vector<std::vector<AbstractDistPtr> > shark::NBClassifier< InputType, OutputType >::FeatureDistributionsType

Type of features distribution.

Definition at line 75 of file NBClassifier.h.

Constructor & Destructor Documentation

◆ NBClassifier() [1/2]

template<class InputType = RealVector, class OutputType = unsigned int>

shark::NBClassifier< InputType, OutputType >::NBClassifier	(	std::size_t	classSize,
		std::size_t	featureSize
	)

inline

Ctor Will build hypothesis that all features in each class follows Normal distribution

Parameters

classSize	size of class
featureSize	size of feature

Definition at line 84 of file NBClassifier.h.

References shark::NBClassifier< InputType, OutputType >::m_featureDistributions, and SIZE_CHECK.

◆ NBClassifier() [2/2]

template<class InputType = RealVector, class OutputType = unsigned int>

shark::NBClassifier< InputType, OutputType >::NBClassifier ( FeatureDistributionsType const & featureDists )

inlineexplicit

Ctor The distributions for each feature in each class are given by featureDists

Parameters

featureDists the distribution of features

Definition at line 100 of file NBClassifier.h.

References shark::NBClassifier< InputType, OutputType >::m_featureDistributions, and SIZE_CHECK.

Member Function Documentation

◆ createState()

template<class InputType = RealVector, class OutputType = unsigned int>

boost::shared_ptr<State> shark::NBClassifier< InputType, OutputType >::createState ( ) const

inlinevirtual

Creates an internal state of the model.

The state is needed when the derivatives are to be calculated. Eval can store a state which is then reused to speed up the calculations of the derivatives. This also allows eval to be evaluated in parallel!

Reimplemented from shark::AbstractModel< InputType, OutputType >.

Definition at line 134 of file NBClassifier.h.

◆ eval()

template<class InputType = RealVector, class OutputType = unsigned int>

void shark::NBClassifier< InputType, OutputType >::eval	(	BatchInputType const &	patterns,
		BatchOutputType &	outputs,
		State &	state
	)		const

inlinevirtual

see AbstractModel::eval

Implements shark::AbstractModel< InputType, OutputType >.

Definition at line 139 of file NBClassifier.h.

References shark::NBClassifier< InputType, OutputType >::m_classPriors, shark::NBClassifier< InputType, OutputType >::m_featureDistributions, remora::max(), shark::safeLog(), SHARK_ASSERT, and SIZE_CHECK.

◆ getDistSize()

template<class InputType = RealVector, class OutputType = unsigned int>

DistSizeType shark::NBClassifier< InputType, OutputType >::getDistSize ( ) const

inline

Get the size of distribution in format of (class size, feature size)

Returns: the size of distribution

Definition at line 126 of file NBClassifier.h.

References shark::AbstractModel< InputType, OutputType >::eval(), shark::NBClassifier< InputType, OutputType >::m_featureDistributions, and SIZE_CHECK.

◆ getFeatureDist()

template<class InputType = RealVector, class OutputType = unsigned int>

AbstractDistribution& shark::NBClassifier< InputType, OutputType >::getFeatureDist	(	std::size_t	classIndex,
		std::size_t	featureIndex
	)		const

inline

Get a feature distribution for feature featureIndex given class classIndex

Parameters

classIndex	index of class
featureIndex	index of feature

Returns: the distribution for feature featureIndex given class classIndex

Definition at line 114 of file NBClassifier.h.

References shark::NBClassifier< InputType, OutputType >::m_featureDistributions, SHARK_ASSERT, and SIZE_CHECK.

◆ name()

template<class InputType = RealVector, class OutputType = unsigned int>

std::string shark::NBClassifier< InputType, OutputType >::name ( ) const

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 107 of file NBClassifier.h.

◆ parameterVector()

template<class InputType = RealVector, class OutputType = unsigned int>

RealVector shark::NBClassifier< InputType, OutputType >::parameterVector ( ) const

inlinevirtual

This model does not have any parameters.

Reimplemented from shark::IParameterizable.

Definition at line 185 of file NBClassifier.h.

◆ setClassPrior()

template<class InputType = RealVector, class OutputType = unsigned int>

void shark::NBClassifier< InputType, OutputType >::setClassPrior	(	OutputType	classToAdd,
		double	probability
	)

inline

Set prior distribution of class to be probability

Parameters

classToAdd	the class of which probability will be updated
probability	the probability of the class

Template Parameters

OutputType the type of output class

Definition at line 176 of file NBClassifier.h.

References shark::NBClassifier< InputType, OutputType >::m_classPriors, and SHARKEXCEPTION.

◆ setParameterVector()

template<class InputType = RealVector, class OutputType = unsigned int>

void shark::NBClassifier< InputType, OutputType >::setParameterVector ( const RealVector & param )

inlinevirtual

This model does not have any parameters.

Reimplemented from shark::IParameterizable.

Definition at line 190 of file NBClassifier.h.

References SHARK_ASSERT.

Member Data Documentation

◆ m_classPriors

template<class InputType = RealVector, class OutputType = unsigned int>

ClassPriorsType shark::NBClassifier< InputType, OutputType >::m_classPriors

protected

Definition at line 199 of file NBClassifier.h.

Referenced by shark::NBClassifier< InputType, OutputType >::eval(), and shark::NBClassifier< InputType, OutputType >::setClassPrior().

◆ m_featureDistributions

template<class InputType = RealVector, class OutputType = unsigned int>

FeatureDistributionsType shark::NBClassifier< InputType, OutputType >::m_featureDistributions

protected

Feature and class distributions

Definition at line 198 of file NBClassifier.h.

Referenced by shark::NBClassifier< InputType, OutputType >::eval(), shark::NBClassifier< InputType, OutputType >::getDistSize(), shark::NBClassifier< InputType, OutputType >::getFeatureDist(), and shark::NBClassifier< InputType, OutputType >::NBClassifier().

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

include/shark/Models/NBClassifier.h

Public Types

Public Member Functions

Protected Attributes

Detailed Description

template<class InputType = RealVector, class OutputType = unsigned int> class shark::NBClassifier< InputType, OutputType >

Member Typedef Documentation

◆ AbstractDistPtr

◆ BatchInputType

◆ BatchOutputType

◆ ClassPriorsType

◆ DistSizeType

◆ FeatureDistributionsType

Constructor & Destructor Documentation

◆ NBClassifier() [1/2]

◆ NBClassifier() [2/2]

Member Function Documentation

◆ createState()

◆ eval()

◆ getDistSize()

◆ getFeatureDist()

◆ name()

◆ parameterVector()

◆ setClassPrior()

◆ setParameterVector()

Member Data Documentation

◆ m_classPriors

◆ m_featureDistributions

template<class InputType = RealVector, class OutputType = unsigned int>
class shark::NBClassifier< InputType, OutputType >