Inheritance diagram for mltk::classifier::PrimalClassifier< T >:

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Collaboration diagram for mltk::classifier::PrimalClassifier< T >:

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Public Member Functions
	PrimalClassifier (DataPointer< T > samples)

	PrimalClassifier (mltk::Data< T > &samples)

	PrimalClassifier (const PrimalClassifier< T > &primal_learner)

double	evaluate (const Point< T > &p, bool raw_value=false) override
	Returns the class of a feature point based on the trained Learner. More...

std::string	getFormulationString () override
	getFormulationString Returns a string that represents the formulation of the learner (Primal or Dual). More...

double	getP () const
	GetP Return the value of the p norm. More...

double	getQ () const
	GetQ Return the value of the q norm. More...

void	setqNorm (double q)
	setqNorm Set the q norm used by the classifier. (Euclidean norm is the default) More...

void	setpNorm (double p)
	setpNorm Set the p norm used by the classifier. (Euclidean norm is the default) More...

void	setFlexible (double flexible)
	Set flexibity of the classifier. More...

void	setAlphaAprox (double alpha_aprox)
	Set the percentage of the aproximation. More...

Public Member Functions inherited from mltk::classifier::Classifier< T >
	Classifier (DataPointer< T > samples)

	Classifier (const Classifier< T > &classifier)

virtual double	evaluateProbability (const mltk::Point< double > &p)

mltk::Point< int >	batchEvaluateProbability (const mltk::Data< T > &data)

Solution	getSolution () const
	getSolution Returns the solution of the classifier. More...

Solution *	getSolutionRef ()
	getSolution Returns a reference to the solution of the classifier. More...

void	setGamma (double gamma)
	Set the gamma (margin) of the classifier. More...

void	setW (std::vector< double > w)
	setW Set the weights vector of the classifier. More...

void	setSolution (Solution solution)
	setSolution Set a solution for the classifier. More...

Public Member Functions inherited from mltk::Learner< T >
	Learner (DataPointer< T > _samples)

	Learner (const Learner< T > &learner)

virtual bool	train ()=0
	Function that execute the training phase of a Learner. More...

virtual mltk::Point< double >	batchEvaluate (const Data< T > &data)
	evaluate a batch of points. More...

auto	getSamples ()
	Get the Data used by the learner. More...

double	getElapsedTime () const
	Get the elapsed time in the training phase of the Learner. More...

int	getCtot () const
	Get the total number of updates of the Learner. More...

int	getSteps () const
	getSteps Returns the number of steps through the data by the Learner. More...

int	getUpdates () const
	getUpdates Returns the number of updates needed to get to the the solution. More...

double	getMaxTime () const
	getMaxTime Returns the maximum running time in the training phase of the Learner. More...

double	getPredictionProbability () const
	Get the probability of the last prediction. More...

void	setSeed (const size_t _seed)
	Set the seed to be used by the learner. More...

virtual void	setSamples (const Data< T > &data)
	setSamples Set the samples used by the Learner. More...

virtual void	setSamples (DataPointer< T > data)
	setSamples Set the samples used by the Learner. More...

void	setTimer (Timer _timer)
	setTimer Set the timer used by the Learner. More...

void	setSteps (int _steps)
	Set the partial number of steps used in the training phase of the Learner. More...

void	setCtot (int _ctot)
	Set the partial number of updates of the Learner. More...

void	setVerbose (int _verbose)
	Set the level of verbose. More...

void	setStartTime (double stime)
	setStartTime Set the initial time of the Learner. More...

void	setMaxTime (double maxtime)
	Set the max time of execution. More...

void	setEPS (double eps)
	setEPS Set the precision of the Learner. More...

void	setMaxIterations (int max_it)
	setMaxIterations Set the max number of iterations of the Learner. More...

void	setMaxEpochs (int MAX_EPOCHS)
	Set the max number of epochs for the learner training. More...

void	setMaxUpdates (int max_up)
	setMaxIterations Set the max number of updates of the Learner. More...

void	setLearningRate (double learning_rate)
	Set the learning rate of the Learner. More...

Protected Attributes
std::vector< double >	w
	Weights vector. More...

double	q = 2
	Norm used in the classification. (Euclidean Norm is the default) More...

double	p = 2

double	flexible = 0.0
	Flexibility. More...

double	alpha_aprox = 0.0
	Percentage of aproximation of the result. More...

Protected Attributes inherited from mltk::classifier::Classifier< T >
bool	hasInitialSolution = false
	Inform if there's an initial solution. More...

std::vector< Point< T > >	svs
	Support vectors points. More...

Solution	solution
	Classifier solution. More...

double	gamma = 0
	Classifier margin. More...

Protected Attributes inherited from mltk::Learner< T >
std::shared_ptr< Data< T > >	samples
	Samples used in the model training. More...

double	rate
	Learning rate. More...

double	start_time
	Initial time. More...

double	max_time
	Maximum time of training. More...

int	steps
	Number of steps in the data. More...

int	ctot
	Number of updates of the weights. More...

double	EPS
	Max precision. More...

double	MIN_INC
	Minimun Increment. More...

int	MAX_IT
	Max number of iterations. More...

int	MAX_UP
	Max number of updates. More...

int	MAX_EPOCH

int	verbose
	Verbose level of the output. More...

Timer	timer
	Timer used to measure the time elapsed in the execution of a Learner. More...

size_t	seed
	seed for random operations. More...

double	pred_prob

Member Function Documentation

◆ evaluate()

template<typename T >

double mltk::classifier::PrimalClassifier< T >::evaluate	(	const Point< T > &	p,
		bool	raw_value = `false`
	)

inlineoverridevirtual

Returns the class of a feature point based on the trained Learner.

Parameters

p	Point to be evaluated.

Returns: Prediction made by the learner.

Implements mltk::Learner< T >.

◆ getFormulationString()

template<typename T >

std::string mltk::classifier::PrimalClassifier< T >::getFormulationString ( )

inlineoverridevirtual

getFormulationString Returns a string that represents the formulation of the learner (Primal or Dual).

Returns: std::string

Implements mltk::Learner< T >.

◆ getP()

template<typename T >

double mltk::classifier::PrimalClassifier< T >::getP ( ) const

inline

GetP Return the value of the p norm.

Returns: double

◆ getQ()

template<typename T >

double mltk::classifier::PrimalClassifier< T >::getQ ( ) const

inline

GetQ Return the value of the q norm.

Returns: double

◆ setAlphaAprox()

template<typename T >

void mltk::classifier::PrimalClassifier< T >::setAlphaAprox ( double alpha_aprox )

inline

Set the percentage of the aproximation.

Parameters

alpha_aprox Aproximation.

◆ setFlexible()

template<typename T >

void mltk::classifier::PrimalClassifier< T >::setFlexible ( double flexible )

inline

Set flexibity of the classifier.

Parameters

flexible flexibility.

◆ setpNorm()

template<typename T >

void mltk::classifier::PrimalClassifier< T >::setpNorm ( double p )

inline

setpNorm Set the p norm used by the classifier. (Euclidean norm is the default)

Parameters

p	Norm that will be used by the classifier.

◆ setqNorm()

template<typename T >

void mltk::classifier::PrimalClassifier< T >::setqNorm ( double q )

inline

setqNorm Set the q norm used by the classifier. (Euclidean norm is the default)

Parameters

q	Norm that will be used by the classifier.

Member Data Documentation

◆ alpha_aprox

template<typename T >

double mltk::classifier::PrimalClassifier< T >::alpha_aprox = 0.0

protected

Percentage of aproximation of the result.

◆ flexible

template<typename T >

double mltk::classifier::PrimalClassifier< T >::flexible = 0.0

protected

Flexibility.

◆ q

template<typename T >

double mltk::classifier::PrimalClassifier< T >::q = 2

protected

Norm used in the classification. (Euclidean Norm is the default)

◆ w

template<typename T >

std::vector<double> mltk::classifier::PrimalClassifier< T >::w

protected

Weights vector.

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

PrimalClassifier.hpp

Public Member Functions

Protected Attributes

Member Function Documentation

◆ evaluate()

◆ getFormulationString()

◆ getP()

◆ getQ()

◆ setAlphaAprox()

◆ setFlexible()

◆ setpNorm()

◆ setqNorm()

Member Data Documentation

◆ alpha_aprox

◆ flexible

◆ q

◆ w