Inheritance diagram for mltk::Learner< T >:

Collaboration diagram for mltk::Learner< T >:

Public Member Functions
	Learner (DataPointer< T > _samples)

	Learner (const Learner< T > &learner)

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

virtual double	evaluate (const Point< T > &p, bool raw_value=false)=0
	Returns the class of a feature point based on the trained Learner. More...

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

virtual std::string	getFormulationString ()=0
	getFormulationString Returns a string that represents the formulation of the learner (Primal or Dual). 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::shared_ptr< Data< T > >	samples
	Samples used in the model training. More...

double	rate = 0.5f
	Learning rate. More...

double	start_time = 0.0f
	Initial time. More...

double	max_time = 110
	Maximum time of training. More...

int	steps = 0
	Number of steps in the data. More...

int	ctot = 0
	Number of updates of the weights. More...

double	EPS = 0.0000001
	Max precision. More...

double	MIN_INC = 1.001
	Minimun Increment. More...

int	MAX_IT = 1E9
	Max number of iterations. More...

int	MAX_UP = 1E9
	Max number of updates. More...

int	MAX_EPOCH = 1E9

int	verbose = 1
	Verbose level of the output. More...

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

size_t	seed = 0
	seed for random operations. More...

double	pred_prob = 1.0

Member Function Documentation

◆ batchEvaluate()

template<typename T >

mltk::Point< double > mltk::Learner< T >::batchEvaluate ( const Data< T > & data )

virtual

evaluate a batch of points.

Parameters

data	dataset containing points for evaluation.

Returns: copy of the passed data already evaluated.

◆ evaluate()

template<typename T = double>

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

pure virtual

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

Parameters

p	Point to be evaluated.

Returns: Prediction made by the learner.

◆ getCtot()

template<typename T = double>

int mltk::Learner< T >::getCtot ( ) const

inline

Get the total number of updates of the Learner.

Returns: int

◆ getElapsedTime()

template<typename T = double>

double mltk::Learner< T >::getElapsedTime ( ) const

inline

Get the elapsed time in the training phase of the Learner.

Returns: double

◆ getFormulationString()

template<typename T = double>

virtual std::string mltk::Learner< T >::getFormulationString ( )

pure virtual

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

Returns: std::string

Implemented in mltk::regressor::PrimalRegressor< T >, mltk::regressor::PrimalRegressor< double >, mltk::regressor::KNNRegressor< T, Callable >, mltk::regressor::DualRegressor< T >, mltk::ensemble::VotingClassifier< T >, mltk::ensemble::PerceptronCommittee< T >, mltk::ensemble::BaggingClassifier< T >, mltk::ensemble::AutoWeightedVoting< T >, mltk::ensemble::AdaBoostClassifier< T >, mltk::clusterer::KMeans< T, Callable >, mltk::classifier::PrimalClassifier< T >, mltk::classifier::PrimalClassifier< double >, mltk::classifier::BalancedPerceptron< T >, mltk::classifier::OneVsOne< T >, mltk::classifier::OneVsAll< T >, mltk::classifier::DualClassifier< T >, and mltk::classifier::DualClassifier< double >.

◆ getMaxTime()

template<typename T = double>

double mltk::Learner< T >::getMaxTime ( ) const

inline

getMaxTime Returns the maximum running time in the training phase of the Learner.

Returns: double

◆ getPredictionProbability()

template<typename T = double>

double mltk::Learner< T >::getPredictionProbability ( ) const

inline

Get the probability of the last prediction.

Returns: Last prediction probability.

◆ getSamples()

template<typename T = double>

auto mltk::Learner< T >::getSamples ( )

inline

Get the Data used by the learner.

Returns: Data object.

◆ getSteps()

template<typename T = double>

int mltk::Learner< T >::getSteps ( ) const

inline

getSteps Returns the number of steps through the data by the Learner.

Returns: int

◆ getUpdates()

template<typename T = double>

int mltk::Learner< T >::getUpdates ( ) const

inline

getUpdates Returns the number of updates needed to get to the the solution.

Returns: int

◆ setCtot()

template<typename T = double>

void mltk::Learner< T >::setCtot ( int _ctot )

inline

Set the partial number of updates of the Learner.

Parameters

_ctot Number of updates.

◆ setEPS()

template<typename T = double>

void mltk::Learner< T >::setEPS ( double eps )

inline

setEPS Set the precision of the Learner.

Parameters

eps	Precision.

◆ setLearningRate()

template<typename T = double>

void mltk::Learner< T >::setLearningRate ( double learning_rate )

inline

Set the learning rate of the Learner.

Parameters

learning_rate Learning rate.

◆ setMaxEpochs()

template<typename T = double>

void mltk::Learner< T >::setMaxEpochs ( int MAX_EPOCHS )

inline

Set the max number of epochs for the learner training.

Parameters

MAX_EPOCHS Max number of epochs.

◆ setMaxIterations()

template<typename T = double>

void mltk::Learner< T >::setMaxIterations ( int max_it )

inline

setMaxIterations Set the max number of iterations of the Learner.

Parameters

max_it Number max of iterations.

◆ setMaxTime()

template<typename T = double>

void mltk::Learner< T >::setMaxTime ( double maxtime )

inline

Set the max time of execution.

Parameters

maxtime Max time.

◆ setMaxUpdates()

template<typename T = double>

void mltk::Learner< T >::setMaxUpdates ( int max_up )

inline

setMaxIterations Set the max number of updates of the Learner.

Parameters

MAX_IT Number max of updates.

◆ setSamples() [1/2]

template<typename T = double>

virtual void mltk::Learner< T >::setSamples ( const Data< T > & data )

inlinevirtual

setSamples Set the samples used by the Learner.

Parameters

data	Samples to be used.

◆ setSamples() [2/2]

template<typename T = double>

virtual void mltk::Learner< T >::setSamples ( DataPointer< T > data )

inlinevirtual

setSamples Set the samples used by the Learner.

Parameters

data	Samples to be used.

Reimplemented in mltk::ensemble::Ensemble< T >.

◆ setSeed()

template<typename T = double>

void mltk::Learner< T >::setSeed ( const size_t _seed )

inline

Set the seed to be used by the learner.

Parameters

_seed Seed to be used.

◆ setStartTime()

template<typename T = double>

void mltk::Learner< T >::setStartTime ( double stime )

inline

setStartTime Set the initial time of the Learner.

Parameters

stime Initial time.

◆ setSteps()

template<typename T = double>

void mltk::Learner< T >::setSteps ( int _steps )

inline

Set the partial number of steps used in the training phase of the Learner.

Parameters

_steps Number of steps.

◆ setTimer()

template<typename T = double>

void mltk::Learner< T >::setTimer ( Timer _timer )

inline

setTimer Set the timer used by the Learner.

Parameters

_timer Timer to be used.

◆ setVerbose()

template<typename T = double>

void mltk::Learner< T >::setVerbose ( int _verbose )

inline

Set the level of verbose.

Parameters

_verbose level of verbose.

◆ train()

template<typename T = double>

virtual bool mltk::Learner< T >::train ( )

pure virtual

Function that execute the training phase of a Learner.

Returns: void

Member Data Documentation

◆ ctot

template<typename T = double>

int mltk::Learner< T >::ctot = 0

protected

Number of updates of the weights.

◆ EPS

template<typename T = double>

double mltk::Learner< T >::EPS = 0.0000001

protected

Max precision.

◆ MAX_IT

template<typename T = double>

int mltk::Learner< T >::MAX_IT = 1E9

protected

Max number of iterations.

◆ max_time

template<typename T = double>

double mltk::Learner< T >::max_time = 110

protected

Maximum time of training.

◆ MAX_UP

template<typename T = double>

int mltk::Learner< T >::MAX_UP = 1E9

protected

Max number of updates.

◆ MIN_INC

template<typename T = double>

double mltk::Learner< T >::MIN_INC = 1.001

protected

Minimun Increment.

◆ rate

template<typename T = double>

double mltk::Learner< T >::rate = 0.5f

protected

Learning rate.

◆ samples

template<typename T = double>

std::shared_ptr<Data< T > > mltk::Learner< T >::samples

protected

Samples used in the model training.

◆ seed

template<typename T = double>

size_t mltk::Learner< T >::seed = 0

protected

seed for random operations.

◆ start_time

template<typename T = double>

double mltk::Learner< T >::start_time = 0.0f

protected

Initial time.

◆ steps

template<typename T = double>

int mltk::Learner< T >::steps = 0

protected

Number of steps in the data.

◆ timer

template<typename T = double>

Timer mltk::Learner< T >::timer = Timer()

protected

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

◆ verbose

template<typename T = double>

int mltk::Learner< T >::verbose = 1

protected

Verbose level of the output.

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

Learner.hpp

Public Member Functions

Protected Attributes

Member Function Documentation

◆ batchEvaluate()

◆ evaluate()

◆ getCtot()

◆ getElapsedTime()

◆ getFormulationString()

◆ getMaxTime()

◆ getPredictionProbability()

◆ getSamples()

◆ getSteps()

◆ getUpdates()

◆ setCtot()

◆ setEPS()

◆ setLearningRate()

◆ setMaxEpochs()

◆ setMaxIterations()

◆ setMaxTime()

◆ setMaxUpdates()

◆ setSamples() [1/2]

◆ setSamples() [2/2]

◆ setSeed()

◆ setStartTime()

◆ setSteps()

◆ setTimer()

◆ setVerbose()

◆ train()

Member Data Documentation

◆ ctot

◆ EPS

◆ MAX_IT

◆ max_time

◆ MAX_UP

◆ MIN_INC

◆ rate

◆ samples

◆ seed

◆ start_time

◆ steps

◆ timer

◆ verbose