opennn::InputsSelection Class Reference

Abstract base class for algorithms that search the optimal subset of input variables. More...

#include <inputs_selection.h>

Inheritance diagram for opennn::InputsSelection:

Public Types
enum class	StoppingCondition {   MaximumTime , SelectionErrorGoal , MaximumInputs , MaximumEpochs ,   MaximumSelectionFailures }
	Reasons the inputs selection loop may terminate. More...

Public Member Functions
	InputsSelection (TrainingStrategy *=nullptr)
	Constructs the algorithm bound to an optional training strategy.
virtual	~InputsSelection ()=default
const TrainingStrategy *	get_training_strategy () const
bool	has_training_strategy () const
bool	get_display () const
virtual Index	get_minimum_inputs_number () const =0
	Returns the minimum number of input variables that the algorithm may select.
virtual Index	get_maximum_inputs_number () const =0
	Returns the maximum number of input variables that the algorithm may select.
void	set (TrainingStrategy *new_training_strategy)
void	set_trials_number (const Index new_trials_number)
void	set_display (bool new_display)
void	set_validation_error_goal (const float new_validation_error_goal)
void	set_maximum_epochs (const Index new_maximum_epochs)
void	set_maximum_validation_failures (const Index new_maximum_validation_failures)
void	set_maximum_time (const float new_maximum_time)
void	check () const
	Verifies that the training strategy and its dependencies are valid for inputs selection.
virtual InputsSelectionResults	perform_input_selection ()=0
	Runs the inputs selection algorithm until a stopping criterion is met.
string	get_name () const
virtual void	from_JSON (const JsonDocument &)=0
	Loads algorithm configuration from a JSON document.
virtual void	to_JSON (JsonWriter &) const =0
	Writes algorithm configuration to a JSON writer.
void	save (const filesystem::path &) const
	Saves the algorithm configuration to disk.
void	load (const filesystem::path &)
	Loads the algorithm configuration from disk.
virtual void	print () const
	Prints a human-readable description of the algorithm to stdout.

Protected Attributes
TrainingStrategy *	training_strategy = nullptr
Index	trials_number = 1
bool	display = true
float	validation_error_goal
Index	maximum_epochs
Index	maximum_validation_failures = 100
float	maximum_time
string	name

Detailed Description

Abstract base class for algorithms that search the optimal subset of input variables.

Member Enumeration Documentation

StoppingCondition

enum class opennn::InputsSelection::StoppingCondition

strong

Reasons the inputs selection loop may terminate.

Enumerator
MaximumTime
SelectionErrorGoal
MaximumInputs
MaximumEpochs
MaximumSelectionFailures

Constructor & Destructor Documentation

InputsSelection()

opennn::InputsSelection::InputsSelection

(

TrainingStrategy *

= nullptr

)

Constructs the algorithm bound to an optional training strategy.

~InputsSelection()

virtual opennn::InputsSelection::~InputsSelection ( )

virtualdefault

Member Function Documentation

check()

void opennn::InputsSelection::check

(

)

const

Verifies that the training strategy and its dependencies are valid for inputs selection.

from_JSON()

virtual void opennn::InputsSelection::from_JSON ( const JsonDocument & )

pure virtual

Loads algorithm configuration from a JSON document.

Implemented in opennn::GeneticAlgorithm, and opennn::GrowingInputs.

get_display()

bool opennn::InputsSelection::get_display ( ) const

inline

get_maximum_inputs_number()

virtual Index opennn::InputsSelection::get_maximum_inputs_number ( ) const

pure virtual

Returns the maximum number of input variables that the algorithm may select.

Implemented in opennn::GeneticAlgorithm, and opennn::GrowingInputs.

get_minimum_inputs_number()

virtual Index opennn::InputsSelection::get_minimum_inputs_number ( ) const

pure virtual

Returns the minimum number of input variables that the algorithm may select.

Implemented in opennn::GeneticAlgorithm, and opennn::GrowingInputs.

get_name()

string opennn::InputsSelection::get_name ( ) const

inline

get_training_strategy()

const TrainingStrategy * opennn::InputsSelection::get_training_strategy ( ) const

inline

has_training_strategy()

bool opennn::InputsSelection::has_training_strategy ( ) const

inline

load()

void opennn::InputsSelection::load

(

const filesystem::path &

)

Loads the algorithm configuration from disk.

perform_input_selection()

virtual InputsSelectionResults opennn::InputsSelection::perform_input_selection ( )

pure virtual

Runs the inputs selection algorithm until a stopping criterion is met.

Returns

Results including the chosen inputs, optimal parameters and error histories.

Implemented in opennn::GeneticAlgorithm, and opennn::GrowingInputs.

print()

virtual void opennn::InputsSelection::print ( ) const

inlinevirtual

Prints a human-readable description of the algorithm to stdout.

save()

void opennn::InputsSelection::save

(

const filesystem::path &

)

const

Saves the algorithm configuration to disk.

set()

void opennn::InputsSelection::set ( TrainingStrategy * new_training_strategy )

inline

set_display()

void opennn::InputsSelection::set_display ( bool new_display )

inline

set_maximum_epochs()

void opennn::InputsSelection::set_maximum_epochs ( const Index new_maximum_epochs )

inline

set_maximum_time()

void opennn::InputsSelection::set_maximum_time ( const float new_maximum_time )

inline

set_maximum_validation_failures()

void opennn::InputsSelection::set_maximum_validation_failures ( const Index new_maximum_validation_failures )

inline

set_trials_number()

void opennn::InputsSelection::set_trials_number ( const Index new_trials_number )

inline

set_validation_error_goal()

void opennn::InputsSelection::set_validation_error_goal ( const float new_validation_error_goal )

inline

to_JSON()

virtual void opennn::InputsSelection::to_JSON ( JsonWriter & ) const

pure virtual

Writes algorithm configuration to a JSON writer.

Implemented in opennn::GeneticAlgorithm, and opennn::GrowingInputs.

Member Data Documentation

display

bool opennn::InputsSelection::display = true

protected

maximum_epochs

Index opennn::InputsSelection::maximum_epochs

protected

maximum_time

float opennn::InputsSelection::maximum_time

protected

maximum_validation_failures

Index opennn::InputsSelection::maximum_validation_failures = 100

protected

name

string opennn::InputsSelection::name

protected

training_strategy

TrainingStrategy* opennn::InputsSelection::training_strategy = nullptr

protected

trials_number

Index opennn::InputsSelection::trials_number = 1

protected

validation_error_goal

float opennn::InputsSelection::validation_error_goal

protected