#include <root_mean_squared_error.h>

Inheritance diagram for OpenNN::RootMeanSquaredError:

Public Member Functions
	RootMeanSquaredError (void)

	RootMeanSquaredError (NeuralNetwork *)

	RootMeanSquaredError (DataSet *)

	RootMeanSquaredError (NeuralNetwork , DataSet )

	RootMeanSquaredError (const tinyxml2::XMLDocument &)

virtual	~RootMeanSquaredError (void)

void	check (void) const

double	calculate_performance (void) const

double	calculate_performance (const Vector< double > &) const

double	calculate_generalization_performance (void) const

Vector< double >	calculate_gradient (void) const

Matrix< double >	calculate_Hessian (void) const

std::string	write_performance_term_type (void) const

tinyxml2::XMLDocument *	to_XML (void) const

void	from_XML (const tinyxml2::XMLDocument &)

Public Member Functions inherited from OpenNN::PerformanceTerm
	PerformanceTerm (void)

	PerformanceTerm (NeuralNetwork *)

	PerformanceTerm (DataSet *)

	PerformanceTerm (MathematicalModel *)

	PerformanceTerm (NeuralNetwork , DataSet )

	PerformanceTerm (NeuralNetwork , MathematicalModel )

	PerformanceTerm (NeuralNetwork , MathematicalModel , DataSet *)

	PerformanceTerm (const tinyxml2::XMLDocument &)

	PerformanceTerm (const PerformanceTerm &)

virtual	~PerformanceTerm (void)

virtual PerformanceTerm &	operator= (const PerformanceTerm &)

virtual bool	operator== (const PerformanceTerm &) const

NeuralNetwork *	get_neural_network_pointer (void) const

MathematicalModel *	get_mathemtaical_model_pointer (void) const

DataSet *	get_data_set_pointer (void) const

NumericalDifferentiation *	get_numerical_differentiation_pointer (void) const

const bool &	get_display (void) const

bool	has_neural_network (void) const

bool	has_mathematical_model (void) const

bool	has_data_set (void) const

bool	has_numerical_differentiation (void) const

virtual void	set (void)

virtual void	set (NeuralNetwork *)

virtual void	set (DataSet *)

virtual void	set (MathematicalModel *)

virtual void	set (NeuralNetwork , DataSet )

virtual void	set (NeuralNetwork , MathematicalModel )

virtual void	set (NeuralNetwork , MathematicalModel , DataSet *)

void	set (const PerformanceTerm &)

virtual void	set_neural_network_pointer (NeuralNetwork *)

virtual void	set_mathematical_model_pointer (MathematicalModel *)

virtual void	set_data_set_pointer (DataSet *)

void	set_numerical_differentiation_pointer (NumericalDifferentiation *)

virtual void	set_default (void)

void	set_display (const bool &)

void	construct_numerical_differentiation (void)

void	delete_numerical_differentiation_pointer (void)

Vector< Vector< double > >	calculate_layers_delta (const Vector< Vector< double > > &, const Vector< double > &) const

Vector< Vector< double > >	calculate_layers_delta (const Vector< Vector< double > > &, const Vector< double > &, const Vector< double > &) const

Matrix< Matrix< double > >	calculate_interlayers_Delta (const Vector< Vector< double > > &, const Vector< Vector< double > > &, const Matrix< Matrix< double > > &, const Vector< double > &, const Matrix< double > &, const Vector< Vector< double > > &) const

Vector< double >	calculate_point_gradient (const Vector< double > &, const Vector< Vector< double > > &, const Vector< Vector< double > > &) const

Vector< double >	calculate_point_gradient (const Vector< Matrix< double > > &, const Vector< Vector< double > > &) const

Matrix< double >	calculate_point_Hessian (const Vector< Vector< double > > &, const Vector< Vector< Vector< double > > > &, const Matrix< Matrix< double > > &, const Vector< Vector< double > > &, const Matrix< Matrix< double > > &) const

virtual Vector< double >	calculate_gradient (const Vector< double > &) const

virtual Matrix< double >	calculate_Hessian (const Vector< double > &) const

virtual Vector< double >	calculate_terms (void) const

virtual Vector< double >	calculate_terms (const Vector< double > &) const

virtual Matrix< double >	calculate_terms_Jacobian (void) const

virtual PerformanceTerm::FirstOrderTerms	calculate_first_order_terms (void) const

virtual std::string	write_information (void) const

virtual std::string	to_string (void) const

size_t	calculate_Kronecker_delta (const size_t &, const size_t &) const

Additional Inherited Members
Protected Attributes inherited from OpenNN::PerformanceTerm
NeuralNetwork *	neural_network_pointer

DataSet *	data_set_pointer

MathematicalModel *	mathematical_model_pointer

NumericalDifferentiation *	numerical_differentiation_pointer

bool	display

Detailed Description

This class represents the root mean squared error performance term. The root mean squared error measures the difference between the outputs from a neural network and the targets in a data set. This functional is used in data modeling problems.

Definition at line 42 of file root_mean_squared_error.h.

Constructor & Destructor Documentation

OpenNN::RootMeanSquaredError::RootMeanSquaredError ( void )

explicit

Default constructor. It creates a root mean squared error performance term object not associated to any neural network and not measured on any data set. It also initializes all the rest of class members to their default values.

Definition at line 29 of file root_mean_squared_error.cpp.

OpenNN::RootMeanSquaredError::RootMeanSquaredError ( NeuralNetwork * new_neural_network_pointer )

explicit

Neural network constructor. It creates a root mean squared error associated to a neural network object but not to a data set object. It also initializes all the rest of class members to their default values.

Parameters

new_neural_network_pointer Pointer to a neural network object.

Definition at line 41 of file root_mean_squared_error.cpp.

OpenNN::RootMeanSquaredError::RootMeanSquaredError ( DataSet * new_data_set_pointer )

explicit

Data set constructor. It creates a root mean squared error associated to a data set object but not to a neural network object. It also initializes all the rest of class members to their default values.

Parameters

new_data_set_pointer Pointer to a data set object.

Definition at line 54 of file root_mean_squared_error.cpp.

OpenNN::RootMeanSquaredError::RootMeanSquaredError	(	NeuralNetwork *	new_neural_network_pointer,
		DataSet *	new_data_set_pointer
	)

explicit

Neural network and data set constructor. It creates a root mean squared error performance term object associated to a neural network and measured on a data set. It also initializes all the rest of class members to their default values.

Parameters

new_neural_network_pointer	Pointer to a neural network object.
new_data_set_pointer	Pointer to a data set object.

Definition at line 69 of file root_mean_squared_error.cpp.

OpenNN::RootMeanSquaredError::RootMeanSquaredError ( const tinyxml2::XMLDocument & root_mean_squared_error_document )

explicit

XML constructor. This constructor creates a root mean squared object neither associated to a neural network nor a data set. It also loads the member data from a XML document.

Parameters

root_mean_squared_error_document TinyXML document with the object members.

Definition at line 82 of file root_mean_squared_error.cpp.

Member Function Documentation

Matrix< double > OpenNN::RootMeanSquaredError::calculate_Hessian ( void ) const

virtual

Todo:

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 579 of file root_mean_squared_error.cpp.

double OpenNN::RootMeanSquaredError::calculate_performance ( void ) const

virtual

Returns the performance value of a neural network according to the root mean squared error on the training instances of a data set.

Implements OpenNN::PerformanceTerm.

Definition at line 195 of file root_mean_squared_error.cpp.

double OpenNN::RootMeanSquaredError::calculate_performance ( const Vector< double > & parameters ) const

virtual

Returns which would be the performance of a multilayer perceptron for an hypothetical vector of parameters. It does not set that vector of parameters to the multilayer perceptron.

Parameters

parameters Vector of potential parameters for the multilayer perceptron associated to the performance term.

Implements OpenNN::PerformanceTerm.

Definition at line 278 of file root_mean_squared_error.cpp.

void OpenNN::RootMeanSquaredError::check ( void ) const

virtual

Checks that there are a neural network and a data set associated to the root mean squared error, and that the numbers of inputs and outputs in the neural network are equal to the numbers of inputs and targets in the data set. If some of the above conditions is not hold, the method throws an exception.

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 105 of file root_mean_squared_error.cpp.

void OpenNN::RootMeanSquaredError::from_XML ( const tinyxml2::XMLDocument & document )

virtual

Loads a root mean squared error object from a XML document.

Parameters

document TinyXML document containing the members of the object.

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 635 of file root_mean_squared_error.cpp.

tinyxml2::XMLDocument * OpenNN::RootMeanSquaredError::to_XML ( void ) const

virtual

Serializes the root mean squared error object into a XML document of the TinyXML library. See the OpenNN manual for more information about the format of this element.

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 602 of file root_mean_squared_error.cpp.

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

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation