#include <mean_squared_error.h>

Inheritance diagram for OpenNN::MeanSquaredError:

Public Member Functions
	MeanSquaredError (void)

	MeanSquaredError (NeuralNetwork *)

	MeanSquaredError (DataSet *)

	MeanSquaredError (NeuralNetwork , DataSet )

	MeanSquaredError (const tinyxml2::XMLDocument &)

	MeanSquaredError (const MeanSquaredError &)

virtual	~MeanSquaredError (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

FirstOrderPerformance	calculate_first_order_performance (void) const

SecondOrderPerformance	calculate_second_order_performance (void) const

Vector< double >	calculate_terms (void) const

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

Matrix< double >	calculate_terms_Jacobian (void) const

FirstOrderTerms	calculate_first_order_terms (void) const

std::string	write_performance_term_type (void) const

tinyxml2::XMLDocument *	to_XML (void) const

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 std::string	write_information (void) const

virtual std::string	to_string (void) const

virtual void	from_XML (const tinyxml2::XMLDocument &)

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 mean squared error performance term. The 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, such as function regression, pattern recognition and time series prediction.

Definition at line 43 of file mean_squared_error.h.

Constructor & Destructor Documentation

OpenNN::MeanSquaredError::MeanSquaredError ( void )

explicit

Default constructor. It creates a mean squared error performance term 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 27 of file mean_squared_error.cpp.

OpenNN::MeanSquaredError::MeanSquaredError ( NeuralNetwork * new_neural_network_pointer )

explicit

Neural network constructor. It creates a mean squared error performance term object associated to a neural network object but not measured on any 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 40 of file mean_squared_error.cpp.

OpenNN::MeanSquaredError::MeanSquaredError ( DataSet * new_data_set_pointer )

explicit

Data set constructor. It creates a mean squared error performance term not associated to any neural network but to be measured on a given data set 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 mean_squared_error.cpp.

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

explicit

Neural network and data set constructor. It creates a 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 mean_squared_error.cpp.

OpenNN::MeanSquaredError::MeanSquaredError ( const tinyxml2::XMLDocument & mean_squared_error_document )

explicit

XML constructor. It creates a mean squared error object with all pointers set to NULL. The object members are loaded by means of a XML document-> Please be careful with the format of that file, which is specified in the OpenNN manual.

Parameters

mean_squared_error_document TinyXML document with the mean squared error elements.

Definition at line 83 of file mean_squared_error.cpp.

OpenNN::MeanSquaredError::MeanSquaredError ( const MeanSquaredError & other_mean_squared_error )

Copy constructor. It creates a copy of an existing mean squared error object.

Parameters

other_mean_squared_error Mean squared error object to be copied.

Definition at line 95 of file mean_squared_error.cpp.

Member Function Documentation

PerformanceTerm::FirstOrderPerformance OpenNN::MeanSquaredError::calculate_first_order_performance ( void ) const

Todo:

Definition at line 595 of file mean_squared_error.cpp.

MeanSquaredError::FirstOrderTerms OpenNN::MeanSquaredError::calculate_first_order_terms ( void ) const

virtual

Returns a first order terms performance structure, which contains the values and the Jacobian of the performance terms function.

Todo:

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 911 of file mean_squared_error.cpp.

double OpenNN::MeanSquaredError::calculate_generalization_performance ( void ) const

virtual

Returns the mean squared error of the multilayer perceptron measured on the generalization instances of the data set.

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 389 of file mean_squared_error.cpp.

Vector< double > OpenNN::MeanSquaredError::calculate_gradient ( void ) const

virtual

Calculates the performance gradient by means of the back-propagation algorithm, and returns it in a single vector of size the number of multilayer perceptron parameters.

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 470 of file mean_squared_error.cpp.

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

virtual

Todo:

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 583 of file mean_squared_error.cpp.

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

virtual

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

Parameters

parameters Vector of potential parameters for the neural network associated to the performance term.

Implements OpenNN::PerformanceTerm.

Definition at line 289 of file mean_squared_error.cpp.

PerformanceTerm::SecondOrderPerformance OpenNN::MeanSquaredError::calculate_second_order_performance ( void ) const

Todo:

Definition at line 618 of file mean_squared_error.cpp.

Vector< double > OpenNN::MeanSquaredError::calculate_terms ( void ) const

virtual

Returns performance vector of the performance terms function for the mean squared error. It uses the error back-propagation method.

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 643 of file mean_squared_error.cpp.

Vector< double > OpenNN::MeanSquaredError::calculate_terms ( const Vector< double > & network_parameters ) const

virtual

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

Parameters

network_parameters Vector of a potential multilayer_perceptron_pointer parameters for the multilayer perceptron associated to the performance functional.

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 725 of file mean_squared_error.cpp.

Matrix< double > OpenNN::MeanSquaredError::calculate_terms_Jacobian ( void ) const

virtual

Returns the Jacobian matrix of the mean squared error function, whose elements are given by the derivatives of the squared errors data set with respect to the multilayer perceptron parameters.

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 771 of file mean_squared_error.cpp.

void OpenNN::MeanSquaredError::check ( void ) const

virtual

Checks that there are a neural network and a data set associated to the 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 118 of file mean_squared_error.cpp.

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

virtual

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

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 946 of file 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