#include <sum_squared_error.h>

Inheritance diagram for OpenNN::SumSquaredError:

Public Member Functions
	SumSquaredError (void)

	SumSquaredError (NeuralNetwork *)

	SumSquaredError (DataSet *)

	SumSquaredError (NeuralNetwork , DataSet )

	SumSquaredError (const tinyxml2::XMLDocument &)

	SumSquaredError (const SumSquaredError &)

virtual	~SumSquaredError (void)

void	check (void) const

double	calculate_performance (void) const

double	calculate_generalization_performance (void) const

Vector< double >	calculate_gradient (void) const

Matrix< double >	calculate_Hessian (void) const

double	calculate_performance (const Vector< double > &) const

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

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

Vector< double >	calculate_terms (void) const

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

Matrix< double >	calculate_terms_Jacobian (void) const

PerformanceTerm::FirstOrderTerms	calculate_first_order_terms (void) const

Vector< double >	calculate_squared_errors (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 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 sum squared peformance term functional. This is used as the performance term in data modeling problems, such as function regression, pattern recognition or time series prediction.

Definition at line 42 of file sum_squared_error.h.

Constructor & Destructor Documentation

OpenNN::SumSquaredError::SumSquaredError ( void )

explicit

Default constructor. It creates a sum 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 28 of file sum_squared_error.cpp.

OpenNN::SumSquaredError::SumSquaredError ( NeuralNetwork * new_neural_network_pointer )

explicit

Neural network constructor. It creates a sum squared error performance term associated to a neural network but not measured on any 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.

Definition at line 40 of file sum_squared_error.cpp.

OpenNN::SumSquaredError::SumSquaredError ( DataSet * new_data_set_pointer )

explicit

Data set constructor. It creates a sum squared error not associated to any neural network but to be measured on a 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 53 of file sum_squared_error.cpp.

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

explicit

Neural network and data set constructor. It creates a sum squared error 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 67 of file sum_squared_error.cpp.

OpenNN::SumSquaredError::SumSquaredError ( const tinyxml2::XMLDocument & sum_squared_error_document )

explicit

XML constructor. It creates a sum squared error not associated to any neural network and not measured on any data set. It also sets all the rest of class members from a TinyXML document.

Parameters

sum_squared_error_document XML document with the class members.

Definition at line 80 of file sum_squared_error.cpp.

OpenNN::SumSquaredError::SumSquaredError ( const SumSquaredError & new_sum_squared_error )

Copy constructor. It creates a sum squared error not associated to any neural network and not measured on any data set. It also sets all the rest of class members from another sum squared error object.

Parameters

new_sum_squared_error Object to be copied.

Definition at line 94 of file sum_squared_error.cpp.

Member Function Documentation

PerformanceTerm::FirstOrderTerms OpenNN::SumSquaredError::calculate_first_order_terms ( void ) const

virtual

Returns the first order performance of the terms performance function. This is a structure containing the performance terms vector and the performance terms Jacobian.

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 964 of file sum_squared_error.cpp.

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

virtual

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

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 447 of file sum_squared_error.cpp.

Vector< double > OpenNN::SumSquaredError::calculate_gradient ( const Vector< double > & ) const

virtual

Todo:

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 1059 of file sum_squared_error.cpp.

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

virtual

Calculates the Hessian by means of the back-propagation algorithm, and returns it in a single symmetric matrix of size the number of neural network parameters.

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 568 of file sum_squared_error.cpp.

Matrix< double > OpenNN::SumSquaredError::calculate_Hessian ( const Vector< double > & ) const

virtual

Todo:

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 1071 of file sum_squared_error.cpp.

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

virtual

Returns which would be the sum squard error performance 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 268 of file sum_squared_error.cpp.

Vector< double > OpenNN::SumSquaredError::calculate_terms ( const Vector< double > & parameters ) const

virtual

Returns the performance terms vector for a hypotetical vector of parameters.

Parameters

parameters Neural network parameters for which the performance terms vector is to be computed.

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 773 of file sum_squared_error.cpp.

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

virtual

Returns the terms_Jacobian matrix of the sum squared error function, whose elements are given by the derivatives of the squared errors data set with respect to the multilayer perceptron parameters. The terms_Jacobian matrix here is computed using a back-propagation algorithm.

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 821 of file sum_squared_error.cpp.

void OpenNN::SumSquaredError::check ( void ) const

virtual

Checks that there are a neural network and a data set associated to the sum 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 sum_squared_error.cpp.

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

virtual

Loads a sum squared error object from a XML document.

Parameters

document TinyXML document containing the members of the object.

Reimplemented from OpenNN::PerformanceTerm.

Definition at line 1127 of file sum_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