14 #ifndef __SCALINGLAYER_H__
15 #define __SCALINGLAYER_H__
33 #include "../tinyxml2/tinyxml2.h"
109 void set(
const size_t&);
111 void set(
const tinyxml2::XMLDocument&);
123 void set_mean(
const size_t&,
const double&);
178 tinyxml2::XMLDocument*
to_XML(
void)
const;
179 virtual void from_XML(
const tinyxml2::XMLDocument&);
Vector< double > calculate_outputs(const Vector< double > &) const
void prune_scaling_neuron(const size_t &)
void set_item_statistics(const size_t &, const Statistics< double > &)
std::string write_scaling_method(void) const
Returns a string with the name of the method used for scaling.
size_t get_scaling_neurons_number(void) const
Returns the number of unscaling neurons in this layer.
Vector< double > calculate_mean_standard_deviation_derivatives(const Vector< double > &) const
const ScalingMethod & get_scaling_method(void) const
Returns the method used for scaling.
std::string write_scaling_method_text(void) const
Vector< Statistics< double > > statistics
Statistics of input variables.
void check_range(const Vector< double > &) const
ScalingMethod scaling_method
Method for scaling the input variables.
virtual void from_XML(const tinyxml2::XMLDocument &)
Vector< Statistics< double > > get_statistics(void) const
void set(void)
Sets the scaling layer to be empty.
Vector< double > calculate_minimum_maximum_derivatives(const Vector< double > &) const
void set_mean(const size_t &, const double &)
void set_statistics(const Vector< Statistics< double > > &)
bool operator==(const ScalingLayer &) const
void set_display(const bool &)
ScalingMethod
Enumeration of available methods for scaling the input variables.
void set_scaling_method(const ScalingMethod &)
Vector< double > arrange_means(void) const
Returns a single matrix with the means of all scaling neurons.
Vector< double > calculate_minimum_maximum_outputs(const Vector< double > &) const
Vector< double > calculate_minimum_maximum_second_derivatives(const Vector< double > &) const
tinyxml2::XMLDocument * to_XML(void) const
const bool & get_display(void) const
void set_maximum(const size_t &, const double &)
std::string write_mean_standard_deviation_expression(const Vector< std::string > &, const Vector< std::string > &) const
Vector< double > arrange_standard_deviations(void) const
Returns a single matrix with the standard deviations of all scaling neurons.
virtual void set_default(void)
Vector< Matrix< double > > arrange_Hessian_form(const Vector< double > &) const
Arranges a "Hessian form" vector of matrices from the vector of second derivatives.
bool display
Display warning messages to screen.
std::string to_string(void) const
Returns a string representation of the current scaling layer object.
virtual ~ScalingLayer(void)
Destructor.
void initialize_random(void)
Matrix< double > arrange_Jacobian(const Vector< double > &) const
Arranges a "Jacobian" matrix from the vector of derivatives.
Vector< double > calculate_derivatives(const Vector< double > &) const
std::string write_no_scaling_expression(const Vector< std::string > &, const Vector< std::string > &) const
Vector< double > calculate_mean_standard_deviation_outputs(const Vector< double > &) const
std::string write_expression(const Vector< std::string > &, const Vector< std::string > &) const
Returns a string with the expression of the inputs scaling process.
Matrix< double > arrange_statistics(void) const
std::string write_minimum_maximum_expression(const Vector< std::string > &, const Vector< std::string > &) const
Vector< double > calculate_second_derivatives(const Vector< double > &) const
Vector< double > calculate_mean_standard_deviation_second_derivatives(const Vector< double > &) const
void set_standard_deviation(const size_t &, const double &)
void set_minimum(const size_t &, const double &)
ScalingLayer & operator=(const ScalingLayer &)
bool is_empty(void) const
Returns true if the number of scaling neurons is zero, and false otherwise.