bool save( const std:: string  & configuration_file )

Save the entire network to a configuration file.

int save_to_fixed( const std:: string  & configuration_file )

Saves the entire network to a configuration file.

bool save_train( const std:: string  & filename )

Save the training structure to a file, with the format as specified in read_train_from_file

bool save_train_to_fixed( const std:: string  & filename, unsigned  int  decimal_point )

Saves the training structure to a fixed point data file.

void scale_input( fann_type  * input_vector )

Scale data in input vector before feed it to ann based on previously calculated parameters.

void scale_input_train_data( fann_type  new_min, fann_type  new_max )

Scales the inputs in the training data to the specified range.

void scale_output( fann_type  * output_vector )

Scale data in output vector before feed it to ann based on previously calculated parameters.

void scale_output_train_data( fann_type  new_min, fann_type  new_max )

Scales the outputs in the training data to the specified range.

void scale_train( training_data  & data )

Scale input and output data based on previously calculated parameters.

void scale_train_data( fann_type  new_min, fann_type  new_max )

Scales the inputs and outputs in the training data to the specified range.

void set_activation_function( activation_function_enum  activation_function, int  layer, int  neuron )

Set the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.

void set_activation_function_hidden(     activation_function_enum  activation_function )

Set the activation function for all of the hidden layers.

void set_activation_function_layer(     activation_function_enum  activation_function,     int  layer )

Set the activation function for all the neurons in the layer number layer, counting the input layer as layer 0.

void set_activation_function_output(     activation_function_enum  activation_function )

Set the activation function for the output layer.

void set_activation_steepness( fann_type  steepness, int  layer, int  neuron )

Set the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.

void set_activation_steepness_hidden( fann_type  steepness )

Set the steepness of the activation steepness in all of the hidden layers.

void set_activation_steepness_layer( fann_type  steepness, int  layer )

Set the activation steepness all of the neurons in layer number layer, counting the input layer as layer 0.

void set_activation_steepness_output( fann_type  steepness )

Set the steepness of the activation steepness in the output layer.

void set_bit_fail_limit( fann_type  bit_fail_limit )

Set the bit fail limit used during training.

void set_callback( callback_type  callback, void  * user_data )

Sets the callback function for use during training.

void set_cascade_activation_functions(     activation_function_enum  * cascade_activation_functions,     unsigned  int  cascade_activation_functions_count )

Sets the array of cascade candidate activation functions.

void set_cascade_activation_steepnesses(     fann_type  * cascade_activation_steepnesses,     unsigned  int  cascade_activation_steepnesses_count )

Sets the array of cascade candidate activation steepnesses.

void set_cascade_candidate_change_fraction(     float  cascade_candidate_change_fraction )

Sets the cascade candidate change fraction.

void set_cascade_candidate_limit( fann_type  cascade_candidate_limit )

Sets the candidate limit.

void set_cascade_candidate_stagnation_epochs(     unsigned  int  cascade_candidate_stagnation_epochs )

Sets the number of cascade candidate stagnation epochs.

void set_cascade_max_cand_epochs( unsigned  int  cascade_max_cand_epochs )

Sets the max candidate epochs.

void set_cascade_max_out_epochs( unsigned  int  cascade_max_out_epochs )

Sets the maximum out epochs.

void set_cascade_num_candidate_groups(     unsigned  int  cascade_num_candidate_groups )

Sets the number of candidate groups.

void set_cascade_output_change_fraction( float  cascade_output_change_fraction )

Sets the cascade output change fraction.

void set_cascade_output_stagnation_epochs(     unsigned  int  cascade_output_stagnation_epochs )

Sets the number of cascade output stagnation epochs.

void set_cascade_weight_multiplier( fann_type  cascade_weight_multiplier )

Sets the weight multiplier.

void set_error_log( FILE  * log_file )

Change where errors are logged to.

bool set_input_scaling_params( const  training_data  & data, float  new_input_min, float  new_input_max )

Calculate scaling parameters for future use based on training data.

void set_learning_momentum( float  learning_momentum )

Set the learning momentum.

void set_learning_rate( float  learning_rate )

Set the learning rate.

bool set_output_scaling_params( const  training_data  & data, float  new_output_min, float  new_output_max )

Calculate scaling parameters for future use based on training data.

void set_quickprop_decay( float  quickprop_decay )

Sets the quickprop decay factor.

void set_quickprop_mu( float  quickprop_mu )

Sets the quickprop mu factor.

void set_rprop_decrease_factor( float  rprop_decrease_factor )

The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training.

void set_rprop_delta_max( float  rprop_delta_max )

The maximum step-size is a positive number determining how large the maximum step-size may be.

void set_rprop_delta_min( float  rprop_delta_min )

The minimum step-size is a small positive number determining how small the minimum step-size may be.

void set_rprop_delta_zero( float  rprop_delta_zero )

The initial step-size is a small positive number determining how small the initial step-size may be.

void set_rprop_increase_factor( float  rprop_increase_factor )

The increase factor used during RPROP training.

void set_sarprop_step_error_shift( float  sarprop_step_error_shift )

Set the sarprop step error shift.

void set_sarprop_step_error_threshold_factor(     float  sarprop_step_error_threshold_factor )

Set the sarprop step error threshold factor.

void set_sarprop_temperature( float  sarprop_temperature )

Set the sarprop_temperature.

void set_sarprop_weight_decay_shift( float  sarprop_weight_decay_shift )

Set the sarprop weight decay shift.

bool set_scaling_params( const  training_data  & data, float  new_input_min, float  new_input_max, float  new_output_min, float  new_output_max )

Calculate scaling parameters for future use based on training data.

void set_train_data( unsigned  int  num_data, unsigned  int  num_input, fann_type  ** input, unsigned  int  num_output, fann_type  ** output )

Set the training data to the input and output data provided.

void set_train_error_function( error_function_enum  train_error_function )

Set the error function used during training.

void set_train_stop_function( stop_function_enum  train_stop_function )

Set the stop function used during training.

void set_training_algorithm( training_algorithm_enum  training_algorithm )

Set the training algorithm.

void set_weight( unsigned  int  from_neuron, unsigned  int  to_neuron, fann_type  weight )

Set a connection in the network.

void set_weight_array( connection  * connections, unsigned  int  num_connections )

Set connections in the network.

void shuffle_train_data()

Shuffles training data, randomizing the order.

neural_net( const std:: string  & configuration_file )

Constructs a backpropagation neural network from a configuration file, which have been saved by save.

void subset_train_data( unsigned  int  pos, unsigned  int  length )

Changes the training data to a subset, starting at position pos and length elements forward.