Index

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S
	save, neural_net
	save_to_fixed, neural_net
	save_train, training_data
	save_train_to_fixed, training_data
	scale_input, neural_net
	scale_input_train_data, training_data
	scale_output, neural_net
	scale_output_train_data, training_data
	scale_train, neural_net
	scale_train_data, training_data
	set_activation_function, neural_net
	set_activation_function_hidden, neural_net
	set_activation_function_layer, neural_net
	set_activation_function_output, neural_net
	set_activation_steepness, neural_net
	set_activation_steepness_hidden, neural_net
	set_activation_steepness_layer, neural_net
	set_activation_steepness_output, neural_net
	set_bit_fail_limit, neural_net
	set_callback, neural_net
	set_cascade_activation_functions, neural_net
	set_cascade_activation_steepnesses, neural_net
	set_cascade_candidate_change_fraction, neural_net
	set_cascade_candidate_limit, neural_net
	set_cascade_candidate_stagnation_epochs, neural_net
	set_cascade_max_cand_epochs, neural_net
	set_cascade_max_out_epochs, neural_net
	set_cascade_num_candidate_groups, neural_net
	set_cascade_output_change_fraction, neural_net
	set_cascade_output_stagnation_epochs, neural_net
	set_cascade_weight_multiplier, neural_net
	set_error_log, neural_net
	set_input_scaling_params, neural_net
	set_learning_momentum, neural_net
	set_learning_rate, neural_net
	set_output_scaling_params, neural_net
	set_quickprop_decay, neural_net
	set_quickprop_mu, neural_net
	set_rprop_decrease_factor, neural_net
	set_rprop_delta_max, neural_net
	set_rprop_delta_min, neural_net
	set_rprop_delta_zero, neural_net
	set_rprop_increase_factor, neural_net
	set_sarprop_step_error_shift, neural_net
	set_sarprop_step_error_threshold_factor, neural_net
	set_sarprop_temperature, neural_net
	set_sarprop_weight_decay_shift, neural_net
	set_scaling_params, neural_net
	set_train_data, training_data
	set_train_error_function, neural_net
	set_train_stop_function, neural_net
	set_training_algorithm, neural_net
	set_weight, neural_net
	set_weight_array, neural_net
	SHORTCUT, FANN
	shuffle_train_data, training_data
	stop_function_enum, FANN
	STOPFUNC_BIT, FANN
	STOPFUNC_MSE, FANN
	struct fann
	struct fann_error
	struct fann_train_data
	subset_train_data, training_data

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.

Each layer has connections to all following layers

void shuffle_train_data()

Shuffles training data, randomizing the order.

Stop criteria used during training.

Stop criteria is number of bits that fail.

Stop criteria is Mean Square Error (MSE) value.

struct fann

The fast artificial neural network(fann) structure.

struct fann_error

Structure used to store error-related information, both struct fann and struct fann_train_data can be casted to this type.

struct fann_train_data

Structure used to store data, for use with training.

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.