Index - FANN

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
	shuffle_train_data, training_data
	stop_function_enum
	STOPFUNC_BIT
	STOPFUNC_MSE
	string&configuration_file), neural_net::neural_net(const std
	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.

neural_net( const std:: string & configuration_file )

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

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.