Creates a standard fully connected backpropagation neural network.
FANN_EXTERNAL struct fann *FANN_API fann_create_standard( unsigned int num_layers, ... )
Just like fann_create_standard, but with an array of layer sizes instead of individual parameters.
FANN_EXTERNAL struct fann *FANN_API fann_create_standard_array( unsigned int num_layers, const unsigned int * layers )
Creates a standard backpropagation neural network, which is not fully connected.
FANN_EXTERNAL struct fann *FANN_API fann_create_sparse( float connection_rate, unsigned int num_layers, ... )
Just like fann_create_sparse, but with an array of layer sizes instead of individual parameters.
FANN_EXTERNAL struct fann *FANN_API fann_create_sparse_array( float connection_rate, unsigned int num_layers, const unsigned int * layers )
Creates a standard backpropagation neural network, which is not fully connected and which also has shortcut connections.
FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut( unsigned int num_layers, ... )
Just like fann_create_shortcut, but with an array of layer sizes instead of individual parameters.
FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut_array( unsigned int num_layers, const unsigned int * layers )
Destroys the entire network and properly freeing all the associated memmory.
FANN_EXTERNAL void FANN_API fann_destroy( struct fann * ann )
Creates a copy of a fann structure.
FANN_EXTERNAL struct fann * FANN_API fann_copy( struct fann * ann )
Will run input through the neural network, returning an array of outputs, the number of which being equal to the number of neurons in the output layer.
FANN_EXTERNAL fann_type * FANN_API fann_run( struct fann * ann, fann_type * input )
Give each connection a random weight between min_weight and max_weight
FANN_EXTERNAL void FANN_API fann_randomize_weights( struct fann * ann, fann_type min_weight, fann_type max_weight )
Initialize the weights using Widrow + Nguyen’s algorithm.
FANN_EXTERNAL void FANN_API fann_init_weights( struct fann * ann, struct fann_train_data * train_data )
Will print the connections of the ann in a compact matrix, for easy viewing of the internals of the ann.
FANN_EXTERNAL void FANN_API fann_print_connections( struct fann * ann )
Prints all of the parameters and options of the ANN
FANN_EXTERNAL void FANN_API fann_print_parameters( struct fann * ann )
Get the number of input neurons.
FANN_EXTERNAL unsigned int FANN_API fann_get_num_input( struct fann * ann )
Get the number of output neurons.
FANN_EXTERNAL unsigned int FANN_API fann_get_num_output( struct fann * ann )
Get the total number of neurons in the entire network.
FANN_EXTERNAL unsigned int FANN_API fann_get_total_neurons( struct fann * ann )
Get the total number of connections in the entire network.
FANN_EXTERNAL unsigned int FANN_API fann_get_total_connections( struct fann * ann )
Get the type of neural network it was created as.
FANN_EXTERNAL enum fann_nettype_enum FANN_API fann_get_network_type( struct fann * ann )
Get the connection rate used when the network was created
FANN_EXTERNAL float FANN_API fann_get_connection_rate( struct fann * ann )
Get the number of layers in the network
FANN_EXTERNAL unsigned int FANN_API fann_get_num_layers( struct fann * ann )
Get the number of neurons in each layer in the network.
FANN_EXTERNAL void FANN_API fann_get_layer_array( struct fann * ann, unsigned int * layers )
Get the number of bias in each layer in the network.
FANN_EXTERNAL void FANN_API fann_get_bias_array( struct fann * ann, unsigned int * bias )
Get the connections in the network.
FANN_EXTERNAL void FANN_API fann_get_connection_array( struct fann * ann, struct fann_connection * connections )
Set connections in the network.
FANN_EXTERNAL void FANN_API fann_set_weight_array( struct fann * ann, struct fann_connection * connections, unsigned int num_connections )
Set a connection in the network.
FANN_EXTERNAL void FANN_API fann_set_weight( struct fann * ann, unsigned int from_neuron, unsigned int to_neuron, fann_type weight )
Store a pointer to user defined data.
FANN_EXTERNAL void FANN_API fann_set_user_data( struct fann * ann, void * user_data )
Get a pointer to user defined data that was previously set with fann_set_user_data.
FANN_EXTERNAL void * FANN_API fann_get_user_data( struct fann * ann )
Returns the position of the decimal point in the ann.
FANN_EXTERNAL unsigned int FANN_API fann_get_decimal_point( struct fann * ann )
returns the multiplier that fix point data is multiplied with.
FANN_EXTERNAL unsigned int FANN_API fann_get_multiplier( struct fann * ann )
Does the same as fann_train_on_data, but reads the training data directly from a file.
FANN_EXTERNAL void FANN_API fann_train_on_file( struct fann * ann, const char * filename, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error )
The fast artificial neural network(fann) structure.
struct fann
Test with a set of inputs, and a set of desired outputs.
FANN_EXTERNAL fann_type * FANN_API fann_test( struct fann * ann, fann_type * input, fann_type * desired_output )
Reads a file that stores training data.
FANN_EXTERNAL struct fann_train_data *FANN_API fann_read_train_from_file( const char * filename )
Saves the entire network to a configuration file.
FANN_EXTERNAL int FANN_API fann_save_to_fixed( struct fann * ann, const char * configuration_file )
Saves the training structure to a fixed point data file.
FANN_EXTERNAL int FANN_API fann_save_train_to_fixed( struct fann_train_data * data, const char * filename, unsigned int decimal_point )
Trains on an entire dataset, for a period of time using the Cascade2 training algorithm.
FANN_EXTERNAL void FANN_API fann_cascadetrain_on_data( struct fann * ann, struct fann_train_data * data, unsigned int max_neurons, unsigned int neurons_between_reports, float desired_error )
Does the same as fann_cascadetrain_on_data, but reads the training data directly from a file.
FANN_EXTERNAL void FANN_API fann_cascadetrain_on_file( struct fann * ann, const char * filename, unsigned int max_neurons, unsigned int neurons_between_reports, float desired_error )
The cascade output change fraction is a number between 0 and 1 determining how large a fraction the fann_get_MSE value should change within fann_get_cascade_output_stagnation_epochs during training of the output connections, in order for the training not to stagnate.
FANN_EXTERNAL float FANN_API fann_get_cascade_output_change_fraction( struct fann * ann )
Reads the mean square error from the network.
FANN_EXTERNAL float FANN_API fann_get_MSE( struct fann * ann )
The number of cascade output stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of fann_get_cascade_output_change_fraction.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_output_stagnation_epochs( struct fann * ann )
Sets the cascade output change fraction.
FANN_EXTERNAL void FANN_API fann_set_cascade_output_change_fraction( struct fann * ann, float cascade_output_change_fraction )
Sets the number of cascade output stagnation epochs.
FANN_EXTERNAL void FANN_API fann_set_cascade_output_stagnation_epochs( struct fann * ann, unsigned int cascade_output_stagnation_epochs )
The cascade candidate change fraction is a number between 0 and 1 determining how large a fraction the fann_get_MSE value should change within fann_get_cascade_candidate_stagnation_epochs during training of the candidate neurons, in order for the training not to stagnate.
FANN_EXTERNAL float FANN_API fann_get_cascade_candidate_change_fraction( struct fann * ann )
The number of cascade candidate stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of fann_get_cascade_candidate_change_fraction.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_candidate_stagnation_epochs( struct fann * ann )
Sets the cascade candidate change fraction.
FANN_EXTERNAL void FANN_API fann_set_cascade_candidate_change_fraction( struct fann * ann, float cascade_candidate_change_fraction )
Sets the number of cascade candidate stagnation epochs.
FANN_EXTERNAL void FANN_API fann_set_cascade_candidate_stagnation_epochs( struct fann * ann, unsigned int cascade_candidate_stagnation_epochs )
The weight multiplier is a parameter which is used to multiply the weights from the candidate neuron before adding the neuron to the neural network.
FANN_EXTERNAL fann_type FANN_API fann_get_cascade_weight_multiplier( struct fann * ann )
Sets the weight multiplier.
FANN_EXTERNAL void FANN_API fann_set_cascade_weight_multiplier( struct fann * ann, fann_type cascade_weight_multiplier )
The candidate limit is a limit for how much the candidate neuron may be trained.
FANN_EXTERNAL fann_type FANN_API fann_get_cascade_candidate_limit( struct fann * ann )
Sets the candidate limit.
FANN_EXTERNAL void FANN_API fann_set_cascade_candidate_limit( struct fann * ann, fann_type cascade_candidate_limit )
The maximum out epochs determines the maximum number of epochs the output connections may be trained after adding a new candidate neuron.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_max_out_epochs( struct fann * ann )
Sets the maximum out epochs.
FANN_EXTERNAL void FANN_API fann_set_cascade_max_out_epochs( struct fann * ann, unsigned int cascade_max_out_epochs )
The minimum out epochs determines the minimum number of epochs the output connections must be trained after adding a new candidate neuron.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_min_out_epochs( struct fann * ann )
Sets the minimum out epochs.
FANN_EXTERNAL void FANN_API fann_set_cascade_min_out_epochs( struct fann * ann, unsigned int cascade_min_out_epochs )
The maximum candidate epochs determines the maximum number of epochs the input connections to the candidates may be trained before adding a new candidate neuron.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_max_cand_epochs( struct fann * ann )
Sets the max candidate epochs.
FANN_EXTERNAL void FANN_API fann_set_cascade_max_cand_epochs( struct fann * ann, unsigned int cascade_max_cand_epochs )
The minimum candidate epochs determines the minimum number of epochs the input connections to the candidates may be trained before adding a new candidate neuron.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_min_cand_epochs( struct fann * ann )
Sets the min candidate epochs.
FANN_EXTERNAL void FANN_API fann_set_cascade_min_cand_epochs( struct fann * ann, unsigned int cascade_min_cand_epochs )
The number of candidates used during training (calculated by multiplying fann_get_cascade_activation_functions_count, fann_get_cascade_activation_steepnesses_count and fann_get_cascade_num_candidate_groups).
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_num_candidates( struct fann * ann )
The number of activation functions in the fann_get_cascade_activation_functions array.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_activation_functions_count( struct fann * ann )
The number of activation steepnesses in the fann_get_cascade_activation_functions array.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_activation_steepnesses_count( struct fann * ann )
The number of candidate groups is the number of groups of identical candidates which will be used during training.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_num_candidate_groups( struct fann * ann )
The cascade activation functions array is an array of the different activation functions used by the candidates.
FANN_EXTERNAL enum fann_activationfunc_enum * FANN_API fann_get_cascade_activation_functions( struct fann * ann )
Sets the array of cascade candidate activation functions.
FANN_EXTERNAL void FANN_API fann_set_cascade_activation_functions( struct fann * ann, enum fann_activationfunc_enum * cascade_activation_functions, unsigned int cascade_activation_functions_count )
The cascade activation steepnesses array is an array of the different activation functions used by the candidates.
FANN_EXTERNAL fann_type * FANN_API fann_get_cascade_activation_steepnesses( struct fann * ann )
Sets the array of cascade candidate activation steepnesses.
FANN_EXTERNAL void FANN_API fann_set_cascade_activation_steepnesses( struct fann * ann, fann_type * cascade_activation_steepnesses, unsigned int cascade_activation_steepnesses_count )
Sets the number of candidate groups.
FANN_EXTERNAL void FANN_API fann_set_cascade_num_candidate_groups( struct fann * ann, unsigned int cascade_num_candidate_groups )
Creates a standard backpropagation neural network, which is not fully connected and which also has shortcut connections.
FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut( unsigned int num_layers, ... )
Set the training algorithm.
FANN_EXTERNAL void FANN_API fann_set_training_algorithm( struct fann * ann, enum fann_train_enum training_algorithm )
The number of fail bits; means the number of output neurons which differ more than the bit fail limit (see fann_get_bit_fail_limit, fann_set_bit_fail_limit).
FANN_EXTERNAL unsigned int FANN_API fann_get_bit_fail( struct fann * ann )
Set the stop function used during training.
FANN_EXTERNAL void FANN_API fann_set_train_stop_function( struct fann * ann, enum fann_stopfunc_enum train_stop_function )
Sets the callback function for use during training.
FANN_EXTERNAL void FANN_API fann_set_callback( struct fann * ann, fann_callback_type callback )
Trains on an entire dataset, for a period of time.
FANN_EXTERNAL void FANN_API fann_train_on_data( struct fann * ann, struct fann_train_data * data, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error )
Encapsulation of a neural network struct fann and associated C API functions.
class neural_net
Encapsulation of a training data set struct fann_train_data and associated C API functions.
class training_data
The FANN namespace groups the C++ wrapper definitions
namespace FANN
Trains on an entire dataset, for a period of time.
void train_on_data( const training_data & data, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error )
Does the same as train_on_data, but reads the training data directly from a file.
void train_on_file( const std:: string & filename, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error )
Get the type of neural network it was created as.
network_type_enum get_network_type()
Trains on an entire dataset, for a period of time using the Cascade2 training algorithm.
void cascadetrain_on_data( const training_data & data, unsigned int max_neurons, unsigned int neurons_between_reports, float desired_error )
Structure used to store data, for use with training.
struct fann_train_data
Default constructor creates an empty neural net.
training_data( ) : train_data(NULL)
Provides automatic cleanup of data.
#ifdef USE_VIRTUAL_DESTRUCTOR virtual #endif ~training_data()
Destructs the training data.
void destroy_train()
Reads a file that stores training data.
bool read_train_from_file( const std:: string & filename )
Save the training structure to a file, with the format as specified in read_train_from_file
bool save_train( const std:: string & filename )
Saves the training structure to a fixed point data file.
bool save_train_to_fixed( const std:: string & filename, unsigned int decimal_point )
Shuffles training data, randomizing the order.
void shuffle_train_data()
Merges the data into the data contained in the training_data.
void merge_train_data( const training_data & data )
Returns the number of training patterns in the training_data.
unsigned int length_train_data()
Returns the number of inputs in each of the training patterns in the training_data.
unsigned int num_input_train_data()
Returns the number of outputs in each of the training patterns in the struct fann_train_data.
unsigned int num_output_train_data()
A pointer to the array of input training data
fann_type **get_input()
A pointer to the array of output training data
fann_type **get_output()
Set the training data to the input and output data provided.
void set_train_data( unsigned int num_data, unsigned int num_input, fann_type ** input, unsigned int num_output, fann_type ** output )
Creates the training data struct from a user supplied function.
void create_train_from_callback( unsigned int num_data, unsigned int num_input, unsigned int num_output, void (FANN_API *user_function)( unsigned int, unsigned int, unsigned int, fann_type * , fann_type * ) )
Scales the inputs in the training data to the specified range.
void scale_input_train_data( fann_type new_min, fann_type new_max )
Scales the outputs in the training data to the specified range.
void scale_output_train_data( fann_type new_min, fann_type new_max )
Scales the inputs and outputs in the training data to the specified range.
void scale_train_data( fann_type new_min, fann_type new_max )
Changes the training data to a subset, starting at position pos and length elements forward.
void subset_train_data( unsigned int pos, unsigned int length )
The fast artificial neural network(fann) structure.
struct fann
Default constructor creates an empty neural net.
neural_net( ) : ann(NULL)
Set the internal fann struct to a copy of other
void copy_from_struct_fann( struct fann * other )
Provides automatic cleanup of data.
#ifdef USE_VIRTUAL_DESTRUCTOR virtual #endif ~neural_net()
Destructs the entire network.
void destroy()
Creates a standard fully connected backpropagation neural network.
bool create_standard( unsigned int num_layers, ... )
Just like create_standard, but with an array of layer sizes instead of individual parameters.
bool create_standard_array( unsigned int num_layers, const unsigned int * layers )
Creates a standard backpropagation neural network, which is not fully connected.
bool create_sparse( float connection_rate, unsigned int num_layers, ... )
Just like create_sparse, but with an array of layer sizes instead of individual parameters.
bool create_sparse_array( float connection_rate, unsigned int num_layers, const unsigned int * layers )
Creates a standard backpropagation neural network, which is not fully connected and which also has shortcut connections.
bool create_shortcut( unsigned int num_layers, ... )
Just like create_shortcut, but with an array of layer sizes instead of individual parameters.
bool create_shortcut_array( unsigned int num_layers, const unsigned int * layers )
Will run input through the neural network, returning an array of outputs, the number of which being equal to the number of neurons in the output layer.
fann_type* run( fann_type * input )
Give each connection a random weight between min_weight and max_weight
void randomize_weights( fann_type min_weight, fann_type max_weight )
Initialize the weights using Widrow + Nguyen’s algorithm.
void init_weights( const training_data & data )
Will print the connections of the ann in a compact matrix, for easy viewing of the internals of the ann.
void print_connections()
Constructs a backpropagation neural network from a configuration file, which have been saved by save.
bool create_from_file( const std:: string & configuration_file )
Save the entire network to a configuration file.
bool save( const std:: string & configuration_file )
Saves the entire network to a configuration file.
int save_to_fixed( const std:: string & configuration_file )
Train one iteration with a set of inputs, and a set of desired outputs.
void train( fann_type * input, fann_type * desired_output )
Train one epoch with a set of training data.
float train_epoch( const training_data & data )
Test with a set of inputs, and a set of desired outputs.
fann_type * test( fann_type * input, fann_type * desired_output )
Test a set of training data and calculates the MSE for the training data.
float test_data( const training_data & data )
Reads the mean square error from the network.
float get_MSE()
Resets the mean square error from the network.
void reset_MSE()
Sets the callback function for use during training.
void set_callback( callback_type callback, void * user_data )
Prints all of the parameters and options of the neural network
void print_parameters()
Return the training algorithm as described by FANN::training_algorithm_enum.
training_algorithm_enum get_training_algorithm()
Set the training algorithm.
void set_training_algorithm( training_algorithm_enum training_algorithm )
Return the learning rate.
float get_learning_rate()
Set the learning rate.
void set_learning_rate( float learning_rate )
Get the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.
activation_function_enum get_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( activation_function_enum activation_function, int layer, int neuron )
Set the activation function for all the neurons in the layer number layer, counting the input layer as layer 0.
void set_activation_function_layer( activation_function_enum activation_function, int layer )
Set the activation function for all of the hidden layers.
void set_activation_function_hidden( activation_function_enum activation_function )
Set the activation function for the output layer.
void set_activation_function_output( activation_function_enum activation_function )
Get the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.
fann_type get_activation_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( fann_type steepness, int layer, int neuron )
Set the activation steepness all of the neurons in layer number layer, counting the input layer as layer 0.
void set_activation_steepness_layer( fann_type steepness, int layer )
Set the steepness of the activation steepness in all of the hidden layers.
void set_activation_steepness_hidden( fann_type steepness )
Set the steepness of the activation steepness in the output layer.
void set_activation_steepness_output( fann_type steepness )
Returns the error function used during training.
error_function_enum get_train_error_function()
Set the error function used during training.
void set_train_error_function( error_function_enum train_error_function )
The decay is a small negative valued number which is the factor that the weights should become smaller in each iteration during quickprop training.
float get_quickprop_decay()
Sets the quickprop decay factor.
void set_quickprop_decay( float quickprop_decay )
The mu factor is used to increase and decrease the step-size during quickprop training.
float get_quickprop_mu()
Sets the quickprop mu factor.
void set_quickprop_mu( float quickprop_mu )
The increase factor is a value larger than 1, which is used to increase the step-size during RPROP training.
float get_rprop_increase_factor()
The increase factor used during RPROP training.
void set_rprop_increase_factor( float rprop_increase_factor )
The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training.
float get_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_decrease_factor( float rprop_decrease_factor )
The initial step-size is a small positive number determining how small the initial step-size may be.
float get_rprop_delta_zero()
The initial step-size is a small positive number determining how small the initial step-size may be.
void set_rprop_delta_zero( float rprop_delta_zero )
The minimum step-size is a small positive number determining how small the minimum step-size may be.
float get_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_min( float rprop_delta_min )
The maximum step-size is a positive number determining how large the maximum step-size may be.
float get_rprop_delta_max()
The maximum step-size is a positive number determining how large the maximum step-size may be.
void set_rprop_delta_max( float rprop_delta_max )
The sarprop weight decay shift.
float get_sarprop_weight_decay_shift()
Set the sarprop weight decay shift.
void set_sarprop_weight_decay_shift( float sarprop_weight_decay_shift )
The sarprop step error threshold factor.
float get_sarprop_step_error_threshold_factor()
Set the sarprop step error threshold factor.
void set_sarprop_step_error_threshold_factor( float sarprop_step_error_threshold_factor )
The get sarprop step error shift.
float get_sarprop_step_error_shift()
Set the sarprop step error shift.
void set_sarprop_step_error_shift( float sarprop_step_error_shift )
The sarprop weight decay shift.
float get_sarprop_temperature()
Set the sarprop_temperature.
void set_sarprop_temperature( float sarprop_temperature )
Get the number of input neurons.
unsigned int get_num_input()
Get the number of output neurons.
unsigned int get_num_output()
Get the total number of neurons in the entire network.
unsigned int get_total_neurons()
Get the total number of connections in the entire network.
unsigned int get_total_connections()
Returns the position of the decimal point in the ann.
unsigned int get_decimal_point()
Returns the multiplier that fix point data is multiplied with.
unsigned int get_multiplier()
Get the connection rate used when the network was created
float get_connection_rate()
Get the number of layers in the network
unsigned int get_num_layers()
Get the number of neurons in each layer in the network.
void get_layer_array( unsigned int * layers )
Get the number of bias in each layer in the network.
void get_bias_array( unsigned int * bias )
Get the connections in the network.
void get_connection_array( connection * connections )
Set connections in the network.
void set_weight_array( connection * connections, unsigned int num_connections )
Set a connection in the network.
void set_weight( unsigned int from_neuron, unsigned int to_neuron, fann_type weight )
Get the learning momentum.
float get_learning_momentum()
Set the learning momentum.
void set_learning_momentum( float learning_momentum )
Returns the the stop function used during training.
stop_function_enum get_train_stop_function()
Set the stop function used during training.
void set_train_stop_function( stop_function_enum train_stop_function )
Returns the bit fail limit used during training.
fann_type get_bit_fail_limit()
Set the bit fail limit used during training.
void set_bit_fail_limit( fann_type bit_fail_limit )
The number of fail bits; means the number of output neurons which differ more than the bit fail limit (see get_bit_fail_limit, set_bit_fail_limit).
unsigned int get_bit_fail()
Does the same as cascadetrain_on_data, but reads the training data directly from a file.
void cascadetrain_on_file( const std:: string & filename, unsigned int max_neurons, unsigned int neurons_between_reports, float desired_error )
The cascade output change fraction is a number between 0 and 1 determining how large a fraction the get_MSE value should change within get_cascade_output_stagnation_epochs during training of the output connections, in order for the training not to stagnate.
float get_cascade_output_change_fraction()
The number of cascade output stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of get_cascade_output_change_fraction.
unsigned int get_cascade_output_stagnation_epochs()
Sets the cascade output change fraction.
void set_cascade_output_change_fraction( float cascade_output_change_fraction )
Sets the number of cascade output stagnation epochs.
void set_cascade_output_stagnation_epochs( unsigned int cascade_output_stagnation_epochs )
The cascade candidate change fraction is a number between 0 and 1 determining how large a fraction the get_MSE value should change within get_cascade_candidate_stagnation_epochs during training of the candidate neurons, in order for the training not to stagnate.
float get_cascade_candidate_change_fraction()
The number of cascade candidate stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of get_cascade_candidate_change_fraction.
unsigned int get_cascade_candidate_stagnation_epochs()
Sets the cascade candidate change fraction.
void set_cascade_candidate_change_fraction( float cascade_candidate_change_fraction )
Sets the number of cascade candidate stagnation epochs.
void set_cascade_candidate_stagnation_epochs( unsigned int cascade_candidate_stagnation_epochs )
The weight multiplier is a parameter which is used to multiply the weights from the candidate neuron before adding the neuron to the neural network.
fann_type get_cascade_weight_multiplier()
Sets the weight multiplier.
void set_cascade_weight_multiplier( fann_type cascade_weight_multiplier )
The candidate limit is a limit for how much the candidate neuron may be trained.
fann_type get_cascade_candidate_limit()
Sets the candidate limit.
void set_cascade_candidate_limit( fann_type cascade_candidate_limit )
The maximum out epochs determines the maximum number of epochs the output connections may be trained after adding a new candidate neuron.
unsigned int get_cascade_max_out_epochs()
Sets the maximum out epochs.
void set_cascade_max_out_epochs( unsigned int cascade_max_out_epochs )
The maximum candidate epochs determines the maximum number of epochs the input connections to the candidates may be trained before adding a new candidate neuron.
unsigned int get_cascade_max_cand_epochs()
Sets the max candidate epochs.
void set_cascade_max_cand_epochs( unsigned int cascade_max_cand_epochs )
The number of candidates used during training (calculated by multiplying get_cascade_activation_functions_count, get_cascade_activation_steepnesses_count and get_cascade_num_candidate_groups).
unsigned int get_cascade_num_candidates()
The number of activation functions in the get_cascade_activation_functions array.
unsigned int get_cascade_activation_functions_count()
The number of activation steepnesses in the get_cascade_activation_functions array.
unsigned int get_cascade_activation_steepnesses_count()
The number of candidate groups is the number of groups of identical candidates which will be used during training.
unsigned int get_cascade_num_candidate_groups()
The cascade activation functions array is an array of the different activation functions used by the candidates.
activation_function_enum * get_cascade_activation_functions()
Sets the array of cascade candidate activation functions.
void set_cascade_activation_functions( activation_function_enum * cascade_activation_functions, unsigned int cascade_activation_functions_count )
The cascade activation steepnesses array is an array of the different activation functions used by the candidates.
fann_type *get_cascade_activation_steepnesses()
Sets the array of cascade candidate activation steepnesses.
void set_cascade_activation_steepnesses( fann_type * cascade_activation_steepnesses, unsigned int cascade_activation_steepnesses_count )
Sets the number of candidate groups.
void set_cascade_num_candidate_groups( unsigned int cascade_num_candidate_groups )
Scale input and output data based on previously calculated parameters.
void scale_train( training_data & data )
Descale input and output data based on previously calculated parameters.
void descale_train( training_data & data )
Calculate scaling parameters for future use based on training data.
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.
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.
bool set_scaling_params( const training_data & data, float new_input_min, float new_input_max, float new_output_min, float new_output_max )
Clears scaling parameters.
bool clear_scaling_params()
Scale data in input vector before feed it to ann based on previously calculated parameters.
void scale_input( fann_type * input_vector )
Scale data in output vector before feed it to ann based on previously calculated parameters.
void scale_output( fann_type * output_vector )
Scale data in input vector after get it from ann based on previously calculated parameters.
void descale_input( fann_type * input_vector )
Scale data in output vector after get it from ann based on previously calculated parameters.
void descale_output( fann_type * output_vector )
Change where errors are logged to.
void set_error_log( FILE * log_file )
Returns the last error number.
unsigned int get_errno()
Resets the last error number.
void reset_errno()
Resets the last error string.
void reset_errstr()
Returns the last errstr.
std::string get_errstr()
Prints the last error to stderr.
void print_error()
Get the type of neural network it was created as.
FANN_EXTERNAL enum fann_nettype_enum FANN_API fann_get_network_type( struct fann * ann )
Returns an exact copy of a struct fann_train_data.
FANN_EXTERNAL struct fann_train_data *FANN_API fann_duplicate_train_data( struct fann_train_data * data )
Reads a file that stores training data.
FANN_EXTERNAL struct fann_train_data *FANN_API fann_read_train_from_file( const char * filename )
Save the training structure to a file, with the format as specified in fann_read_train_from_file
FANN_EXTERNAL int FANN_API fann_save_train( struct fann_train_data * data, const char * filename )
Saves the training structure to a fixed point data file.
FANN_EXTERNAL int FANN_API fann_save_train_to_fixed( struct fann_train_data * data, const char * filename, unsigned int decimal_point )
Returns the number of training patterns in the struct fann_train_data.
FANN_EXTERNAL unsigned int FANN_API fann_length_train_data( struct fann_train_data * data )
Returns the number of inputs in each of the training patterns in the struct fann_train_data.
FANN_EXTERNAL unsigned int FANN_API fann_num_input_train_data( struct fann_train_data * data )
Returns the number of outputs in each of the training patterns in the struct fann_train_data.
FANN_EXTERNAL unsigned int FANN_API fann_num_output_train_data( struct fann_train_data * data )
Creates the training data struct from a user supplied function.
FANN_EXTERNAL struct fann_train_data * FANN_API fann_create_train_from_callback( unsigned int num_data, unsigned int num_input, unsigned int num_output, void (FANN_API *user_function)( unsigned int, unsigned int, unsigned int, fann_type * , fann_type * ) )
Scales the inputs in the training data to the specified range.
FANN_EXTERNAL void FANN_API fann_scale_input_train_data( struct fann_train_data * train_data, fann_type new_min, fann_type new_max )
Scales the outputs in the training data to the specified range.
FANN_EXTERNAL void FANN_API fann_scale_output_train_data( struct fann_train_data * train_data, fann_type new_min, fann_type new_max )
Scales the inputs and outputs in the training data to the specified range.
FANN_EXTERNAL void FANN_API fann_scale_train_data( struct fann_train_data * train_data, fann_type new_min, fann_type new_max )
Returns an copy of a subset of the struct fann_train_data, starting at position pos and length elements forward.
FANN_EXTERNAL struct fann_train_data *FANN_API fann_subset_train_data( struct fann_train_data * data, unsigned int pos, unsigned int length )
Just like fann_create_standard, but with an array of layer sizes instead of individual parameters.
FANN_EXTERNAL struct fann *FANN_API fann_create_standard_array( unsigned int num_layers, const unsigned int * layers )
Creates a standard fully connected backpropagation neural network.
FANN_EXTERNAL struct fann *FANN_API fann_create_standard( unsigned int num_layers, ... )
Creates a standard backpropagation neural network, which is not fully connected.
FANN_EXTERNAL struct fann *FANN_API fann_create_sparse( float connection_rate, unsigned int num_layers, ... )
Just like fann_create_sparse, but with an array of layer sizes instead of individual parameters.
FANN_EXTERNAL struct fann *FANN_API fann_create_sparse_array( float connection_rate, unsigned int num_layers, const unsigned int * layers )
Creates a standard backpropagation neural network, which is not fully connected and which also has shortcut connections.
FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut( unsigned int num_layers, ... )
Just like fann_create_shortcut, but with an array of layer sizes instead of individual parameters.
FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut_array( unsigned int num_layers, const unsigned int * layers )
Will run input through the neural network, returning an array of outputs, the number of which being equal to the number of neurons in the output layer.
FANN_EXTERNAL fann_type * FANN_API fann_run( struct fann * ann, fann_type * input )
Give each connection a random weight between min_weight and max_weight
FANN_EXTERNAL void FANN_API fann_randomize_weights( struct fann * ann, fann_type min_weight, fann_type max_weight )
Initialize the weights using Widrow + Nguyen’s algorithm.
FANN_EXTERNAL void FANN_API fann_init_weights( struct fann * ann, struct fann_train_data * train_data )
Constructs a backpropagation neural network from a configuration file, which have been saved by fann_save.
FANN_EXTERNAL struct fann *FANN_API fann_create_from_file( const char * configuration_file )
Save the entire network to a configuration file.
FANN_EXTERNAL int FANN_API fann_save( struct fann * ann, const char * configuration_file )
Saves the entire network to a configuration file.
FANN_EXTERNAL int FANN_API fann_save_to_fixed( struct fann * ann, const char * configuration_file )
Get the number of input neurons.
FANN_EXTERNAL unsigned int FANN_API fann_get_num_input( struct fann * ann )
Get the number of output neurons.
FANN_EXTERNAL unsigned int FANN_API fann_get_num_output( struct fann * ann )
Train one iteration with a set of inputs, and a set of desired outputs.
FANN_EXTERNAL void FANN_API fann_train( struct fann * ann, fann_type * input, fann_type * desired_output )
Set the training algorithm.
FANN_EXTERNAL void FANN_API fann_set_training_algorithm( struct fann * ann, enum fann_train_enum training_algorithm )
Train one epoch with a set of training data.
FANN_EXTERNAL float FANN_API fann_train_epoch( struct fann * ann, struct fann_train_data * data )
Trains on an entire dataset, for a period of time.
FANN_EXTERNAL void FANN_API fann_train_on_data( struct fann * ann, struct fann_train_data * data, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error )
Does the same as fann_train_on_data, but reads the training data directly from a file.
FANN_EXTERNAL void FANN_API fann_train_on_file( struct fann * ann, const char * filename, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error )
Test with a set of inputs, and a set of desired outputs.
FANN_EXTERNAL fann_type * FANN_API fann_test( struct fann * ann, fann_type * input, fann_type * desired_output )
Test a set of training data and calculates the MSE for the training data.
FANN_EXTERNAL float FANN_API fann_test_data( struct fann * ann, struct fann_train_data * data )
Reads the mean square error from the network.
FANN_EXTERNAL float FANN_API fann_get_MSE( struct fann * ann )
Resets the mean square error from the network.
FANN_EXTERNAL void FANN_API fann_reset_MSE( struct fann * ann )
Prints all of the parameters and options of the ANN
FANN_EXTERNAL void FANN_API fann_print_parameters( struct fann * ann )
Return the training algorithm as described by fann_train_enum.
FANN_EXTERNAL enum fann_train_enum FANN_API fann_get_training_algorithm( struct fann * ann )
Return the learning rate.
FANN_EXTERNAL float FANN_API fann_get_learning_rate( struct fann * ann )
Get the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL enum fann_activationfunc_enum FANN_API fann_get_activation_function( struct fann * ann, int layer, int neuron )
Set the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_function( struct fann * ann, enum fann_activationfunc_enum activation_function, int layer, int neuron )
Set the activation function for all the neurons in the layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_function_layer( struct fann * ann, enum fann_activationfunc_enum activation_function, int layer )
Set the activation function for all of the hidden layers.
FANN_EXTERNAL void FANN_API fann_set_activation_function_hidden( struct fann * ann, enum fann_activationfunc_enum activation_function )
Set the activation function for the output layer.
FANN_EXTERNAL void FANN_API fann_set_activation_function_output( struct fann * ann, enum fann_activationfunc_enum activation_function )
Get the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL fann_type FANN_API fann_get_activation_steepness( struct fann * ann, int layer, int neuron )
Set the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness( struct fann * ann, fann_type steepness, int layer, int neuron )
Set the activation steepness all of the neurons in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness_layer( struct fann * ann, fann_type steepness, int layer )
Set the steepness of the activation steepness in all of the hidden layers.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness_hidden( struct fann * ann, fann_type steepness )
Set the steepness of the activation steepness in the output layer.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness_output( struct fann * ann, fann_type steepness )
Returns the error function used during training.
FANN_EXTERNAL enum fann_errorfunc_enum FANN_API fann_get_train_error_function( struct fann * ann )
Set the error function used during training.
FANN_EXTERNAL void FANN_API fann_set_train_error_function( struct fann * ann, enum fann_errorfunc_enum train_error_function )
The decay is a small negative valued number which is the factor that the weights should become smaller in each iteration during quickprop training.
FANN_EXTERNAL float FANN_API fann_get_quickprop_decay( struct fann * ann )
Sets the quickprop decay factor.
FANN_EXTERNAL void FANN_API fann_set_quickprop_decay( struct fann * ann, float quickprop_decay )
The mu factor is used to increase and decrease the step-size during quickprop training.
FANN_EXTERNAL float FANN_API fann_get_quickprop_mu( struct fann * ann )
Sets the quickprop mu factor.
FANN_EXTERNAL void FANN_API fann_set_quickprop_mu( struct fann * ann, float quickprop_mu )
The increase factor is a value larger than 1, which is used to increase the step-size during RPROP training.
FANN_EXTERNAL float FANN_API fann_get_rprop_increase_factor( struct fann * ann )
The increase factor used during RPROP training.
FANN_EXTERNAL void FANN_API fann_set_rprop_increase_factor( struct fann * ann, float rprop_increase_factor )
The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training.
FANN_EXTERNAL float FANN_API fann_get_rprop_decrease_factor( struct fann * ann )
The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training.
FANN_EXTERNAL void FANN_API fann_set_rprop_decrease_factor( struct fann * ann, float rprop_decrease_factor )
The initial step-size is a positive number determining the initial step size.
FANN_EXTERNAL float FANN_API fann_get_rprop_delta_zero( struct fann * ann )
The initial step-size is a positive number determining the initial step size.
FANN_EXTERNAL void FANN_API fann_set_rprop_delta_zero( struct fann * ann, float rprop_delta_max )
The minimum step-size is a small positive number determining how small the minimum step-size may be.
FANN_EXTERNAL float FANN_API fann_get_rprop_delta_min( struct fann * ann )
The minimum step-size is a small positive number determining how small the minimum step-size may be.
FANN_EXTERNAL void FANN_API fann_set_rprop_delta_min( struct fann * ann, float rprop_delta_min )
The maximum step-size is a positive number determining how large the maximum step-size may be.
FANN_EXTERNAL float FANN_API fann_get_rprop_delta_max( struct fann * ann )
The maximum step-size is a positive number determining how large the maximum step-size may be.
FANN_EXTERNAL void FANN_API fann_set_rprop_delta_max( struct fann * ann, float rprop_delta_max )
Set the sarprop weight decay shift.
FANN_EXTERNAL void FANN_API fann_set_sarprop_weight_decay_shift( struct fann * ann, float sarprop_weight_decay_shift )
Set the sarprop step error threshold factor.
FANN_EXTERNAL void FANN_API fann_set_sarprop_step_error_threshold_factor( struct fann * ann, float sarprop_step_error_threshold_factor )
Set the sarprop step error shift.
FANN_EXTERNAL void FANN_API fann_set_sarprop_step_error_shift( struct fann * ann, float sarprop_step_error_shift )
Set the sarprop_temperature.
FANN_EXTERNAL void FANN_API fann_set_sarprop_temperature( struct fann * ann, float sarprop_temperature )
Returns the position of the decimal point in the ann.
FANN_EXTERNAL unsigned int FANN_API fann_get_decimal_point( struct fann * ann )
returns the multiplier that fix point data is multiplied with.
FANN_EXTERNAL unsigned int FANN_API fann_get_multiplier( struct fann * ann )
Get the connection rate used when the network was created
FANN_EXTERNAL float FANN_API fann_get_connection_rate( struct fann * ann )
Get the number of layers in the network
FANN_EXTERNAL unsigned int FANN_API fann_get_num_layers( struct fann * ann )
Get the number of neurons in each layer in the network.
FANN_EXTERNAL void FANN_API fann_get_layer_array( struct fann * ann, unsigned int * layers )
Get the number of bias in each layer in the network.
FANN_EXTERNAL void FANN_API fann_get_bias_array( struct fann * ann, unsigned int * bias )
Get the connections in the network.
FANN_EXTERNAL void FANN_API fann_get_connection_array( struct fann * ann, struct fann_connection * connections )
Set connections in the network.
FANN_EXTERNAL void FANN_API fann_set_weight_array( struct fann * ann, struct fann_connection * connections, unsigned int num_connections )
Set a connection in the network.
FANN_EXTERNAL void FANN_API fann_set_weight( struct fann * ann, unsigned int from_neuron, unsigned int to_neuron, fann_type weight )
The number of fail bits; means the number of output neurons which differ more than the bit fail limit (see fann_get_bit_fail_limit, fann_set_bit_fail_limit).
FANN_EXTERNAL unsigned int FANN_API fann_get_bit_fail( struct fann * ann )
Set the stop function used during training.
FANN_EXTERNAL void FANN_API fann_set_train_stop_function( struct fann * ann, enum fann_stopfunc_enum train_stop_function )
Trains on an entire dataset, for a period of time using the Cascade2 training algorithm.
FANN_EXTERNAL void FANN_API fann_cascadetrain_on_data( struct fann * ann, struct fann_train_data * data, unsigned int max_neurons, unsigned int neurons_between_reports, float desired_error )
Does the same as fann_cascadetrain_on_data, but reads the training data directly from a file.
FANN_EXTERNAL void FANN_API fann_cascadetrain_on_file( struct fann * ann, const char * filename, unsigned int max_neurons, unsigned int neurons_between_reports, float desired_error )
The cascade output change fraction is a number between 0 and 1 determining how large a fraction the fann_get_MSE value should change within fann_get_cascade_output_stagnation_epochs during training of the output connections, in order for the training not to stagnate.
FANN_EXTERNAL float FANN_API fann_get_cascade_output_change_fraction( struct fann * ann )
Sets the cascade output change fraction.
FANN_EXTERNAL void FANN_API fann_set_cascade_output_change_fraction( struct fann * ann, float cascade_output_change_fraction )
The number of cascade output stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of fann_get_cascade_output_change_fraction.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_output_stagnation_epochs( struct fann * ann )
Sets the number of cascade output stagnation epochs.
FANN_EXTERNAL void FANN_API fann_set_cascade_output_stagnation_epochs( struct fann * ann, unsigned int cascade_output_stagnation_epochs )
The cascade candidate change fraction is a number between 0 and 1 determining how large a fraction the fann_get_MSE value should change within fann_get_cascade_candidate_stagnation_epochs during training of the candidate neurons, in order for the training not to stagnate.
FANN_EXTERNAL float FANN_API fann_get_cascade_candidate_change_fraction( struct fann * ann )
Sets the cascade candidate change fraction.
FANN_EXTERNAL void FANN_API fann_set_cascade_candidate_change_fraction( struct fann * ann, float cascade_candidate_change_fraction )
The number of cascade candidate stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of fann_get_cascade_candidate_change_fraction.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_candidate_stagnation_epochs( struct fann * ann )
Sets the number of cascade candidate stagnation epochs.
FANN_EXTERNAL void FANN_API fann_set_cascade_candidate_stagnation_epochs( struct fann * ann, unsigned int cascade_candidate_stagnation_epochs )
The weight multiplier is a parameter which is used to multiply the weights from the candidate neuron before adding the neuron to the neural network.
FANN_EXTERNAL fann_type FANN_API fann_get_cascade_weight_multiplier( struct fann * ann )
Sets the weight multiplier.
FANN_EXTERNAL void FANN_API fann_set_cascade_weight_multiplier( struct fann * ann, fann_type cascade_weight_multiplier )
The candidate limit is a limit for how much the candidate neuron may be trained.
FANN_EXTERNAL fann_type FANN_API fann_get_cascade_candidate_limit( struct fann * ann )
Sets the candidate limit.
FANN_EXTERNAL void FANN_API fann_set_cascade_candidate_limit( struct fann * ann, fann_type cascade_candidate_limit )
The maximum out epochs determines the maximum number of epochs the output connections may be trained after adding a new candidate neuron.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_max_out_epochs( struct fann * ann )
Sets the maximum out epochs.
FANN_EXTERNAL void FANN_API fann_set_cascade_max_out_epochs( struct fann * ann, unsigned int cascade_max_out_epochs )
The maximum candidate epochs determines the maximum number of epochs the input connections to the candidates may be trained before adding a new candidate neuron.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_max_cand_epochs( struct fann * ann )
Sets the max candidate epochs.
FANN_EXTERNAL void FANN_API fann_set_cascade_max_cand_epochs( struct fann * ann, unsigned int cascade_max_cand_epochs )
The number of candidates used during training (calculated by multiplying fann_get_cascade_activation_functions_count, fann_get_cascade_activation_steepnesses_count and fann_get_cascade_num_candidate_groups).
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_num_candidates( struct fann * ann )
The number of activation functions in the fann_get_cascade_activation_functions array.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_activation_functions_count( struct fann * ann )
Sets the array of cascade candidate activation functions.
FANN_EXTERNAL void FANN_API fann_set_cascade_activation_functions( struct fann * ann, enum fann_activationfunc_enum * cascade_activation_functions, unsigned int cascade_activation_functions_count )
The number of activation steepnesses in the fann_get_cascade_activation_functions array.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_activation_steepnesses_count( struct fann * ann )
The cascade activation steepnesses array is an array of the different activation functions used by the candidates.
FANN_EXTERNAL fann_type * FANN_API fann_get_cascade_activation_steepnesses( struct fann * ann )
Sets the array of cascade candidate activation steepnesses.
FANN_EXTERNAL void FANN_API fann_set_cascade_activation_steepnesses( struct fann * ann, fann_type * cascade_activation_steepnesses, unsigned int cascade_activation_steepnesses_count )
The number of candidate groups is the number of groups of identical candidates which will be used during training.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_num_candidate_groups( struct fann * ann )
Sets the number of candidate groups.
FANN_EXTERNAL void FANN_API fann_set_cascade_num_candidate_groups( struct fann * ann, unsigned int cascade_num_candidate_groups )
Scale input and output data based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_scale_train( struct fann * ann, struct fann_train_data * data )
Descale input and output data based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_descale_train( struct fann * ann, struct fann_train_data * data )
Calculate input scaling parameters for future use based on training data.
FANN_EXTERNAL int FANN_API fann_set_input_scaling_params( struct fann * ann, const struct fann_train_data * data, float new_input_min, float new_input_max )
Calculate output scaling parameters for future use based on training data.
FANN_EXTERNAL int FANN_API fann_set_output_scaling_params( struct fann * ann, const struct fann_train_data * data, float new_output_min, float new_output_max )
Calculate input and output scaling parameters for future use based on training data.
FANN_EXTERNAL int FANN_API fann_set_scaling_params( struct fann * ann, const struct fann_train_data * data, float new_input_min, float new_input_max, float new_output_min, float new_output_max )
Clears scaling parameters.
FANN_EXTERNAL int FANN_API fann_clear_scaling_params( struct fann * ann )
Scale data in input vector before feed it to ann based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_scale_input( struct fann * ann, fann_type * input_vector )
Scale data in output vector before feed it to ann based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_scale_output( struct fann * ann, fann_type * output_vector )
Scale data in input vector after get it from ann based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_descale_input( struct fann * ann, fann_type * input_vector )
Scale data in output vector after get it from ann based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_descale_output( struct fann * ann, fann_type * output_vector )
Structure used to store error-related information, both struct fann and struct fann_train_data can be casted to this type.
struct fann_error
Change where errors are logged to.
FANN_EXTERNAL void FANN_API fann_set_error_log( struct fann_error * errdat, FILE * log_file )
Resets the last error number.
FANN_EXTERNAL void FANN_API fann_reset_errno( struct fann_error * errdat )
Returns the last error number.
FANN_EXTERNAL enum fann_errno_enum FANN_API fann_get_errno( struct fann_error * errdat )
Resets the last error string.
FANN_EXTERNAL void FANN_API fann_reset_errstr( struct fann_error * errdat )
The fast artificial neural network(fann) structure.
struct fann
Structure used to store data, for use with training.
struct fann_train_data
Trains on an entire dataset, for a period of time.
FANN_EXTERNAL void FANN_API fann_train_on_data( struct fann * ann, struct fann_train_data * data, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error )
Does the same as fann_train_on_data, but reads the training data directly from a file.
FANN_EXTERNAL void FANN_API fann_train_on_file( struct fann * ann, const char * filename, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error )
Get the type of neural network it was created as.
FANN_EXTERNAL enum fann_nettype_enum FANN_API fann_get_network_type( struct fann * ann )
Trains on an entire dataset, for a period of time using the Cascade2 training algorithm.
FANN_EXTERNAL void FANN_API fann_cascadetrain_on_data( struct fann * ann, struct fann_train_data * data, unsigned int max_neurons, unsigned int neurons_between_reports, float desired_error )
Structure used to store error-related information, both struct fann and struct fann_train_data can be casted to this type.
struct fann_error
Set the training algorithm.
FANN_EXTERNAL void FANN_API fann_set_training_algorithm( struct fann * ann, enum fann_train_enum training_algorithm )
Return the training algorithm as described by fann_train_enum.
FANN_EXTERNAL enum fann_train_enum FANN_API fann_get_training_algorithm( struct fann * ann )
Set the activation function for all of the hidden layers.
FANN_EXTERNAL void FANN_API fann_set_activation_function_hidden( struct fann * ann, enum fann_activationfunc_enum activation_function )
Set the activation function for the output layer.
FANN_EXTERNAL void FANN_API fann_set_activation_function_output( struct fann * ann, enum fann_activationfunc_enum activation_function )
Set the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_function( struct fann * ann, enum fann_activationfunc_enum activation_function, int layer, int neuron )
Set the steepness of the activation steepness in all of the hidden layers.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness_hidden( struct fann * ann, fann_type steepness )
Set the steepness of the activation steepness in the output layer.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness_output( struct fann * ann, fann_type steepness )
Set the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness( struct fann * ann, fann_type steepness, int layer, int neuron )
Set the activation function for all the neurons in the layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_function_layer( struct fann * ann, enum fann_activationfunc_enum activation_function, int layer )
Set the error function used during training.
FANN_EXTERNAL void FANN_API fann_set_train_error_function( struct fann * ann, enum fann_errorfunc_enum train_error_function )
Returns the error function used during training.
FANN_EXTERNAL enum fann_errorfunc_enum FANN_API fann_get_train_error_function( struct fann * ann )
Returns the bit fail limit used during training.
FANN_EXTERNAL fann_type FANN_API fann_get_bit_fail_limit( struct fann * ann )
Set the bit fail limit used during training.
FANN_EXTERNAL void FANN_API fann_set_bit_fail_limit( struct fann * ann, fann_type bit_fail_limit )
Set the stop function used during training.
FANN_EXTERNAL void FANN_API fann_set_train_stop_function( struct fann * ann, enum fann_stopfunc_enum train_stop_function )
Returns the the stop function used during training.
FANN_EXTERNAL enum fann_stopfunc_enum FANN_API fann_get_train_stop_function( struct fann * ann )
Sets the callback function for use during training.
FANN_EXTERNAL void FANN_API fann_set_callback( struct fann * ann, fann_callback_type callback )
Change where errors are logged to.
FANN_EXTERNAL void FANN_API fann_set_error_log( struct fann_error * errdat, FILE * log_file )
Returns the last error number.
FANN_EXTERNAL enum fann_errno_enum FANN_API fann_get_errno( struct fann_error * errdat )
Creates a standard fully connected backpropagation neural network.
FANN_EXTERNAL struct fann *FANN_API fann_create_standard( unsigned int num_layers, ... )
Destroys the entire network and properly freeing all the associated memmory.
FANN_EXTERNAL void FANN_API fann_destroy( struct fann * ann )
Get the connections in the network.
FANN_EXTERNAL void FANN_API fann_get_connection_array( struct fann * ann, struct fann_connection * connections )
Set connections in the network.
FANN_EXTERNAL void FANN_API fann_set_weight_array( struct fann * ann, struct fann_connection * connections, unsigned int num_connections )
The fast artificial neural network(fann) structure.
struct fann
Structure used to store data, for use with training.
struct fann_train_data
Change where errors are logged to.
FANN_EXTERNAL void FANN_API fann_set_error_log( struct fann_error * errdat, FILE * log_file )
Returns the last error number.
FANN_EXTERNAL enum fann_errno_enum FANN_API fann_get_errno( struct fann_error * errdat )
Resets the last error number.
FANN_EXTERNAL void FANN_API fann_reset_errno( struct fann_error * errdat )
Resets the last error string.
FANN_EXTERNAL void FANN_API fann_reset_errstr( struct fann_error * errdat )
Returns the last errstr.
FANN_EXTERNAL char *FANN_API fann_get_errstr( struct fann_error * errdat )
Prints the last error to stderr.
FANN_EXTERNAL void FANN_API fann_print_error( struct fann_error * errdat )
Structure used to store error-related information, both struct fann and struct fann_train_data can be casted to this type.
struct fann_error
Save the entire network to a configuration file.
FANN_EXTERNAL int FANN_API fann_save( struct fann * ann, const char * configuration_file )
Constructs a backpropagation neural network from a configuration file, which have been saved by fann_save.
FANN_EXTERNAL struct fann *FANN_API fann_create_from_file( const char * configuration_file )
Saves the entire network to a configuration file.
FANN_EXTERNAL int FANN_API fann_save_to_fixed( struct fann * ann, const char * configuration_file )
Sets the callback function for use during training.
FANN_EXTERNAL void FANN_API fann_set_callback( struct fann * ann, fann_callback_type callback )
Change where errors are logged to.
FANN_EXTERNAL void FANN_API fann_set_error_log( struct fann_error * errdat, FILE * log_file )
Store a pointer to user defined data.
FANN_EXTERNAL void FANN_API fann_set_user_data( struct fann * ann, void * user_data )
Reads the mean square error from the network.
FANN_EXTERNAL float FANN_API fann_get_MSE( struct fann * ann )
Structure used to store data, for use with training.
struct fann_train_data
Train one iteration with a set of inputs, and a set of desired outputs.
FANN_EXTERNAL void FANN_API fann_train( struct fann * ann, fann_type * input, fann_type * desired_output )
Test with a set of inputs, and a set of desired outputs.
FANN_EXTERNAL fann_type * FANN_API fann_test( struct fann * ann, fann_type * input, fann_type * desired_output )
Reads the mean square error from the network.
FANN_EXTERNAL float FANN_API fann_get_MSE( struct fann * ann )
The number of fail bits; means the number of output neurons which differ more than the bit fail limit (see fann_get_bit_fail_limit, fann_set_bit_fail_limit).
FANN_EXTERNAL unsigned int FANN_API fann_get_bit_fail( struct fann * ann )
Returns the bit fail limit used during training.
FANN_EXTERNAL fann_type FANN_API fann_get_bit_fail_limit( struct fann * ann )
Set the bit fail limit used during training.
FANN_EXTERNAL void FANN_API fann_set_bit_fail_limit( struct fann * ann, fann_type bit_fail_limit )
Resets the mean square error from the network.
FANN_EXTERNAL void FANN_API fann_reset_MSE( struct fann * ann )
Trains on an entire dataset, for a period of time.
FANN_EXTERNAL void FANN_API fann_train_on_data( struct fann * ann, struct fann_train_data * data, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error )
Does the same as fann_train_on_data, but reads the training data directly from a file.
FANN_EXTERNAL void FANN_API fann_train_on_file( struct fann * ann, const char * filename, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error )
Train one epoch with a set of training data.
FANN_EXTERNAL float FANN_API fann_train_epoch( struct fann * ann, struct fann_train_data * data )
Test a set of training data and calculates the MSE for the training data.
FANN_EXTERNAL float FANN_API fann_test_data( struct fann * ann, struct fann_train_data * data )
Reads a file that stores training data.
FANN_EXTERNAL struct fann_train_data *FANN_API fann_read_train_from_file( const char * filename )
Creates an empty training data struct.
FANN_EXTERNAL struct fann_train_data * FANN_API fann_create_train( unsigned int num_data, unsigned int num_input, unsigned int num_output )
Creates the training data struct from a user supplied function.
FANN_EXTERNAL struct fann_train_data * FANN_API fann_create_train_from_callback( unsigned int num_data, unsigned int num_input, unsigned int num_output, void (FANN_API *user_function)( unsigned int, unsigned int, unsigned int, fann_type * , fann_type * ) )
Destructs the training data and properly deallocates all of the associated data.
FANN_EXTERNAL void FANN_API fann_destroy_train( struct fann_train_data * train_data )
Shuffles training data, randomizing the order.
FANN_EXTERNAL void FANN_API fann_shuffle_train_data( struct fann_train_data * train_data )
Scale input and output data based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_scale_train( struct fann * ann, struct fann_train_data * data )
Descale input and output data based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_descale_train( struct fann * ann, struct fann_train_data * data )
Calculate input scaling parameters for future use based on training data.
FANN_EXTERNAL int FANN_API fann_set_input_scaling_params( struct fann * ann, const struct fann_train_data * data, float new_input_min, float new_input_max )
Calculate output scaling parameters for future use based on training data.
FANN_EXTERNAL int FANN_API fann_set_output_scaling_params( struct fann * ann, const struct fann_train_data * data, float new_output_min, float new_output_max )
Calculate input and output scaling parameters for future use based on training data.
FANN_EXTERNAL int FANN_API fann_set_scaling_params( struct fann * ann, const struct fann_train_data * data, float new_input_min, float new_input_max, float new_output_min, float new_output_max )
Clears scaling parameters.
FANN_EXTERNAL int FANN_API fann_clear_scaling_params( struct fann * ann )
Scale data in input vector before feed it to ann based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_scale_input( struct fann * ann, fann_type * input_vector )
Scale data in output vector before feed it to ann based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_scale_output( struct fann * ann, fann_type * output_vector )
Scale data in input vector after get it from ann based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_descale_input( struct fann * ann, fann_type * input_vector )
Scale data in output vector after get it from ann based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_descale_output( struct fann * ann, fann_type * output_vector )
Scales the inputs in the training data to the specified range.
FANN_EXTERNAL void FANN_API fann_scale_input_train_data( struct fann_train_data * train_data, fann_type new_min, fann_type new_max )
Scales the outputs in the training data to the specified range.
FANN_EXTERNAL void FANN_API fann_scale_output_train_data( struct fann_train_data * train_data, fann_type new_min, fann_type new_max )
Scales the inputs and outputs in the training data to the specified range.
FANN_EXTERNAL void FANN_API fann_scale_train_data( struct fann_train_data * train_data, fann_type new_min, fann_type new_max )
Merges the data from data1 and data2 into a new struct fann_train_data.
FANN_EXTERNAL struct fann_train_data *FANN_API fann_merge_train_data( struct fann_train_data * data1, struct fann_train_data * data2 )
Returns an exact copy of a struct fann_train_data.
FANN_EXTERNAL struct fann_train_data *FANN_API fann_duplicate_train_data( struct fann_train_data * data )
Returns an copy of a subset of the struct fann_train_data, starting at position pos and length elements forward.
FANN_EXTERNAL struct fann_train_data *FANN_API fann_subset_train_data( struct fann_train_data * data, unsigned int pos, unsigned int length )
Returns the number of training patterns in the struct fann_train_data.
FANN_EXTERNAL unsigned int FANN_API fann_length_train_data( struct fann_train_data * data )
Returns the number of inputs in each of the training patterns in the struct fann_train_data.
FANN_EXTERNAL unsigned int FANN_API fann_num_input_train_data( struct fann_train_data * data )
Returns the number of outputs in each of the training patterns in the struct fann_train_data.
FANN_EXTERNAL unsigned int FANN_API fann_num_output_train_data( struct fann_train_data * data )
Save the training structure to a file, with the format as specified in fann_read_train_from_file
FANN_EXTERNAL int FANN_API fann_save_train( struct fann_train_data * data, const char * filename )
Saves the training structure to a fixed point data file.
FANN_EXTERNAL int FANN_API fann_save_train_to_fixed( struct fann_train_data * data, const char * filename, unsigned int decimal_point )
Return the training algorithm as described by fann_train_enum.
FANN_EXTERNAL enum fann_train_enum FANN_API fann_get_training_algorithm( struct fann * ann )
Set the training algorithm.
FANN_EXTERNAL void FANN_API fann_set_training_algorithm( struct fann * ann, enum fann_train_enum training_algorithm )
Return the learning rate.
FANN_EXTERNAL float FANN_API fann_get_learning_rate( struct fann * ann )
Set the learning rate.
FANN_EXTERNAL void FANN_API fann_set_learning_rate( struct fann * ann, float learning_rate )
Get the learning momentum.
FANN_EXTERNAL float FANN_API fann_get_learning_momentum( struct fann * ann )
Set the learning momentum.
FANN_EXTERNAL void FANN_API fann_set_learning_momentum( struct fann * ann, float learning_momentum )
Get the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL enum fann_activationfunc_enum FANN_API fann_get_activation_function( struct fann * ann, int layer, int neuron )
Set the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_function( struct fann * ann, enum fann_activationfunc_enum activation_function, int layer, int neuron )
Set the activation function for all the neurons in the layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_function_layer( struct fann * ann, enum fann_activationfunc_enum activation_function, int layer )
Set the activation function for all of the hidden layers.
FANN_EXTERNAL void FANN_API fann_set_activation_function_hidden( struct fann * ann, enum fann_activationfunc_enum activation_function )
Set the activation function for the output layer.
FANN_EXTERNAL void FANN_API fann_set_activation_function_output( struct fann * ann, enum fann_activationfunc_enum activation_function )
Get the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL fann_type FANN_API fann_get_activation_steepness( struct fann * ann, int layer, int neuron )
Set the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness( struct fann * ann, fann_type steepness, int layer, int neuron )
Set the activation steepness all of the neurons in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness_layer( struct fann * ann, fann_type steepness, int layer )
Set the steepness of the activation steepness in all of the hidden layers.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness_hidden( struct fann * ann, fann_type steepness )
Set the steepness of the activation steepness in the output layer.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness_output( struct fann * ann, fann_type steepness )
Returns the error function used during training.
FANN_EXTERNAL enum fann_errorfunc_enum FANN_API fann_get_train_error_function( struct fann * ann )
Set the error function used during training.
FANN_EXTERNAL void FANN_API fann_set_train_error_function( struct fann * ann, enum fann_errorfunc_enum train_error_function )
Returns the the stop function used during training.
FANN_EXTERNAL enum fann_stopfunc_enum FANN_API fann_get_train_stop_function( struct fann * ann )
Set the stop function used during training.
FANN_EXTERNAL void FANN_API fann_set_train_stop_function( struct fann * ann, enum fann_stopfunc_enum train_stop_function )
Sets the callback function for use during training.
FANN_EXTERNAL void FANN_API fann_set_callback( struct fann * ann, fann_callback_type callback )
The decay is a small negative valued number which is the factor that the weights should become smaller in each iteration during quickprop training.
FANN_EXTERNAL float FANN_API fann_get_quickprop_decay( struct fann * ann )
Sets the quickprop decay factor.
FANN_EXTERNAL void FANN_API fann_set_quickprop_decay( struct fann * ann, float quickprop_decay )
The mu factor is used to increase and decrease the step-size during quickprop training.
FANN_EXTERNAL float FANN_API fann_get_quickprop_mu( struct fann * ann )
Sets the quickprop mu factor.
FANN_EXTERNAL void FANN_API fann_set_quickprop_mu( struct fann * ann, float quickprop_mu )
The increase factor is a value larger than 1, which is used to increase the step-size during RPROP training.
FANN_EXTERNAL float FANN_API fann_get_rprop_increase_factor( struct fann * ann )
The increase factor used during RPROP training.
FANN_EXTERNAL void FANN_API fann_set_rprop_increase_factor( struct fann * ann, float rprop_increase_factor )
The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training.
FANN_EXTERNAL float FANN_API fann_get_rprop_decrease_factor( struct fann * ann )
The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training.
FANN_EXTERNAL void FANN_API fann_set_rprop_decrease_factor( struct fann * ann, float rprop_decrease_factor )
The minimum step-size is a small positive number determining how small the minimum step-size may be.
FANN_EXTERNAL float FANN_API fann_get_rprop_delta_min( struct fann * ann )
The minimum step-size is a small positive number determining how small the minimum step-size may be.
FANN_EXTERNAL void FANN_API fann_set_rprop_delta_min( struct fann * ann, float rprop_delta_min )
The maximum step-size is a positive number determining how large the maximum step-size may be.
FANN_EXTERNAL float FANN_API fann_get_rprop_delta_max( struct fann * ann )
The maximum step-size is a positive number determining how large the maximum step-size may be.
FANN_EXTERNAL void FANN_API fann_set_rprop_delta_max( struct fann * ann, float rprop_delta_max )
The initial step-size is a positive number determining the initial step size.
FANN_EXTERNAL float FANN_API fann_get_rprop_delta_zero( struct fann * ann )
The initial step-size is a positive number determining the initial step size.
FANN_EXTERNAL void FANN_API fann_set_rprop_delta_zero( struct fann * ann, float rprop_delta_max )
The sarprop weight decay shift.
FANN_EXTERNAL float FANN_API fann_get_sarprop_weight_decay_shift( struct fann * ann )
Set the sarprop weight decay shift.
FANN_EXTERNAL void FANN_API fann_set_sarprop_weight_decay_shift( struct fann * ann, float sarprop_weight_decay_shift )
The sarprop step error threshold factor.
FANN_EXTERNAL float FANN_API fann_get_sarprop_step_error_threshold_factor( struct fann * ann )
Set the sarprop step error threshold factor.
FANN_EXTERNAL void FANN_API fann_set_sarprop_step_error_threshold_factor( struct fann * ann, float sarprop_step_error_threshold_factor )
The get sarprop step error shift.
FANN_EXTERNAL float FANN_API fann_get_sarprop_step_error_shift( struct fann * ann )
Set the sarprop step error shift.
FANN_EXTERNAL void FANN_API fann_set_sarprop_step_error_shift( struct fann * ann, float sarprop_step_error_shift )
The sarprop weight decay shift.
FANN_EXTERNAL float FANN_API fann_get_sarprop_temperature( struct fann * ann )
Set the sarprop_temperature.
FANN_EXTERNAL void FANN_API fann_set_sarprop_temperature( struct fann * ann, float sarprop_temperature )
Get the number of input neurons.
FANN_EXTERNAL unsigned int FANN_API fann_get_num_input( struct fann * ann )
Get the number of output neurons.
FANN_EXTERNAL unsigned int FANN_API fann_get_num_output( struct fann * ann )
Trains on an entire dataset, for a period of time using the Cascade2 training algorithm.
FANN_EXTERNAL void FANN_API fann_cascadetrain_on_data( struct fann * ann, struct fann_train_data * data, unsigned int max_neurons, unsigned int neurons_between_reports, float desired_error )