activation_function_enum get_activation_function( int  layer, int  neuron )

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

fann_type get_activation_steepness( int  layer, int  neuron )

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

void get_bias_array( unsigned  int  * bias )

Get the number of bias in each layer in the network.

unsigned int get_bit_fail()

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).

fann_type get_bit_fail_limit()

Returns the bit fail limit used during training.

activation_function_enum *get_cascade_activation_functions()

The cascade activation functions array is an array of the different activation functions used by the candidates.

unsigned int get_cascade_activation_functions_count()

The number of activation functions in the get_cascade_activation_functions array.

fann_type *get_cascade_activation_steepnesses()

The cascade activation steepnesses array is an array of the different activation functions used by the candidates.

unsigned int get_cascade_activation_steepnesses_count()

The number of activation steepnesses in the get_cascade_activation_functions array.

float get_cascade_candidate_change_fraction()

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.

fann_type get_cascade_candidate_limit()

The candidate limit is a limit for how much the candidate neuron may be trained.

unsigned int get_cascade_candidate_stagnation_epochs()

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_max_cand_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_out_epochs()

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_num_candidate_groups()

The number of candidate groups is the number of groups of identical candidates which will be used during training.

unsigned int get_cascade_num_candidates()

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).

float get_cascade_output_change_fraction()

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.

unsigned int get_cascade_output_stagnation_epochs()

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.

fann_type get_cascade_weight_multiplier()

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.

void get_connection_array( connection  * connections )

Get the connections in the network.

float get_connection_rate()

Get the connection rate used when the network was created

unsigned int get_decimal_point()

Returns the position of the decimal point in the ann.

unsigned int get_errno()

Returns the last error number.

std::string get_errstr()

Returns the last errstr.

fann_type **get_input()

Grant access to the encapsulated data since many situations and applications creates the data from sources other than files or uses the training data for testing and related functions

void get_layer_array( unsigned  int  * layers )

Get the number of neurons in each layer in the network.

float get_learning_momentum()

Get the learning momentum.

float get_learning_rate()

Return the learning rate.

fann_type get_max_input()

Get the maximum value of all in the input data

fann_type get_max_output()

Get the maximum value of all in the output data

fann_type get_min_input()

Get the minimum value of all in the input data

fann_type get_min_output()

Get the minimum value of all in the output data

float get_MSE()

Reads the mean square error from the network.

unsigned int get_multiplier()

Returns the multiplier that fix point data is multiplied with.

network_type_enum get_network_type()

Get the type of neural network it was created as.

unsigned int get_num_input()

Get the number of input neurons.

unsigned int get_num_layers()

Get the number of layers in the network

unsigned int get_num_output()

Get the number of output neurons.

fann_type **get_output()

Grant access to the encapsulated data since many situations and applications creates the data from sources other than files or uses the training data for testing and related functions

float get_quickprop_decay()

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_mu()

The mu factor is used to increase and decrease the step-size during quickprop 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.

float get_rprop_delta_max()

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

float get_rprop_delta_zero()

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

float get_rprop_increase_factor()

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

float get_sarprop_step_error_shift()

The get sarprop step error shift.

float get_sarprop_step_error_threshold_factor()

The sarprop step error threshold factor.

float get_sarprop_temperature()

The sarprop weight decay shift.

float get_sarprop_weight_decay_shift()

The sarprop weight decay shift.

unsigned int get_total_connections()

Get the total number of connections in the entire network.

unsigned int get_total_neurons()

Get the total number of neurons in the entire network.

error_function_enum get_train_error_function()

Returns the error function used during training.

fann_type *get_train_input( unsigned  int  position )

Gets the training input data at the given position

fann_type *get_train_output( unsigned  int  position )

Gets the training output data at the given position

stop_function_enum get_train_stop_function()

Returns the the stop function used during training.

training_algorithm_enum get_training_algorithm()

Return the training algorithm as described by FANN::training_algorithm_enum.

void init_weights( const  training_data  & data )

Initialize the weights using Widrow + Nguyen’s algorithm.

unsigned int length_train_data()

Returns the number of training patterns in the training_data.

void merge_train_data( const  training_data  & data )

Merges the data into the data contained in the training_data.

#ifdef USE_VIRTUAL_DESTRUCTOR virtual #endif ~neural_net()

Provides automatic cleanup of data.

neural_net( ) : ann(NULL)

Creates an empty neural net.

neural_net( float  connection_rate, unsigned  int  num_layers, const unsigned  int  * layers )

Creates a standard backpropagation neural network, which is sparsely connected, this will default the network_type_enum to LAYER

template <class InputIterator> neural_net( network_type_enum  net_type, InputIterator  layersBeginIterator, InputIterator  layersEndIterator )

Creates a neural network of the desired network_type_enum net_type, based on iterator to the layers.

neural_net( network_type_enum  net_type, unsigned  int  num_layers, const unsigned  int  * layers )

Creates a neural network of the desired network_type_enum net_type, based on array of layers.

neural_net( struct  fann  * other )

Creates a copy the other neural_net.

unsigned int num_input_train_data()

Returns the number of inputs in each of the training patterns in the training_data.

unsigned int num_output_train_data()

Returns the number of outputs in each of the training patterns in the struct fann_train_data.

void print_connections()

Will print the connections of the ann in a compact matrix, for easy viewing of the internals of the ann.

void print_error()

Prints the last error to stderr.

void print_parameters()

Prints all of the parameters and options of the neural network