#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

void randomize_weights( fann_type  min_weight, fann_type  max_weight )

Give each connection a random weight between min_weight and max_weight

bool read_train_from_file( const std:: string  & filename )

Reads a file that stores training data.

void reset_errno()

Resets the last error number.

void reset_errstr()

Resets the last error string.

void reset_MSE()

Resets the mean square error from the network.

fann_type *run( fann_type  * input )

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.