neural_net( float  connection_rate, unsigned  int  num_layers,   ... )

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

neural_net( network_type_enum  net_type, unsigned  int  num_layers,   ... )

Creates a neural network of the desired network_type_enum net_type.

void cascadetrain_on_data( const  training_data  & data, unsigned  int  max_neurons, unsigned  int  neurons_between_reports, float  desired_error )

Trains on an entire dataset, for a period of time using the Cascade2 training algorithm.

void cascadetrain_on_file( const std:: string  & filename, unsigned  int  max_neurons, unsigned  int  neurons_between_reports, float  desired_error )

Does the same as cascadetrain_on_data, but reads the training data directly from a file.

bool clear_scaling_params()

Clears scaling parameters.

Set the internal fann struct to a copy of other

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

Creates a standard backpropagation neural network, which is fully connected and which also has shortcut connections.

Just like create_shortcut, but with an array of layer sizes instead of individual parameters.

Creates a standard backpropagation neural network, which is not fully connected.

Just like create_sparse, but with an array of layer sizes instead of individual parameters.

Creates a standard fully connected backpropagation neural network.

Just like create_standard, but with an array of layer sizes instead of individual parameters.

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 *) )

Creates the training data struct from a user supplied function.

void descale_input( fann_type  * input_vector )

Scale data in input vector after get it from ann based on previously calculated parameters.

void descale_output( fann_type  * output_vector )

Scale data in output vector after get it from ann based on previously calculated parameters.

void descale_train( training_data  & data )

Descale input and output data based on previously calculated parameters.

void destroy()

Destructs the entire network.

void destroy_train()

Destructs the training data.

void disable_seed_rand()

Disables the automatic random generator seeding that happens in FANN.

void enable_seed_rand()

Enables the automatic random generator seeding that happens in FANN.