void init_weights( const  training_data  & data )

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 )

Initialize the weights using Widrow + Nguyen’s algorithm.

unsigned int length_train_data()

Returns the number of training patterns in the training_data.

FANN_EXTERNAL unsigned int FANN_API fann_length_train_data(     struct  fann_train_data  * data )

Returns the number of training patterns in the struct fann_train_data.

FANN_EXTERNAL void FANN_API MAKE_NAME(     neuron_compute_MSE )(struct fann *ann, struct fann_neuron *neuron, fann_type *desired_output)

Compute the error on a MIMO Neuron after forward propagation of a certain input vector i.e.

FANN_EXTERNAL int FANN_API MAKE_NAME(     layer_constructor )(struct fann *ann, struct fann_layer *layer, struct fann_layer_descr *descr)

Allocates room inside the layer for neurons and connections.

FANN_EXTERNAL void FANN_API MAKE_NAME(     layer_destructor )(struct fann_layer* layer)

destroy the layer allocated data

FANN_EXTERNAL void FANN_API MAKE_NAME(     layer_run )(struct fann *ann, struct fann_layer* layer)

Iterates over all neurons and calls the respective run functions.

FANN_EXTERNAL void FANN_API MAKE_NAME(     layer_train_initialize )(struct fann *ann, struct fann_layer *layer)

Allocates room inside the layer for training data structures.

FANN_EXTERNAL void FANN_API MAKE_NAME(     neuron_backprop )(struct fann *ann, struct fann_neuron *neuron, fann_type *prev_layer_errors)

Train the MIMO neuron: this function backpropagates the error to the previous layer and computes the weight update.

FANN_EXTERNAL void FANN_API MAKE_NAME(     neuron_compute_MSE )(struct fann *ann, struct fann_neuron *neuron, fann_type *desired_output)

Compute the error on a MIMO Neuron after forward propagation of a certain input vector i.e.

FANN_EXTERNAL int FANN_API MAKE_NAME(     neuron_constructor )(struct fann *ann, struct fann_layer *layer, struct fann_neuron *neuron, struct fann_neuron_descr * descr)

Allocates room inside the neuron for the connections.

FANN_EXTERNAL void FANN_API MAKE_NAME(     neuron_destructor )(struct fann_neuron* neuron)

destroy the neuron allocated data

FANN_EXTERNAL void FANN_API MAKE_NAME(     neuron_run )(struct fann *ann, struct fann_neuron *neuron)

Does a forward iteration on the neuron.

FANN_EXTERNAL void FANN_API MAKE_NAME(     neuron_train_initialize )(struct fann *ann, struct fann_layer *layer, struct fann_neuron *neuron)

Allocates room inside the neuron for training data structures.

FANN_EXTERNAL void FANN_API MAKE_NAME(     neuron_update )(struct fann *ann, struct fann_neuron *neuron)

Apply the training on the MIMO neuron: the weight update stored in the neuron is applied.

void merge_train_data( const  training_data  & data )

Merges the data into the data contained in the training_data.

FANN_EXTERNAL struct fann_train_data *FANN_API fann_merge_train_data(     struct  fann_train_data  * data1,     struct  fann_train_data  * data2 )

Merges the data from data1 and data2 into a new struct fann_train_data.

neural_net( ) : ann(NULL)

Default constructor creates an empty neural net.

#ifdef USE_VIRTUAL_DESTRUCTOR virtual #endif ~neural_net()

Provides automatic cleanup of data.

unsigned int num_input_train_data()

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

FANN_EXTERNAL unsigned int FANN_API fann_num_input_train_data(     struct  fann_train_data  * data )

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

unsigned int num_output_train_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 )

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.

FANN_EXTERNAL void FANN_API fann_print_connections( struct  fann  * ann )

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.

FANN_EXTERNAL void FANN_API fann_print_error( struct  fann_error  * errdat )

Prints the last error to stderr.

void print_parameters()

Prints all of the parameters and options of the neural network

FANN_EXTERNAL void FANN_API fann_print_parameters( struct  fann  * ann )

Prints all of the parameters and options of the ANN

void randomize_weights( fann_type  min_weight, fann_type  max_weight )

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 )

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.

FANN_EXTERNAL struct fann_train_data *FANN_API fann_read_train_from_file(     const  char  * filename )

Reads a file that stores training data.

void reset_errno()

Resets the last error number.

FANN_EXTERNAL void FANN_API fann_reset_errno( struct  fann_error  * errdat )

Resets the last error number.

void reset_errstr()

Resets the last error string.

FANN_EXTERNAL void FANN_API fann_reset_errstr( struct  fann_error  * errdat )

Resets the last error string.

void reset_MSE()

Resets the mean square error from the network.

FANN_EXTERNAL void FANN_API fann_reset_MSE( struct  fann  * ann )

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.

FANN_EXTERNAL fann_type * FANN_API fann_run( struct  fann  * ann, 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.