Index

G |
| get_activation_function, neural_net
| get_activation_steepness, neural_net
| get_bias_array, neural_net
| get_bit_fail, neural_net
| get_bit_fail_limit, neural_net
| get_cascade_activation_functions, neural_net
| get_cascade_activation_functions_count, neural_net
| get_cascade_activation_steepnesses, neural_net
| get_cascade_activation_steepnesses_count, neural_net
| get_cascade_candidate_change_fraction, neural_net
| get_cascade_candidate_limit, neural_net
| get_cascade_candidate_stagnation_epochs, neural_net
| get_cascade_max_cand_epochs, neural_net
| get_cascade_max_out_epochs, neural_net
| get_cascade_num_candidate_groups, neural_net
| get_cascade_num_candidates, neural_net
| get_cascade_output_change_fraction, neural_net
| get_cascade_output_stagnation_epochs, neural_net
| get_cascade_weight_multiplier, neural_net
| get_connection_array, neural_net
| get_connection_rate, neural_net
| get_decimal_point, neural_net
| get_errno, neural_net
| get_errstr, neural_net
| get_input, training_data
| get_layer_array, neural_net
| get_learning_momentum, neural_net
| get_learning_rate, neural_net
| get_MSE, neural_net
| get_multiplier, neural_net
| get_network_type, neural_net
| get_num_input, neural_net
| get_num_layers, neural_net
| get_num_output, neural_net
| get_output, training_data
| get_quickprop_decay, neural_net
| get_quickprop_mu, neural_net
| get_rprop_decrease_factor, neural_net
| get_rprop_delta_max, neural_net
| get_rprop_delta_min, neural_net
| get_rprop_increase_factor, neural_net
| get_total_connections, neural_net
| get_total_neurons, neural_net
| get_train_error_function, neural_net
| get_train_stop_function, neural_net
| get_training_algorithm, neural_net
I |
| init_weights, neural_net
L |
| LAYER, FANN
| length_train_data, training_data
M |
| merge_train_data, training_data
N |
| network_type_enum, FANN
| neural_net Global neural_net
~ | neural_net, neural_net
| num_input_train_data, training_data
| num_output_train_data, training_data
P |
| Parameters fann.h fann_cascade.h fann_train.h
| print_connections, neural_net
| print_error, neural_net
| print_parameters, neural_net
R |
| randomize_weights, neural_net
| read_train_from_file, training_data
| reset_errno, neural_net
| reset_errstr, neural_net
| reset_MSE, neural_net
| run, neural_net

> 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()

A pointer to the array of input training data

> 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.

> 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()

A pointer to the array of output training data

> 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_increase_factor()

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

> 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.

> 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.
Each layer only has connections to the next layer

> 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.
Definition of network types used by neural_net::get_network_type

> class neural_net

Encapsulation of a neural network struct fann and associated C API functions.

> 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.

> 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.
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