Function Index

C |
| cascadetrain_on_data, neural_net
| cascadetrain_on_file, neural_net
| clear_scaling_params, neural_net
| copy_from_struct_fann, neural_net
| create_from_file, neural_net
| create_shortcut, neural_net
| create_shortcut_array, neural_net
| create_sparse, neural_net
| create_sparse_array, neural_net
| create_standard, neural_net
| create_standard_array, neural_net
| create_train_from_callback, training_data
D |
| descale_input, neural_net
| descale_output, neural_net
| descale_train, neural_net
| destroy neural_net training_data
F |
| fann_cascadetrain_on_data
| fann_cascadetrain_on_file
| fann_clear_scaling_params
| fann_copy
| fann_create_from_file
| fann_create_shortcut
| fann_create_shortcut_array
| fann_create_sparse
| fann_create_sparse_array
| fann_create_standard
| fann_create_standard_array
| fann_create_train
| fann_create_train_from_callback
| fann_descale_input
| fann_descale_output
| fann_descale_train
| fann_destroy
| fann_destroy_train
| fann_duplicate_train_data
| fann_get_activation_function
| fann_get_activation_steepness
| fann_get_bias_array
| fann_get_bit_fail
| fann_get_bit_fail_limit
| fann_get_cascade_activation_functions
| fann_get_cascade_activation_functions_count
| fann_get_cascade_activation_steepnesses
| fann_get_cascade_activation_steepnesses_count
| fann_get_cascade_candidate_change_fraction
| fann_get_cascade_candidate_limit
| fann_get_cascade_candidate_stagnation_epochs
| fann_get_cascade_max_cand_epochs
| fann_get_cascade_max_out_epochs
| fann_get_cascade_min_cand_epochs
| fann_get_cascade_min_out_epochs
| fann_get_cascade_num_candidate_groups
| fann_get_cascade_num_candidates
| fann_get_cascade_output_change_fraction
| fann_get_cascade_output_stagnation_epochs
| fann_get_cascade_weight_multiplier
| fann_get_connection_array
| fann_get_connection_rate
| fann_get_decimal_point
| fann_get_errno
| fann_get_errstr
| fann_get_layer_array
| fann_get_learning_momentum
| fann_get_learning_rate
| fann_get_MSE
| fann_get_multiplier
| fann_get_network_type
| fann_get_num_input
| fann_get_num_layers
| fann_get_num_output
| fann_get_quickprop_decay
| fann_get_quickprop_mu
| fann_get_rprop_decrease_factor
| fann_get_rprop_delta_max
| fann_get_rprop_delta_min
| fann_get_rprop_delta_zero
| fann_get_rprop_increase_factor
| fann_get_sarprop_step_error_shift
| fann_get_sarprop_step_error_threshold_factor
| fann_get_sarprop_temperature
| fann_get_sarprop_weight_decay_shift
| fann_get_total_connections
| fann_get_total_neurons
| fann_get_train_error_function
| fann_get_train_stop_function
| fann_get_training_algorithm
| fann_get_user_data
| fann_init_weights
| fann_length_train_data
| fann_merge_train_data
| fann_num_input_train_data
| fann_num_output_train_data
| fann_print_connections
| fann_print_error
| fann_print_parameters
| fann_randomize_weights
| fann_read_train_from_file
| fann_reset_errno
| fann_reset_errstr
| fann_reset_MSE
| fann_run
| fann_save
| fann_save_to_fixed
| fann_save_train
| fann_save_train_to_fixed
| fann_scale_input
| fann_scale_input_train_data
| fann_scale_output
| fann_scale_output_train_data
| fann_scale_train
| fann_scale_train_data
| fann_set_activation_function
| fann_set_activation_function_hidden
| fann_set_activation_function_layer
| fann_set_activation_function_output
| fann_set_activation_steepness
| fann_set_activation_steepness_hidden
| fann_set_activation_steepness_layer
| fann_set_activation_steepness_output
| fann_set_bit_fail_limit
| fann_set_callback
| fann_set_cascade_activation_functions
| fann_set_cascade_activation_steepnesses
| fann_set_cascade_candidate_change_fraction
| fann_set_cascade_candidate_limit
| fann_set_cascade_candidate_stagnation_epochs
| fann_set_cascade_max_cand_epochs
| fann_set_cascade_max_out_epochs
| fann_set_cascade_min_cand_epochs
| fann_set_cascade_min_out_epochs
| fann_set_cascade_num_candidate_groups
| fann_set_cascade_output_change_fraction
| fann_set_cascade_output_stagnation_epochs
| fann_set_cascade_weight_multiplier
| fann_set_error_log
| fann_set_input_scaling_params
| fann_set_learning_momentum
| fann_set_learning_rate
| fann_set_output_scaling_params
| fann_set_quickprop_decay
| fann_set_quickprop_mu
| fann_set_rprop_decrease_factor
| fann_set_rprop_delta_max
| fann_set_rprop_delta_min
| fann_set_rprop_delta_zero
| fann_set_rprop_increase_factor
| fann_set_sarprop_step_error_shift
| fann_set_sarprop_step_error_threshold_factor
| fann_set_sarprop_temperature
| fann_set_sarprop_weight_decay_shift
| fann_set_scaling_params
| fann_set_train_error_function
| fann_set_train_stop_function
| fann_set_training_algorithm
| fann_set_user_data
| fann_set_weight
| fann_set_weight_array
| fann_shuffle_train_data
| fann_subset_train_data
| fann_test
| fann_test_data
| fann_train
| fann_train_epoch
| fann_train_on_data
| fann_train_on_file

> 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.

> void copy_from_struct_fann( | struct | fann | * | other | )

Set the internal fann struct to a copy of other

> bool create_from_file( | const std:: | string | & | configuration_file | )

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

> bool create_shortcut( | unsigned | int | num_layers,
> |  |  | ... | )

Creates a standard backpropagation neural network, which is not fully connected and which also has shortcut connections.

> bool create_shortcut_array( | unsigned | int |  | num_layers,
> | const unsigned | int | * | layers | )

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

> bool create_sparse( |  | float | connection_rate,
> | unsigned | int | num_layers,
> |  |  | ... | )

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

> bool create_sparse_array( |  | float |  | connection_rate,
> | unsigned | int |  | num_layers,
> | const unsigned | int | * | layers | )

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

> bool create_standard( | unsigned | int | num_layers,
> |  |  | ... | )

Creates a standard fully connected backpropagation neural network.

> bool create_standard_array( | unsigned | int |  | num_layers,
> | const unsigned | int | * | layers | )

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.

> FANN_EXTERNAL void FANN_API fann_cascadetrain_on_data(
> | struct | fann | * | ann,
> | struct | fann_train_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.

> FANN_EXTERNAL void FANN_API fann_cascadetrain_on_file(
> | struct | fann | * | ann,
> | const | char | * | filename,
> | unsigned | int |  | max_neurons,
> | unsigned | int |  | neurons_between_reports,
> |  | float |  | desired_error
> )

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

> FANN_EXTERNAL int FANN_API fann_clear_scaling_params( | struct | fann | * | ann | )

Clears scaling parameters.

> FANN_EXTERNAL struct fann * FANN_API fann_copy( | struct | fann | * | ann | )

Creates a copy of a fann structure.

> FANN_EXTERNAL struct fann *FANN_API fann_create_from_file(
> | const | char | * | configuration_file
> )

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

> FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut(
> | unsigned | int | num_layers,
> |  |  | ...
> )

Creates a standard backpropagation neural network, which is not fully connected and which also has shortcut connections.

> FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut_array(
> | unsigned | int |  | num_layers,
> | const unsigned | int | * | layers
> )

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

> FANN_EXTERNAL struct fann *FANN_API fann_create_sparse(
> |  | float | connection_rate,
> | unsigned | int | num_layers,
> |  |  | ...
> )

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

> FANN_EXTERNAL struct fann *FANN_API fann_create_sparse_array(
> |  | float |  | connection_rate,
> | unsigned | int |  | num_layers,
> | const unsigned | int | * | layers
> )

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

> FANN_EXTERNAL struct fann *FANN_API fann_create_standard(
> | unsigned | int | num_layers,
> |  |  | ...
> )

Creates a standard fully connected backpropagation neural network.

> FANN_EXTERNAL struct fann *FANN_API fann_create_standard_array(
> | unsigned | int |  | num_layers,
> | const unsigned | int | * | layers
> )

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

> FANN_EXTERNAL struct fann_train_data * FANN_API fann_create_train(
> | unsigned | int | num_data,
> | unsigned | int | num_input,
> | unsigned | int | num_output
> )

Creates an empty training data struct.

> FANN_EXTERNAL struct fann_train_data * FANN_API fann_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.

> FANN_EXTERNAL void FANN_API fann_descale_input( | struct | fann | * | ann,
> |  | fann_type | * | input_vector | )

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

> FANN_EXTERNAL void FANN_API fann_descale_output( | struct | fann | * | ann,
> |  | fann_type | * | output_vector | )

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

> FANN_EXTERNAL void FANN_API fann_descale_train( | struct | fann | * | ann,
> | struct | fann_train_data | * | data | )

Descale input and output data based on previously calculated parameters.

> FANN_EXTERNAL void FANN_API fann_destroy( | struct | fann | * | ann | )

Destroys the entire network and properly freeing all the associated memmory.

> FANN_EXTERNAL void FANN_API fann_destroy_train(
> | struct | fann_train_data | * | train_data
> )

Destructs the training data and properly deallocates all of the associated data.

> FANN_EXTERNAL struct fann_train_data *FANN_API fann_duplicate_train_data(
> | struct | fann_train_data | * | data
> )

Returns an exact copy of a struct fann_train_data.

> FANN_EXTERNAL enum fann_activationfunc_enum FANN_API fann_get_activation_function(
> | struct | fann | * | ann,
> |  | int |  | layer,
> |  | int |  | neuron
> )

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

> FANN_EXTERNAL fann_type FANN_API fann_get_activation_steepness(
> | struct | fann | * | ann,
> |  | int |  | layer,
> |  | int |  | neuron
> )

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

> FANN_EXTERNAL void FANN_API fann_get_bias_array( | struct | fann | * | ann,
> | unsigned | int | * | bias | )

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

> FANN_EXTERNAL unsigned int FANN_API fann_get_bit_fail( | struct | fann | * | ann | )

The number of fail bits; means the number of output neurons which differ more than the bit fail limit (see fann_get_bit_fail_limit, fann_set_bit_fail_limit).

> FANN_EXTERNAL fann_type FANN_API fann_get_bit_fail_limit( | struct | fann | * | ann | )

Returns the bit fail limit used during training.

> FANN_EXTERNAL enum fann_activationfunc_enum * FANN_API fann_get_cascade_activation_functions(
> | struct | fann | * | ann
> )

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

> FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_activation_functions_count(
> | struct | fann | * | ann
> )

The number of activation functions in the fann_get_cascade_activation_functions array.

> FANN_EXTERNAL fann_type * FANN_API fann_get_cascade_activation_steepnesses(
> | struct | fann | * | ann
> )

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

> FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_activation_steepnesses_count(
> | struct | fann | * | ann
> )

The number of activation steepnesses in the fann_get_cascade_activation_functions array.

> FANN_EXTERNAL float FANN_API fann_get_cascade_candidate_change_fraction(
> | struct | fann | * | ann
> )

The cascade candidate change fraction is a number between 0 and 1 determining how large a fraction the fann_get_MSE value should change within fann_get_cascade_candidate_stagnation_epochs during training of the candidate neurons, in order for the training not to stagnate.

> FANN_EXTERNAL fann_type FANN_API fann_get_cascade_candidate_limit(
> | struct | fann | * | ann
> )

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

> FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_candidate_stagnation_epochs(
> | struct | fann | * | ann
> )

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 fann_get_cascade_candidate_change_fraction.

> FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_max_cand_epochs(
> | struct | fann | * | ann
> )

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.

> FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_max_out_epochs(
> | struct | fann | * | ann
> )

The maximum out epochs determines the maximum number of epochs the output connections may be trained after adding a new candidate neuron.

> FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_min_cand_epochs(
> | struct | fann | * | ann
> )

The minimum candidate epochs determines the minimum number of epochs the input connections to the candidates may be trained before adding a new candidate neuron.

> FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_min_out_epochs(
> | struct | fann | * | ann
> )

The minimum out epochs determines the minimum number of epochs the output connections must be trained after adding a new candidate neuron.

> FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_num_candidate_groups(
> | struct | fann | * | ann
> )

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

> FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_num_candidates(
> | struct | fann | * | ann
> )

The number of candidates used during training (calculated by multiplying fann_get_cascade_activation_functions_count, fann_get_cascade_activation_steepnesses_count and fann_get_cascade_num_candidate_groups).

> FANN_EXTERNAL float FANN_API fann_get_cascade_output_change_fraction(
> | struct | fann | * | ann
> )

The cascade output change fraction is a number between 0 and 1 determining how large a fraction the fann_get_MSE value should change within fann_get_cascade_output_stagnation_epochs during training of the output connections, in order for the training not to stagnate.

> FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_output_stagnation_epochs(
> | struct | fann | * | ann
> )

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 fann_get_cascade_output_change_fraction.

> FANN_EXTERNAL fann_type FANN_API fann_get_cascade_weight_multiplier(
> | struct | fann | * | ann
> )

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.

> FANN_EXTERNAL void FANN_API fann_get_connection_array(
> | struct | fann | * | ann,
> | struct | fann_connection | * | connections
> )

Get the connections in the network.

> FANN_EXTERNAL float FANN_API fann_get_connection_rate( | struct | fann | * | ann | )

Get the connection rate used when the network was created

> FANN_EXTERNAL unsigned int FANN_API fann_get_decimal_point( | struct | fann | * | ann | )

Returns the position of the decimal point in the ann.

> FANN_EXTERNAL enum fann_errno_enum FANN_API fann_get_errno(
> | struct | fann_error | * | errdat
> )

Returns the last error number.

> FANN_EXTERNAL char *FANN_API fann_get_errstr( | struct | fann_error | * | errdat | )

Returns the last errstr.

> FANN_EXTERNAL void FANN_API fann_get_layer_array( | struct | fann | * | ann,
> | unsigned | int | * | layers | )

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

> FANN_EXTERNAL float FANN_API fann_get_learning_momentum( | struct | fann | * | ann | )

Get the learning momentum.

> FANN_EXTERNAL float FANN_API fann_get_learning_rate( | struct | fann | * | ann | )

Return the learning rate.

> FANN_EXTERNAL float FANN_API fann_get_MSE( | struct | fann | * | ann | )

Reads the mean square error from the network.

> FANN_EXTERNAL unsigned int FANN_API fann_get_multiplier( | struct | fann | * | ann | )

returns the multiplier that fix point data is multiplied with.

> FANN_EXTERNAL enum fann_nettype_enum FANN_API fann_get_network_type(
> | struct | fann | * | ann
> )

Get the type of neural network it was created as.

> FANN_EXTERNAL unsigned int FANN_API fann_get_num_input( | struct | fann | * | ann | )

Get the number of input neurons.

> FANN_EXTERNAL unsigned int FANN_API fann_get_num_layers( | struct | fann | * | ann | )

Get the number of layers in the network

> FANN_EXTERNAL unsigned int FANN_API fann_get_num_output( | struct | fann | * | ann | )

Get the number of output neurons.

> FANN_EXTERNAL float FANN_API fann_get_quickprop_decay( | struct | fann | * | ann | )

The decay is a small negative valued number which is the factor that the weights should become smaller in each iteration during quickprop training.

> FANN_EXTERNAL float FANN_API fann_get_quickprop_mu( | struct | fann | * | ann | )

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

> FANN_EXTERNAL float FANN_API fann_get_rprop_decrease_factor( | struct | fann | * | ann | )

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

> FANN_EXTERNAL float FANN_API fann_get_rprop_delta_max( | struct | fann | * | ann | )

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

> FANN_EXTERNAL float FANN_API fann_get_rprop_delta_min( | struct | fann | * | ann | )

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

> FANN_EXTERNAL float FANN_API fann_get_rprop_delta_zero( | struct | fann | * | ann | )

The initial step-size is a positive number determining the initial step size.

> FANN_EXTERNAL float FANN_API fann_get_rprop_increase_factor( | struct | fann | * | ann | )

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

> FANN_EXTERNAL float FANN_API fann_get_sarprop_step_error_shift(
> | struct | fann | * | ann
> )

The get sarprop step error shift.

> FANN_EXTERNAL float FANN_API fann_get_sarprop_step_error_threshold_factor(
> | struct | fann | * | ann
> )

The sarprop step error threshold factor.

> FANN_EXTERNAL float FANN_API fann_get_sarprop_temperature( | struct | fann | * | ann | )

The sarprop weight decay shift.

> FANN_EXTERNAL float FANN_API fann_get_sarprop_weight_decay_shift(
> | struct | fann | * | ann
> )

The sarprop weight decay shift.

> FANN_EXTERNAL unsigned int FANN_API fann_get_total_connections(
> | struct | fann | * | ann
> )

Get the total number of connections in the entire network.

> FANN_EXTERNAL unsigned int FANN_API fann_get_total_neurons( | struct | fann | * | ann | )

Get the total number of neurons in the entire network.

> FANN_EXTERNAL enum fann_errorfunc_enum FANN_API fann_get_train_error_function(
> | struct | fann | * | ann
> )

Returns the error function used during training.

> FANN_EXTERNAL enum fann_stopfunc_enum FANN_API fann_get_train_stop_function(
> | struct | fann | * | ann
> )

Returns the the stop function used during training.

> FANN_EXTERNAL enum fann_train_enum FANN_API fann_get_training_algorithm(
> | struct | fann | * | ann
> )

Return the training algorithm as described by fann_train_enum.

> FANN_EXTERNAL void * FANN_API fann_get_user_data( | struct | fann | * | ann | )

Get a pointer to user defined data that was previously set with fann_set_user_data.

> 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.

> 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 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.

> 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.

> 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.

> 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.

> FANN_EXTERNAL void FANN_API fann_print_error( | struct | fann_error | * | errdat | )

Prints the last error to stderr.

> FANN_EXTERNAL void FANN_API fann_print_parameters( | struct | fann | * | ann | )

Prints all of the parameters and options of the ANN

> 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

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

Reads a file that stores training data.

> FANN_EXTERNAL void FANN_API fann_reset_errno( | struct | fann_error | * | errdat | )

Resets the last error number.

> FANN_EXTERNAL void FANN_API fann_reset_errstr( | struct | fann_error | * | errdat | )

Resets the last error string.

> FANN_EXTERNAL void FANN_API fann_reset_MSE( | struct | fann | * | ann | )

Resets the mean square error from the network.

> 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.

> FANN_EXTERNAL int FANN_API fann_save( | struct | fann | * | ann,
> | const | char | * | configuration_file | )

Save the entire network to a configuration file.

> FANN_EXTERNAL int FANN_API fann_save_to_fixed( | struct | fann | * | ann,
> | const | char | * | configuration_file | )

Saves the entire network to a configuration file.

> FANN_EXTERNAL int FANN_API fann_save_train( | struct | fann_train_data | * | data,
> | const | char | * | filename | )

Save the training structure to a file, with the format as specified in fann_read_train_from_file

> FANN_EXTERNAL int FANN_API fann_save_train_to_fixed(
> | struct | fann_train_data | * | data,
> | const | char | * | filename,
> | unsigned | int |  | decimal_point
> )

Saves the training structure to a fixed point data file.

> FANN_EXTERNAL void FANN_API fann_scale_input( | struct | fann | * | ann,
> |  | fann_type | * | input_vector | )

Scale data in input vector before feed it to ann based on previously calculated parameters.

> FANN_EXTERNAL void FANN_API fann_scale_input_train_data(
> | struct | fann_train_data | * | train_data,
> |  | fann_type |  | new_min,
> |  | fann_type |  | new_max
> )

Scales the inputs in the training data to the specified range.

> FANN_EXTERNAL void FANN_API fann_scale_output( | struct | fann | * | ann,
> |  | fann_type | * | output_vector | )

Scale data in output vector before feed it to ann based on previously calculated parameters.

> FANN_EXTERNAL void FANN_API fann_scale_output_train_data(
> | struct | fann_train_data | * | train_data,
> |  | fann_type |  | new_min,
> |  | fann_type |  | new_max
> )

Scales the outputs in the training data to the specified range.

> FANN_EXTERNAL void FANN_API fann_scale_train( | struct | fann | * | ann,
> | struct | fann_train_data | * | data | )

Scale input and output data based on previously calculated parameters.

> FANN_EXTERNAL void FANN_API fann_scale_train_data(
> | struct | fann_train_data | * | train_data,
> |  | fann_type |  | new_min,
> |  | fann_type |  | new_max
> )

Scales the inputs and outputs in the training data to the specified range.

> FANN_EXTERNAL void FANN_API fann_set_activation_function(
> | struct | fann | * | ann,
> | enum | fann_activationfunc_enum |  | activation_function,
> |  | int |  | layer,
> |  | int |  | neuron
> )

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

> FANN_EXTERNAL void FANN_API fann_set_activation_function_hidden(
> | struct | fann | * | ann,
> | enum | fann_activationfunc_enum |  | activation_function
> )

Set the activation function for all of the hidden layers.

> FANN_EXTERNAL void FANN_API fann_set_activation_function_layer(
> | struct | fann | * | ann,
> | enum | fann_activationfunc_enum |  | activation_function,
> |  | int |  | layer
> )

Set the activation function for all the neurons in the layer number layer, counting the input layer as layer 0.

> FANN_EXTERNAL void FANN_API fann_set_activation_function_output(
> | struct | fann | * | ann,
> | enum | fann_activationfunc_enum |  | activation_function
> )

Set the activation function for the output layer.

> FANN_EXTERNAL void FANN_API fann_set_activation_steepness( | struct | fann | * | ann,
> |  | fann_type |  | steepness,
> |  | int |  | layer,
> |  | int |  | neuron | )

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

> FANN_EXTERNAL void FANN_API fann_set_activation_steepness_hidden(
> | struct | fann | * | ann,
> |  | fann_type |  | steepness
> )

Set the steepness of the activation steepness in all of the hidden layers.

> FANN_EXTERNAL void FANN_API fann_set_activation_steepness_layer(
> | struct | fann | * | ann,
> |  | fann_type |  | steepness,
> |  | int |  | layer
> )

Set the activation steepness all of the neurons in layer number layer, counting the input layer as layer 0.

> FANN_EXTERNAL void FANN_API fann_set_activation_steepness_output(
> | struct | fann | * | ann,
> |  | fann_type |  | steepness
> )

Set the steepness of the activation steepness in the output layer.

> FANN_EXTERNAL void FANN_API fann_set_bit_fail_limit( | struct | fann | * | ann,
> |  | fann_type |  | bit_fail_limit | )

Set the bit fail limit used during training.

> FANN_EXTERNAL void FANN_API fann_set_callback( | struct | fann | * | ann,
> |  | fann_callback_type |  | callback | )

Sets the callback function for use during training.

> FANN_EXTERNAL void FANN_API fann_set_cascade_activation_functions(
> | struct | fann | * | ann,
> | enum | fann_activationfunc_enum | * | cascade_activation_functions,
> | unsigned | int |  | cascade_activation_functions_count
> )

Sets the array of cascade candidate activation functions.

> FANN_EXTERNAL void FANN_API fann_set_cascade_activation_steepnesses(
> | struct | fann | * | ann,
> |  | fann_type | * | cascade_activation_steepnesses,
> | unsigned | int |  | cascade_activation_steepnesses_count
> )

Sets the array of cascade candidate activation steepnesses.

> FANN_EXTERNAL void FANN_API fann_set_cascade_candidate_change_fraction(
> | struct | fann | * | ann,
> |  | float |  | cascade_candidate_change_fraction
> )

Sets the cascade candidate change fraction.

> FANN_EXTERNAL void FANN_API fann_set_cascade_candidate_limit(
> | struct | fann | * | ann,
> |  | fann_type |  | cascade_candidate_limit
> )

Sets the candidate limit.

> FANN_EXTERNAL void FANN_API fann_set_cascade_candidate_stagnation_epochs(
> | struct | fann | * | ann,
> | unsigned | int |  | cascade_candidate_stagnation_epochs
> )

Sets the number of cascade candidate stagnation epochs.

> FANN_EXTERNAL void FANN_API fann_set_cascade_max_cand_epochs(
> | struct | fann | * | ann,
> | unsigned | int |  | cascade_max_cand_epochs
> )

Sets the max candidate epochs.

> FANN_EXTERNAL void FANN_API fann_set_cascade_max_out_epochs(
> | struct | fann | * | ann,
> | unsigned | int |  | cascade_max_out_epochs
> )

Sets the maximum out epochs.

> FANN_EXTERNAL void FANN_API fann_set_cascade_min_cand_epochs(
> | struct | fann | * | ann,
> | unsigned | int |  | cascade_min_cand_epochs
> )

Sets the min candidate epochs.

> FANN_EXTERNAL void FANN_API fann_set_cascade_min_out_epochs(
> | struct | fann | * | ann,
> | unsigned | int |  | cascade_min_out_epochs
> )

Sets the minimum out epochs.

> FANN_EXTERNAL void FANN_API fann_set_cascade_num_candidate_groups(
> | struct | fann | * | ann,
> | unsigned | int |  | cascade_num_candidate_groups
> )

Sets the number of candidate groups.

> FANN_EXTERNAL void FANN_API fann_set_cascade_output_change_fraction(
> | struct | fann | * | ann,
> |  | float |  | cascade_output_change_fraction
> )

Sets the cascade output change fraction.

> FANN_EXTERNAL void FANN_API fann_set_cascade_output_stagnation_epochs(
> | struct | fann | * | ann,
> | unsigned | int |  | cascade_output_stagnation_epochs
> )

Sets the number of cascade output stagnation epochs.

> FANN_EXTERNAL void FANN_API fann_set_cascade_weight_multiplier(
> | struct | fann | * | ann,
> |  | fann_type |  | cascade_weight_multiplier
> )

Sets the weight multiplier.

> FANN_EXTERNAL void FANN_API fann_set_error_log( | struct | fann_error | * | errdat,
> |  | FILE | * | log_file | )

Change where errors are logged to.

> FANN_EXTERNAL int FANN_API fann_set_input_scaling_params(
> | struct | fann | * | ann,
> | const struct | fann_train_data | * | data,
> |  | float |  | new_input_min,
> |  | float |  | new_input_max
> )

Calculate input scaling parameters for future use based on training data.

> FANN_EXTERNAL void FANN_API fann_set_learning_momentum( | struct | fann | * | ann,
> |  | float |  | learning_momentum | )

Set the learning momentum.

> FANN_EXTERNAL void FANN_API fann_set_learning_rate( | struct | fann | * | ann,
> |  | float |  | learning_rate | )

Set the learning rate.

> FANN_EXTERNAL int FANN_API fann_set_output_scaling_params(
> | struct | fann | * | ann,
> | const struct | fann_train_data | * | data,
> |  | float |  | new_output_min,
> |  | float |  | new_output_max
> )

Calculate output scaling parameters for future use based on training data.

> FANN_EXTERNAL void FANN_API fann_set_quickprop_decay( | struct | fann | * | ann,
> |  | float |  | quickprop_decay | )

Sets the quickprop decay factor.

> FANN_EXTERNAL void FANN_API fann_set_quickprop_mu( | struct | fann | * | ann,
> |  | float |  | quickprop_mu | )

Sets the quickprop mu factor.

> FANN_EXTERNAL void FANN_API fann_set_rprop_decrease_factor(
> | struct | fann | * | ann,
> |  | float |  | rprop_decrease_factor
> )

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

> FANN_EXTERNAL void FANN_API fann_set_rprop_delta_max( | struct | fann | * | ann,
> |  | float |  | rprop_delta_max | )

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

> FANN_EXTERNAL void FANN_API fann_set_rprop_delta_min( | struct | fann | * | ann,
> |  | float |  | rprop_delta_min | )

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

> FANN_EXTERNAL void FANN_API fann_set_rprop_delta_zero( | struct | fann | * | ann,
> |  | float |  | rprop_delta_max | )

The initial step-size is a positive number determining the initial step size.

> FANN_EXTERNAL void FANN_API fann_set_rprop_increase_factor(
> | struct | fann | * | ann,
> |  | float |  | rprop_increase_factor
> )

The increase factor used during RPROP training.

> FANN_EXTERNAL void FANN_API fann_set_sarprop_step_error_shift(
> | struct | fann | * | ann,
> |  | float |  | sarprop_step_error_shift
> )

Set the sarprop step error shift.

> FANN_EXTERNAL void FANN_API fann_set_sarprop_step_error_threshold_factor(
> | struct | fann | * | ann,
> |  | float |  | sarprop_step_error_threshold_factor
> )

Set the sarprop step error threshold factor.

> FANN_EXTERNAL void FANN_API fann_set_sarprop_temperature(
> | struct | fann | * | ann,
> |  | float |  | sarprop_temperature
> )

Set the sarprop_temperature.

> FANN_EXTERNAL void FANN_API fann_set_sarprop_weight_decay_shift(
> | struct | fann | * | ann,
> |  | float |  | sarprop_weight_decay_shift
> )

Set the sarprop weight decay shift.

> FANN_EXTERNAL int FANN_API fann_set_scaling_params(
> | struct | fann | * | ann,
> | const struct | fann_train_data | * | data,
> |  | float |  | new_input_min,
> |  | float |  | new_input_max,
> |  | float |  | new_output_min,
> |  | float |  | new_output_max
> )

Calculate input and output scaling parameters for future use based on training data.

> FANN_EXTERNAL void FANN_API fann_set_train_error_function(
> | struct | fann | * | ann,
> | enum | fann_errorfunc_enum |  | train_error_function
> )

Set the error function used during training.

> FANN_EXTERNAL void FANN_API fann_set_train_stop_function(
> | struct | fann | * | ann,
> | enum | fann_stopfunc_enum |  | train_stop_function
> )

Set the stop function used during training.

> FANN_EXTERNAL void FANN_API fann_set_training_algorithm(
> | struct | fann | * | ann,
> | enum | fann_train_enum |  | training_algorithm
> )

Set the training algorithm.

> FANN_EXTERNAL void FANN_API fann_set_user_data( | struct | fann | * | ann,
> |  | void | * | user_data | )

Store a pointer to user defined data.

> FANN_EXTERNAL void FANN_API fann_set_weight( | struct | fann | * | ann,
> | unsigned | int |  | from_neuron,
> | unsigned | int |  | to_neuron,
> |  | fann_type |  | weight | )

Set a connection in the network.

> FANN_EXTERNAL void FANN_API fann_set_weight_array(
> | struct | fann | * | ann,
> | struct | fann_connection | * | connections,
> | unsigned | int |  | num_connections
> )

Set connections in the network.

> FANN_EXTERNAL void FANN_API fann_shuffle_train_data(
> | struct | fann_train_data | * | train_data
> )

Shuffles training data, randomizing the order.

> FANN_EXTERNAL struct fann_train_data *FANN_API fann_subset_train_data(
> | struct | fann_train_data | * | data,
> | unsigned | int |  | pos,
> | unsigned | int |  | length
> )

Returns an copy of a subset of the struct fann_train_data, starting at position pos and length elements forward.

> FANN_EXTERNAL fann_type * FANN_API fann_test( | struct | fann | * | ann,
> |  | fann_type | * | input,
> |  | fann_type | * | desired_output | )

Test with a set of inputs, and a set of desired outputs.

> FANN_EXTERNAL float FANN_API fann_test_data( | struct | fann | * | ann,
> | struct | fann_train_data | * | data | )

Test a set of training data and calculates the MSE for the training data.

> FANN_EXTERNAL void FANN_API fann_train( | struct | fann | * | ann,
> |  | fann_type | * | input,
> |  | fann_type | * | desired_output | )

Train one iteration with a set of inputs, and a set of desired outputs.

> FANN_EXTERNAL float FANN_API fann_train_epoch( | struct | fann | * | ann,
> | struct | fann_train_data | * | data | )

Train one epoch with a set of training data.

> FANN_EXTERNAL void FANN_API fann_train_on_data(
> | struct | fann | * | ann,
> | struct | fann_train_data | * | data,
> | unsigned | int |  | max_epochs,
> | unsigned | int |  | epochs_between_reports,
> |  | float |  | desired_error
> )

Trains on an entire dataset, for a period of time.

> FANN_EXTERNAL void FANN_API fann_train_on_file(
> | struct | fann | * | ann,
> | const | char | * | filename,
> | unsigned | int |  | max_epochs,
> | unsigned | int |  | epochs_between_reports,
> |  | float |  | desired_error
> )

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