Index
$#! · 0-9 · A · B · C · D · E · F · G · H · I · J · K · L · M · N · O · P · Q · R · S · T · U · V · W · X · Y · Z
F
 FANN
 FANN C++Datatypes
 FANN C++Training Data
 FANN C++Wrapper
 FANN Cascade Training
 FANN Creation/Execution
 FANN Datatypes
 FANN Error Handling
 FANN File Input/Output
 FANN Training
 fann_activationfunc_enum
 FANN_ACTIVATIONFUNC_NAMES
 fann_callback_type
 fann_cascadetrain_on_data
 fann_cascadetrain_on_file
 fann_clear_scaling_params
 fann_connection, struct fann
 fann_copy
 FANN_COS
 FANN_COS_SYMMETRIC
 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_array
 fann_create_train_from_callback
 fann_create_train_pointer_array
 fann_descale_input
 fann_descale_output
 fann_descale_train
 fann_destroy
 fann_destroy_train
 fann_disable_seed_rand
 fann_duplicate_train_data
 FANN_E_CANT_ALLOCATE_MEM
 FANN_E_CANT_OPEN_CONFIG_R
 FANN_E_CANT_OPEN_CONFIG_W
 FANN_E_CANT_OPEN_TD_R
 FANN_E_CANT_OPEN_TD_W
 FANN_E_CANT_READ_CONFIG
 FANN_E_CANT_READ_CONNECTIONS
 FANN_E_CANT_READ_NEURON
 FANN_E_CANT_READ_TD
 FANN_E_CANT_TRAIN_ACTIVATION
 FANN_E_CANT_USE_ACTIVATION
 FANN_E_CANT_USE_TRAIN_ALG
 FANN_E_INDEX_OUT_OF_BOUND
 FANN_E_INPUT_NO_MATCH
 FANN_E_NO_ERROR
 FANN_E_OUTPUT_NO_MATCH
 FANN_E_SCALE_NOT_PRESENT
 FANN_E_TRAIN_DATA_MISMATCH
 FANN_E_TRAIN_DATA_SUBSET
 FANN_E_WRONG_CONFIG_VERSION
 FANN_E_WRONG_NUM_CONNECTIONS
 FANN_E_WRONG_PARAMETERS_FOR_CREATE
 FANN_ELLIOT
 FANN_ELLIOT_SYMMETRIC
 fann_enable_seed_rand
 fann_errno_enum
 fann_errorfunc_enum
 FANN_ERRORFUNC_LINEAR
 FANN_ERRORFUNC_NAMES
 FANN_ERRORFUNC_TANH
 FANN_GAUSSIAN
 FANN_GAUSSIAN_SYMMETRIC
 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_max_train_input
 fann_get_max_train_output
 fann_get_min_train_input
 fann_get_min_train_output
 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_input
 fann_get_train_output
 fann_get_train_stop_function
 fann_get_training_algorithm
 fann_get_user_data
 fann_get_weights
 fann_init_weights
 fann_length_train_data
 FANN_LINEAR
 FANN_LINEAR_PIECE
 FANN_LINEAR_PIECE_SYMMETRIC
 fann_merge_train_data
 FANN_NETTYPE_LAYER
 FANN_NETTYPE_SHORTCUT
 fann_network_type_enum
 FANN_NETWORK_TYPE_NAMES
 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_set_weights
 fann_shuffle_train_data
 FANN_SIGMOID
 FANN_SIGMOID_STEPWISE
 FANN_SIGMOID_SYMMETRIC
 FANN_SIGMOID_SYMMETRIC_STEPWISE
 FANN_SIN
 FANN_SIN_SYMMETRIC
 FANN_STOPFUNC_BIT
 fann_stopfunc_enum
 FANN_STOPFUNC_MSE
 FANN_STOPFUNC_NAMES
 fann_subset_train_data
 fann_test
 fann_test_data
 FANN_THRESHOLD
 FANN_THRESHOLD_SYMMETRIC
 fann_train
 FANN_TRAIN_BATCH
 fann_train_enum
 fann_train_epoch
 FANN_TRAIN_INCREMENTAL
 FANN_TRAIN_NAMES
 fann_train_on_data
 fann_train_on_file
 FANN_TRAIN_QUICKPROP
 FANN_TRAIN_RPROP
 FANN_TRAIN_SARPROP
 fann_type
 File Input and Output
 Functions
namespace FANN
The FANN namespace groups the C++ wrapper definitions
This section includes enums and helper data types used by the two main classes neural_net and training_data
The Fann Wrapper for C++ provides two classes: neural_net and training_data.
Cascade training differs from ordinary training in the sense that it starts with an empty neural network and then adds neurons one by one, while it trains the neural network.
The FANN library is designed to be very easy to use.
The two main datatypes used in the fann library are struct fann, which represents an artificial neural network, and struct fann_train_data, which represents training data.
Errors from the fann library are usually reported on stderr.
It is possible to save an entire ann to a file with fann_save for future loading with fann_create_from_file.
There are many different ways of training neural networks and the FANN library supports a number of different approaches.
The activation functions used for the neurons during training.
Constant array consisting of the names for the activation function, so that the name of an activation function can be received by:
This callback function can be called during training when using fann_train_on_data, fann_train_on_file or fann_cascadetrain_on_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.
Describes a connection between two neurons and its weight
FANN_EXTERNAL struct fann * FANN_API fann_copy(struct fann *ann)
Creates a copy of a fann structure.
Periodical cosinus activation function.
Periodical cosinus activation function.
FANN_EXTERNAL struct fann *FANN_API fann_create_from_file(
   const char *configuration_file
)
Constructs a backpropagation neural network from a configuration file, which has 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 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_array(
   unsigned int num_data,
   unsigned int num_input,
   fann_type *input,
   unsigned int num_output,
   fann_type *output
)
input[num_data*num_input] output[num_data*num_output]
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 struct fann_train_data * FANN_API fann_create_train_pointer_array(
   unsigned int num_data,
   unsigned int num_input,
   fann_type **input,
   unsigned int num_output,
   fann_type **output
)
Creates an training data struct and fills it with data from provided arrays of pointer.
FANN_EXTERNAL void FANN_API fann_descale_input(struct fann *ann,
fann_type *input_vector)
Scale data in input vector after getting 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 getting 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, properly freeing all the associated memory.
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 void FANN_API fann_disable_seed_rand()
Disables the automatic random generator seeding that happens in FANN.
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.
Unable to allocate memory
Unable to open configuration file for reading
Unable to open configuration file for writing
Unable to open train data file for reading
Unable to open train data file for writing
Error reading info from configuration file
Error reading connections from configuration file
Error reading neuron info from configuration file
Error reading training data from file
Unable to train with the selected activation function
Unable to use the selected activation function
Unable to use the selected training algorithm
Index is out of bound
The number of input neurons in the ann and data don’t match
No error
The number of output neurons in the ann and data don’t match
Scaling parameters not present
Irreconcilable differences between two struct fann_train_data structures
Trying to take subset which is not within the training set
Wrong version of configuration file
Number of connections not equal to the number expected
The parameters for create_standard are wrong, either too few parameters provided or a negative/very high value provided
Fast (sigmoid like) activation function defined by David Elliott
Fast (symmetric sigmoid like) activation function defined by David Elliott
FANN_EXTERNAL void FANN_API fann_enable_seed_rand()
Enables the automatic random generator seeding that happens in FANN.
Used to define error events on struct fann and struct fann_train_data.
Error function used during training.
Standard linear error function.
Constant array consisting of the names for the training error functions, so that the name of an error function can be received by:
Tanh error function, usually better but can require a lower learning rate.
Gaussian activation function.
Symmetric gaussian activation function.
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 fann_type FANN_API fann_get_max_train_input(
   struct fann_train_data *train_data
)
Get the maximum value of all in the input data
FANN_EXTERNAL fann_type FANN_API fann_get_max_train_output(
   struct fann_train_data *train_data
)
Get the maximum value of all in the output data
FANN_EXTERNAL fann_type FANN_API fann_get_min_train_input(
   struct fann_train_data *train_data
)
Get the minimum value of all in the input data
FANN_EXTERNAL fann_type FANN_API fann_get_min_train_output(
   struct fann_train_data *train_data
)
Get the minimum value of all in the output data
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 fann_type * FANN_API fann_get_train_input(
   struct fann_train_data *data,
   unsigned int position
)
Gets the training input data at the given position
FANN_EXTERNAL fann_type * FANN_API fann_get_train_output(
   struct fann_train_data *data,
   unsigned int position
)
Gets the training output data at the given position
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_get_weights(struct fann *ann,
fann_type *weights)
Get all the network weights.
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.
Linear activation function.
Bounded linear activation function.
Bounded linear activation function.
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.
Each layer only has connections to the next layer
Each layer has connections to all following layers
Definition of network types used by fann_get_network_type
Constant array consisting of the names for the network types, so that the name of an network type can be received by:
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 feeding 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 feeding 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 for 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_set_weights(struct fann *ann,
fann_type *weights)
Set network weights.
FANN_EXTERNAL void FANN_API fann_shuffle_train_data(
   struct fann_train_data *train_data
)
Shuffles training data, randomizing the order.
Sigmoid activation function.
Stepwise linear approximation to sigmoid.
Symmetric sigmoid activation function, aka.
Stepwise linear approximation to symmetric sigmoid.
Periodical sinus activation function.
Periodical sinus activation function.
Stop criterion is number of bits that fail.
Stop criteria used during training.
Stop criterion is Mean Square Error (MSE) value.
Constant array consisting of the names for the training stop functions, so that the name of a stop function can be received by:
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.
Threshold activation function.
Threshold activation function.
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.
Standard backpropagation algorithm, where the weights are updated after calculating the mean square error for the whole training set.
The Training algorithms used when training on struct fann_train_data with functions like fann_train_on_data or fann_train_on_file.
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.
Standard backpropagation algorithm, where the weights are updated after each training pattern.
Constant array consisting of the names for the training algorithms, so that the name of an training function can be received by:
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.
A more advanced batch training algorithm which achieves good results for many problems.
A more advanced batch training algorithm which achieves good results for many problems.
THE SARPROP ALGORITHM: A SIMULATED ANNEALING ENHANCEMENT TO RESILIENT BACK PROPAGATION http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.47.8197&rep=rep1&type=pdf
fann_type is the type used for the weights, inputs and outputs of the neural network.
Close