{"diffoscope-json-version": 1, "source1": "/srv/reproducible-results/rbuild-debian/r-b-build.lcXhbKFn/b1/libfann_2.2.0+ds-8_armhf.changes", "source2": "/srv/reproducible-results/rbuild-debian/r-b-build.lcXhbKFn/b2/libfann_2.2.0+ds-8_armhf.changes", "unified_diff": null, "details": [{"source1": "Files", "source2": "Files", "unified_diff": "@@ -1,5 +1,5 @@\n \n 83ef94779fe106576f6af77eafbc6a08 83944 libdevel optional libfann-dev_2.2.0+ds-8_armhf.deb\n- b8aa51f214cfda23f4518d2d818329bb 1584736 doc optional libfann-doc_2.2.0+ds-8_all.deb\n+ e7328717ff60b3c6fc559c02bff71c31 1584764 doc optional libfann-doc_2.2.0+ds-8_all.deb\n bb6c0e296c2b8baf647bfbf4da8843da 232260 debug optional libfann2-dbgsym_2.2.0+ds-8_armhf.deb\n d51817408706f05b5a813fce43df18a3 53736 libs optional libfann2_2.2.0+ds-8_armhf.deb\n"}, {"source1": "libfann-doc_2.2.0+ds-8_all.deb", "source2": "libfann-doc_2.2.0+ds-8_all.deb", "unified_diff": null, "details": [{"source1": "file list", "source2": "file list", "unified_diff": "@@ -1,3 +1,3 @@\n -rw-r--r-- 0 0 0 4 2022-12-18 20:58:03.000000 debian-binary\n--rw-r--r-- 0 0 0 3488 2022-12-18 20:58:03.000000 control.tar.xz\n--rw-r--r-- 0 0 0 1581056 2022-12-18 20:58:03.000000 data.tar.xz\n+-rw-r--r-- 0 0 0 3492 2022-12-18 20:58:03.000000 control.tar.xz\n+-rw-r--r-- 0 0 0 1581080 2022-12-18 20:58:03.000000 data.tar.xz\n"}, {"source1": "control.tar.xz", "source2": "control.tar.xz", "unified_diff": null, "details": [{"source1": "control.tar", "source2": "control.tar", "unified_diff": null, "details": [{"source1": "./md5sums", "source2": "./md5sums", "unified_diff": null, "details": [{"source1": "./md5sums", "source2": "./md5sums", "comments": ["Files differ"], "unified_diff": null}]}]}]}, {"source1": "data.tar.xz", "source2": "data.tar.xz", "unified_diff": null, "details": [{"source1": "data.tar", "source2": "data.tar", "unified_diff": null, "details": [{"source1": "./usr/share/doc/libfann-dev/html/files/fann-h.html", "source2": "./usr/share/doc/libfann-dev/html/files/fann-h.html", "unified_diff": "@@ -89,15 +89,15 @@\n \n \n \n \n \n \n \n-\n \n \n \n \n
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 *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_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 void FANN_API fann_destroy(struct fann *ann)
Destroys the entire network and properly freeing all the associated memmory.
FANN_EXTERNAL struct fann * FANN_API fann_copy(struct fann *ann)
Creates a copy of a fann structure.
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 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 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 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_parameters(struct fann *ann)
Prints all of the parameters and options of the ANN
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_output(struct fann *ann)
Get the number of output neurons.
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 unsigned int FANN_API fann_get_total_connections(
   struct fann *ann
)
Get the total number of connections in the entire network.
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 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_num_layers(struct fann *ann)
Get the number of layers in the network
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 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 void FANN_API fann_get_connection_array(
   struct fann *ann,
   struct fann_connection *connections
)
Get the connections 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_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_user_data(struct fann *ann,
void *user_data)
Store a pointer to user defined data.
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 unsigned int FANN_API fann_get_decimal_point(struct fann *ann)
Returns the position of the decimal point in the ann.
FANN_EXTERNAL unsigned int FANN_API fann_get_multiplier(struct fann *ann)
returns the multiplier that fix point data is multiplied with.
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.
struct fann
The fast artificial neural network(fann) structure.
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 struct fann_train_data *FANN_API fann_read_train_from_file(
   const char *filename
)
Reads a file that stores training data.
Definition of network types used by fann_get_network_type
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_to_fixed(
   struct fann_train_data *data,
   const char *filename,
   unsigned int decimal_point
)
Saves the training structure to a fixed point data file.
\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -422,17 +422,17 @@\n FANN Creation/Execution\n _\bF_\bA_\bN_\bN_\b _\bD_\ba_\bt_\ba_\bt_\by_\bp_\be_\bs\n _\bF_\bA_\bN_\bN_\b _\bE_\br_\br_\bo_\br_\b _\bH_\ba_\bn_\bd_\bl_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bF_\bi_\bl_\be_\b _\bI_\bn_\bp_\bu_\bt_\b/_\bO_\bu_\bt_\bp_\bu_\bt\n _\bF_\bA_\bN_\bN_\b _\bT_\br_\ba_\bi_\bn_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bW_\br_\ba_\bp_\bp_\be_\br_\b _\bf_\bo_\br_\b _\bC_\b+_\b+\n _\bI_\bn_\bd_\be_\bx\n-_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bC_\bl_\ba_\bs_\bs_\be_\bs\n _\bC_\bo_\bn_\bs_\bt_\ba_\bn_\bt_\bs\n+_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bF_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n _\bT_\by_\bp_\be_\bs\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n FANN_EXTERNAL struct fann *FANN_API\n fann_create_standard(\n \u00a0\u00a0\u00a0 unsigned\u00a0 int\u00a0 num_layers,\n \u00a0\u00a0\u00a0 \u00a0 ...\n"}]}, {"source1": "./usr/share/doc/libfann-dev/html/files/fann_cascade-h.html", "source2": "./usr/share/doc/libfann-dev/html/files/fann_cascade-h.html", "unified_diff": "@@ -81,15 +81,15 @@\n \n \n \n \n \n \n \n-\n \n \n \n \n
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 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 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_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 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 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 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 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_stagnation_epochs(
   struct fann *ann,
   unsigned int cascade_candidate_stagnation_epochs
)
Sets the number of cascade candidate stagnation epochs.
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_set_cascade_weight_multiplier(
   struct fann *ann,
   fann_type cascade_weight_multiplier
)
Sets the weight multiplier.
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 void FANN_API fann_set_cascade_candidate_limit(
   struct fann *ann,
   fann_type cascade_candidate_limit
)
Sets the candidate limit.
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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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_num_candidate_groups(
   struct fann *ann,
   unsigned int cascade_num_candidate_groups
)
Sets the number of candidate groups.
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 void FANN_API fann_set_training_algorithm(
   struct fann *ann,
   enum fann_train_enum training_algorithm
)
Set the training algorithm.
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 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_callback(struct fann *ann,
fann_callback_type callback)
Sets the callback function for use during training.
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.
The activation functions used for the neurons during training.
\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -563,17 +563,17 @@\n _\bF_\bA_\bN_\bN_\b _\bC_\br_\be_\ba_\bt_\bi_\bo_\bn_\b/_\bE_\bx_\be_\bc_\bu_\bt_\bi_\bo_\bn\n _\bF_\bA_\bN_\bN_\b _\bD_\ba_\bt_\ba_\bt_\by_\bp_\be_\bs\n _\bF_\bA_\bN_\bN_\b _\bE_\br_\br_\bo_\br_\b _\bH_\ba_\bn_\bd_\bl_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bF_\bi_\bl_\be_\b _\bI_\bn_\bp_\bu_\bt_\b/_\bO_\bu_\bt_\bp_\bu_\bt\n _\bF_\bA_\bN_\bN_\b _\bT_\br_\ba_\bi_\bn_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bW_\br_\ba_\bp_\bp_\be_\br_\b _\bf_\bo_\br_\b _\bC_\b+_\b+\n _\bI_\bn_\bd_\be_\bx\n-_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bC_\bl_\ba_\bs_\bs_\be_\bs\n _\bC_\bo_\bn_\bs_\bt_\ba_\bn_\bt_\bs\n+_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bF_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n _\bT_\by_\bp_\be_\bs\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n FANN_EXTERNAL void FANN_API fann_cascadetrain_on_data(\n \u00a0\u00a0\u00a0 struct\u00a0 fann\u00a0 * ann,\n \u00a0\u00a0\u00a0 struct\u00a0 fann_train_data\u00a0 * data,\n \u00a0\u00a0\u00a0 unsigned\u00a0 int\u00a0 max_neurons,\n"}]}, {"source1": "./usr/share/doc/libfann-dev/html/files/fann_cpp-h.html", "source2": "./usr/share/doc/libfann-dev/html/files/fann_cpp-h.html", "unified_diff": "@@ -377,15 +377,15 @@\n \n \n \n \n \n \n \n-\n \n \n \n \n
class neural_net
Encapsulation of a neural network struct fann and associated C API functions.
class training_data
Encapsulation of a training data set struct fann_train_data and associated C API functions.
namespace FANN
The FANN namespace groups the C++ wrapper definitions
void train_on_data(const training_data &data,
unsigned int max_epochs,
unsigned int epochs_between_reports,
float desired_error)
Trains on an entire dataset, for a period of time.
void train_on_file(const std::string &filename,
unsigned int max_epochs,
unsigned int epochs_between_reports,
float desired_error)
Does the same as train_on_data, but reads the training data directly from a file.
network_type_enum get_network_type()
Get the type of neural network it was created as.
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.
struct fann_train_data
Structure used to store data, for use with training.
training_data() : train_data(NULL)
Default constructor creates an empty neural net.
#ifdef USE_VIRTUAL_DESTRUCTOR virtual #endif ~training_data()
Provides automatic cleanup of data.
void destroy_train()
Destructs the training data.
bool read_train_from_file(const std::string &filename)
Reads a file that stores training data.
bool save_train(const std::string &filename)
Save the training structure to a file, with the format as specified in read_train_from_file
bool save_train_to_fixed(const std::string &filename,
unsigned int decimal_point)
Saves the training structure to a fixed point data file.
void shuffle_train_data()
Shuffles training data, randomizing the order.
void merge_train_data(const training_data &data)
Merges the data into the data contained in the training_data.
unsigned int length_train_data()
Returns the number of training patterns in the training_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.
fann_type **get_input()
A pointer to the array of input training data
fann_type **get_output()
A pointer to the array of output training data
void set_train_data(unsigned int num_data,
unsigned int num_input,
fann_type **input,
unsigned int num_output,
fann_type **output)
Set the training data to the input and output data provided.
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 scale_input_train_data(fann_type new_min,
fann_type new_max)
Scales the inputs in the training data to the specified range.
void scale_output_train_data(fann_type new_min,
fann_type new_max)
Scales the outputs in the training data to the specified range.
void scale_train_data(fann_type new_min,
fann_type new_max)
Scales the inputs and outputs in the training data to the specified range.
void subset_train_data(unsigned int pos,
unsigned int length)
Changes the training data to a subset, starting at position pos and length elements forward.
struct fann
The fast artificial neural network(fann) structure.
neural_net() : ann(NULL)
Default constructor creates an empty neural net.
void copy_from_struct_fann(struct fann *other)
Set the internal fann struct to a copy of other
#ifdef USE_VIRTUAL_DESTRUCTOR virtual #endif ~neural_net()
Provides automatic cleanup of data.
void destroy()
Destructs the entire network.
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.
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_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.
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.
void randomize_weights(fann_type min_weight,
fann_type max_weight)
Give each connection a random weight between min_weight and max_weight
void init_weights(const training_data &data)
Initialize the weights using Widrow + Nguyen’s algorithm.
void print_connections()
Will print the connections of the ann in a compact matrix, for easy viewing of the internals of the ann.
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 save(const std::string &configuration_file)
Save the entire network to a configuration file.
int save_to_fixed(const std::string &configuration_file)
Saves the entire network to a configuration file.
void train(fann_type *input,
fann_type *desired_output)
Train one iteration with a set of inputs, and a set of desired outputs.
float train_epoch(const training_data &data)
Train one epoch with a set of training data.
fann_type * test(fann_type *input,
fann_type *desired_output)
Test with a set of inputs, and a set of desired outputs.
float test_data(const training_data &data)
Test a set of training data and calculates the MSE for the training data.
float get_MSE()
Reads the mean square error from the network.
void reset_MSE()
Resets the mean square error from the network.
void set_callback(callback_type callback,
void *user_data)
Sets the callback function for use during training.
void print_parameters()
Prints all of the parameters and options of the neural network
training_algorithm_enum get_training_algorithm()
Return the training algorithm as described by FANN::training_algorithm_enum.
The Training algorithms used when training on training_data with functions like neural_net::train_on_data or neural_net::train_on_file.
void set_training_algorithm(training_algorithm_enum training_algorithm)
Set the training algorithm.
float get_learning_rate()
Return the learning rate.
void set_learning_rate(float learning_rate)
Set the learning rate.
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.
void set_activation_function(activation_function_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.
void set_activation_function_layer(
   activation_function_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.
void set_activation_function_hidden(
   activation_function_enum activation_function
)
Set the activation function for all of the hidden layers.
void set_activation_function_output(
   activation_function_enum activation_function
)
Set the activation function for the output layer.
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 set_activation_steepness(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.
void set_activation_steepness_layer(fann_type steepness,
int layer)
Set the activation steepness all of the neurons in layer number layer, counting the input layer as layer 0.
void set_activation_steepness_hidden(fann_type steepness)
Set the steepness of the activation steepness in all of the hidden layers.
void set_activation_steepness_output(fann_type steepness)
Set the steepness of the activation steepness in the output layer.
error_function_enum get_train_error_function()
Returns the error function used during training.
void set_train_error_function(error_function_enum train_error_function)
Set the error function used during training.
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.
void set_quickprop_decay(float quickprop_decay)
Sets the quickprop decay factor.
float get_quickprop_mu()
The mu factor is used to increase and decrease the step-size during quickprop training.
void set_quickprop_mu(float quickprop_mu)
Sets the quickprop mu factor.
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.
void set_rprop_increase_factor(float rprop_increase_factor)
The increase factor used during RPROP 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.
void set_rprop_decrease_factor(float 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_zero()
The initial step-size is a small positive number determining how small the initial step-size may be.
void set_rprop_delta_zero(float rprop_delta_zero)
The initial step-size is a small positive number determining how small the initial 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.
void set_rprop_delta_min(float rprop_delta_min)
The minimum step-size is a small positive number determining how small the minimum step-size may be.
float get_rprop_delta_max()
The maximum step-size is a positive number determining how large the maximum step-size may be.
void set_rprop_delta_max(float rprop_delta_max)
The maximum step-size is a positive number determining how large the maximum step-size may be.
float get_sarprop_weight_decay_shift()
The sarprop weight decay shift.
void set_sarprop_weight_decay_shift(float sarprop_weight_decay_shift)
Set the sarprop weight decay shift.
float get_sarprop_step_error_threshold_factor()
The sarprop step error threshold factor.
void set_sarprop_step_error_threshold_factor(
   float sarprop_step_error_threshold_factor
)
Set the sarprop step error threshold factor.
float get_sarprop_step_error_shift()
The get sarprop step error shift.
void set_sarprop_step_error_shift(float sarprop_step_error_shift)
Set the sarprop step error shift.
float get_sarprop_temperature()
The sarprop weight decay shift.
void set_sarprop_temperature(float sarprop_temperature)
Set the sarprop_temperature.
unsigned int get_num_input()
Get the number of input neurons.
unsigned int get_num_output()
Get the number of output neurons.
unsigned int get_total_neurons()
Get the total number of neurons in the entire network.
unsigned int get_total_connections()
Get the total number of connections in the entire network.
unsigned int get_decimal_point()
Returns the position of the decimal point in the ann.
unsigned int get_multiplier()
Returns the multiplier that fix point data is multiplied with.
float get_connection_rate()
Get the connection rate used when the network was created
unsigned int get_num_layers()
Get the number of layers in the network
void get_layer_array(unsigned int *layers)
Get the number of neurons in each layer in the network.
void get_bias_array(unsigned int *bias)
Get the number of bias in each layer in the network.
void get_connection_array(connection *connections)
Get the connections in the network.
void set_weight_array(connection *connections,
unsigned int num_connections)
Set connections in the network.
void set_weight(unsigned int from_neuron,
unsigned int to_neuron,
fann_type weight)
Set a connection in the network.
float get_learning_momentum()
Get the learning momentum.
void set_learning_momentum(float learning_momentum)
Set the learning momentum.
stop_function_enum get_train_stop_function()
Returns the the stop function used during training.
void set_train_stop_function(stop_function_enum train_stop_function)
Set the stop function used during training.
fann_type get_bit_fail_limit()
Returns the bit fail limit used during training.
void set_bit_fail_limit(fann_type bit_fail_limit)
Set the bit fail limit used during training.
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).
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.
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.
void set_cascade_output_change_fraction(float cascade_output_change_fraction)
Sets the cascade output change fraction.
void set_cascade_output_stagnation_epochs(
   unsigned int cascade_output_stagnation_epochs
)
Sets the number of cascade output stagnation epochs.
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.
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.
void set_cascade_candidate_change_fraction(
   float cascade_candidate_change_fraction
)
Sets the cascade candidate change fraction.
void set_cascade_candidate_stagnation_epochs(
   unsigned int cascade_candidate_stagnation_epochs
)
Sets the number of cascade candidate stagnation epochs.
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 set_cascade_weight_multiplier(fann_type cascade_weight_multiplier)
Sets the weight multiplier.
fann_type get_cascade_candidate_limit()
The candidate limit is a limit for how much the candidate neuron may be trained.
void set_cascade_candidate_limit(fann_type cascade_candidate_limit)
Sets the candidate limit.
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.
void set_cascade_max_out_epochs(unsigned int cascade_max_out_epochs)
Sets the maximum out epochs.
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.
void set_cascade_max_cand_epochs(unsigned int cascade_max_cand_epochs)
Sets the max candidate epochs.
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).
unsigned int get_cascade_activation_functions_count()
The number of activation functions in the get_cascade_activation_functions array.
unsigned int get_cascade_activation_steepnesses_count()
The number of activation steepnesses in the get_cascade_activation_functions array.
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.
activation_function_enum * get_cascade_activation_functions()
The cascade activation functions array is an array of the different activation functions used by the candidates.
void set_cascade_activation_functions(
   activation_function_enum *cascade_activation_functions,
   unsigned int cascade_activation_functions_count
)
Sets the array of cascade candidate activation functions.
fann_type *get_cascade_activation_steepnesses()
The cascade activation steepnesses array is an array of the different activation functions used by the candidates.
void set_cascade_activation_steepnesses(
   fann_type *cascade_activation_steepnesses,
   unsigned int cascade_activation_steepnesses_count
)
Sets the array of cascade candidate activation steepnesses.
void set_cascade_num_candidate_groups(
   unsigned int cascade_num_candidate_groups
)
Sets the number of candidate groups.
void scale_train(training_data &data)
Scale input and output data based on previously calculated parameters.
void descale_train(training_data &data)
Descale input and output data based on previously calculated parameters.
bool set_input_scaling_params(const training_data &data,
float new_input_min,
float new_input_max)
Calculate scaling parameters for future use based on training data.
bool set_output_scaling_params(const training_data &data,
float new_output_min,
float new_output_max)
Calculate scaling parameters for future use based on training data.
bool set_scaling_params(const training_data &data,
float new_input_min,
float new_input_max,
float new_output_min,
float new_output_max)
Calculate scaling parameters for future use based on training data.
bool clear_scaling_params()
Clears scaling parameters.
void scale_input(fann_type *input_vector)
Scale data in input vector before feed it to ann based on previously calculated parameters.
void scale_output(fann_type *output_vector)
Scale data in output vector before feed it to ann based on previously calculated parameters.
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 set_error_log(FILE *log_file)
Change where errors are logged to.
unsigned int get_errno()
Returns the last error number.
void reset_errno()
Resets the last error number.
void reset_errstr()
Resets the last error string.
std::string get_errstr()
Returns the last errstr.
void print_error()
Prints the last error to stderr.
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.
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 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 struct fann_train_data *FANN_API fann_read_train_from_file(
   const char *filename
)
Reads a file that stores training data.
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 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 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 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_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_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_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 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 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 *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_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_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 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 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 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 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 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 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_output(struct fann *ann)
Get the number of output neurons.
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 void FANN_API fann_set_training_algorithm(
   struct fann *ann,
   enum fann_train_enum training_algorithm
)
Set the training algorithm.
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.
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 float FANN_API fann_get_MSE(struct fann *ann)
Reads 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.
This callback function can be called during training when using neural_net::train_on_data, neural_net::train_on_file or neural_net::cascadetrain_on_data.
FANN_EXTERNAL void FANN_API fann_print_parameters(struct fann *ann)
Prints all of the parameters and options of the ANN
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 float FANN_API fann_get_learning_rate(struct fann *ann)
Return the learning rate.
The activation functions used for the neurons during training.
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 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_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_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_output(
   struct fann *ann,
   enum fann_activationfunc_enum activation_function
)
Set the activation function for the output layer.
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_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_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_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_output(
   struct fann *ann,
   fann_type steepness
)
Set the steepness of the activation steepness in the output layer.
Error function used during training.
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 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 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 void FANN_API fann_set_quickprop_decay(struct fann *ann,
float quickprop_decay)
Sets the quickprop decay factor.
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 void FANN_API fann_set_quickprop_mu(struct fann *ann,
float quickprop_mu)
Sets the quickprop mu factor.
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 void FANN_API fann_set_rprop_increase_factor(
   struct fann *ann,
   float rprop_increase_factor
)
The increase factor used during RPROP 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 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 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 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 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 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 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 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_sarprop_weight_decay_shift(
   struct fann *ann,
   float sarprop_weight_decay_shift
)
Set the sarprop weight decay 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_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_temperature(
   struct fann *ann,
   float sarprop_temperature
)
Set the sarprop_temperature.
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 unsigned int FANN_API fann_get_multiplier(struct fann *ann)
returns the multiplier that fix point data is multiplied with.
Definition of network types used by neural_net::get_network_type
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_num_layers(struct fann *ann)
Get the number of layers in the network
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 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 void FANN_API fann_get_connection_array(
   struct fann *ann,
   struct fann_connection *connections
)
Get the connections 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_weight(struct fann *ann,
unsigned int from_neuron,
unsigned int to_neuron,
fann_type weight)
Set a connection in the network.
Stop criteria used during training.
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 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_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 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 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 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 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 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 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 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 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 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_set_cascade_weight_multiplier(
   struct fann *ann,
   fann_type cascade_weight_multiplier
)
Sets the weight multiplier.
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 void FANN_API fann_set_cascade_candidate_limit(
   struct fann *ann,
   fann_type cascade_candidate_limit
)
Sets the candidate limit.
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 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 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 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 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 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 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 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 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 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 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 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_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_descale_train(struct fann *ann,
struct fann_train_data *data)
Descale input and output data based on previously calculated parameters.
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 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 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 int FANN_API fann_clear_scaling_params(struct fann *ann)
Clears scaling parameters.
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_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_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.
struct fann_error
Structure used to store error-related information, both struct fann and struct fann_train_data can be casted to this type.
FANN_EXTERNAL void FANN_API fann_set_error_log(struct fann_error *errdat,
FILE *log_file)
Change where errors are logged to.
Used to define error events on struct fann and struct fann_train_data.
FANN_EXTERNAL void FANN_API fann_reset_errno(struct fann_error *errdat)
Resets the last error number.
FANN_EXTERNAL enum fann_errno_enum FANN_API fann_get_errno(
   struct fann_error *errdat
)
Returns the last error number.
FANN_EXTERNAL void FANN_API fann_reset_errstr(struct fann_error *errdat)
Resets the last error string.
\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -2412,17 +2412,17 @@\n _\bF_\bA_\bN_\bN_\b _\bC_\br_\be_\ba_\bt_\bi_\bo_\bn_\b/_\bE_\bx_\be_\bc_\bu_\bt_\bi_\bo_\bn\n _\bF_\bA_\bN_\bN_\b _\bD_\ba_\bt_\ba_\bt_\by_\bp_\be_\bs\n _\bF_\bA_\bN_\bN_\b _\bE_\br_\br_\bo_\br_\b _\bH_\ba_\bn_\bd_\bl_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bF_\bi_\bl_\be_\b _\bI_\bn_\bp_\bu_\bt_\b/_\bO_\bu_\bt_\bp_\bu_\bt\n _\bF_\bA_\bN_\bN_\b _\bT_\br_\ba_\bi_\bn_\bi_\bn_\bg\n FANN Wrapper for C++\n _\bI_\bn_\bd_\be_\bx\n-_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bC_\bl_\ba_\bs_\bs_\be_\bs\n _\bC_\bo_\bn_\bs_\bt_\ba_\bn_\bt_\bs\n+_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bF_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n _\bT_\by_\bp_\be_\bs\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n class neural_net\n Encapsulation of a neural network struct fann and associated C API functions.\n class training_data\n Encapsulation of a training data set struct fann_train_data and associated C\n"}]}, {"source1": "./usr/share/doc/libfann-dev/html/files/fann_data-h.html", "source2": "./usr/share/doc/libfann-dev/html/files/fann_data-h.html", "unified_diff": "@@ -62,15 +62,15 @@\n \n \n \n \n \n \n \n-\n \n \n \n \n
struct fann
The fast artificial neural network(fann) structure.
struct fann_train_data
Structure used to store data, for use with training.
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.
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 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.
struct fann_error
Structure used to store error-related information, both struct fann and struct fann_train_data can be casted to this type.
FANN_EXTERNAL void FANN_API fann_set_training_algorithm(
   struct fann *ann,
   enum fann_train_enum training_algorithm
)
Set the training algorithm.
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.
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 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_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_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_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_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_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_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.
The activation functions used for the neurons during training.
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 enum fann_errorfunc_enum FANN_API fann_get_train_error_function(
   struct fann *ann
)
Returns the error function used during training.
Error function used during training.
FANN_EXTERNAL fann_type FANN_API fann_get_bit_fail_limit(struct fann *ann)
Returns the bit fail limit used during training.
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_train_stop_function(
   struct fann *ann,
   enum fann_stopfunc_enum train_stop_function
)
Set the stop 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.
Stop criteria 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_error_log(struct fann_error *errdat,
FILE *log_file)
Change where errors are logged to.
FANN_EXTERNAL enum fann_errno_enum FANN_API fann_get_errno(
   struct fann_error *errdat
)
Returns the last error number.
FANN_EXTERNAL struct fann *FANN_API fann_create_standard(
   unsigned int num_layers,
    ...
)
Creates a standard fully connected backpropagation neural network.
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_get_connection_array(
   struct fann *ann,
   struct fann_connection *connections
)
Get the connections 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.
\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -306,17 +306,17 @@\n _\bF_\bA_\bN_\bN_\b _\bC_\br_\be_\ba_\bt_\bi_\bo_\bn_\b/_\bE_\bx_\be_\bc_\bu_\bt_\bi_\bo_\bn\n FANN Datatypes\n _\bF_\bA_\bN_\bN_\b _\bE_\br_\br_\bo_\br_\b _\bH_\ba_\bn_\bd_\bl_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bF_\bi_\bl_\be_\b _\bI_\bn_\bp_\bu_\bt_\b/_\bO_\bu_\bt_\bp_\bu_\bt\n _\bF_\bA_\bN_\bN_\b _\bT_\br_\ba_\bi_\bn_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bW_\br_\ba_\bp_\bp_\be_\br_\b _\bf_\bo_\br_\b _\bC_\b+_\b+\n _\bI_\bn_\bd_\be_\bx\n-_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bC_\bl_\ba_\bs_\bs_\be_\bs\n _\bC_\bo_\bn_\bs_\bt_\ba_\bn_\bt_\bs\n+_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bF_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n _\bT_\by_\bp_\be_\bs\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n struct fann\n The fast artificial neural network(fann) structure.\n struct fann_train_data\n Structure used to store data, for use with training.\n"}]}, {"source1": "./usr/share/doc/libfann-dev/html/files/fann_error-h.html", "source2": "./usr/share/doc/libfann-dev/html/files/fann_error-h.html", "unified_diff": "@@ -31,15 +31,15 @@\n \n \n \n \n \n \n \n-\n \n \n \n \n
struct fann
The fast artificial neural network(fann) structure.
struct fann_train_data
Structure used to store data, for use with training.
FANN_EXTERNAL void FANN_API fann_set_error_log(struct fann_error *errdat,
FILE *log_file)
Change where errors are logged to.
FANN_EXTERNAL enum fann_errno_enum FANN_API fann_get_errno(
   struct fann_error *errdat
)
Returns the last error number.
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 char *FANN_API fann_get_errstr(struct fann_error *errdat)
Returns the last errstr.
FANN_EXTERNAL void FANN_API fann_print_error(struct fann_error *errdat)
Prints the last error to stderr.
struct fann_error
Structure used to store error-related information, both struct fann and struct fann_train_data can be casted to this type.
Used to define error events on struct fann and struct fann_train_data.
\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -98,17 +98,17 @@\n _\bF_\bA_\bN_\bN_\b _\bC_\br_\be_\ba_\bt_\bi_\bo_\bn_\b/_\bE_\bx_\be_\bc_\bu_\bt_\bi_\bo_\bn\n _\bF_\bA_\bN_\bN_\b _\bD_\ba_\bt_\ba_\bt_\by_\bp_\be_\bs\n FANN Error Handling\n _\bF_\bA_\bN_\bN_\b _\bF_\bi_\bl_\be_\b _\bI_\bn_\bp_\bu_\bt_\b/_\bO_\bu_\bt_\bp_\bu_\bt\n _\bF_\bA_\bN_\bN_\b _\bT_\br_\ba_\bi_\bn_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bW_\br_\ba_\bp_\bp_\be_\br_\b _\bf_\bo_\br_\b _\bC_\b+_\b+\n _\bI_\bn_\bd_\be_\bx\n-_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bC_\bl_\ba_\bs_\bs_\be_\bs\n _\bC_\bo_\bn_\bs_\bt_\ba_\bn_\bt_\bs\n+_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bF_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n _\bT_\by_\bp_\be_\bs\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n struct fann\n The fast artificial neural network(fann) structure.\n struct fann_train_data\n Structure used to store data, for use with training.\n"}]}, {"source1": "./usr/share/doc/libfann-dev/html/files/fann_io-h.html", "source2": "./usr/share/doc/libfann-dev/html/files/fann_io-h.html", "unified_diff": "@@ -23,15 +23,15 @@\n \n \n \n \n \n \n \n-\n \n \n \n \n
FANN_EXTERNAL int FANN_API fann_save(struct fann *ann,
const char *configuration_file)
Save the entire network to a configuration file.
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 int FANN_API fann_save_to_fixed(struct fann *ann,
const char *configuration_file)
Saves the entire network to a configuration file.
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_error_log(struct fann_error *errdat,
FILE *log_file)
Change where errors are logged to.
FANN_EXTERNAL void FANN_API fann_set_user_data(struct fann *ann,
void *user_data)
Store a pointer to user defined data.
FANN_EXTERNAL float FANN_API fann_get_MSE(struct fann *ann)
Reads the mean square error from the network.
\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -65,17 +65,17 @@\n _\bF_\bA_\bN_\bN_\b _\bC_\br_\be_\ba_\bt_\bi_\bo_\bn_\b/_\bE_\bx_\be_\bc_\bu_\bt_\bi_\bo_\bn\n _\bF_\bA_\bN_\bN_\b _\bD_\ba_\bt_\ba_\bt_\by_\bp_\be_\bs\n _\bF_\bA_\bN_\bN_\b _\bE_\br_\br_\bo_\br_\b _\bH_\ba_\bn_\bd_\bl_\bi_\bn_\bg\n FANN File Input/Output\n _\bF_\bA_\bN_\bN_\b _\bT_\br_\ba_\bi_\bn_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bW_\br_\ba_\bp_\bp_\be_\br_\b _\bf_\bo_\br_\b _\bC_\b+_\b+\n _\bI_\bn_\bd_\be_\bx\n-_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bC_\bl_\ba_\bs_\bs_\be_\bs\n _\bC_\bo_\bn_\bs_\bt_\ba_\bn_\bt_\bs\n+_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bF_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n _\bT_\by_\bp_\be_\bs\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n FANN_EXTERNAL int FANN_API struct\u00a0 fann\u00a0 * ann,\n fann_save(\n const\u00a0 char\u00a0 * configuration_file )\n Save the entire network to a configuration file.\n"}]}, {"source1": "./usr/share/doc/libfann-dev/html/files/fann_train-h.html", "source2": "./usr/share/doc/libfann-dev/html/files/fann_train-h.html", "unified_diff": "@@ -196,15 +196,15 @@\n \n \n \n \n \n \n \n-\n \n \n \n \n
struct fann_train_data
Structure used to store data, for use with training.
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 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_get_MSE(struct fann *ann)
Reads the mean square error from 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 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_reset_MSE(struct fann *ann)
Resets the mean square error from the network.
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.
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 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 struct fann_train_data *FANN_API fann_read_train_from_file(
   const char *filename
)
Reads a file that stores training data.
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_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_shuffle_train_data(
   struct fann_train_data *train_data
)
Shuffles training data, randomizing the order.
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_descale_train(struct fann *ann,
struct fann_train_data *data)
Descale input and output data based on previously calculated parameters.
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 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 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 int FANN_API fann_clear_scaling_params(struct fann *ann)
Clears scaling parameters.
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_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_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_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_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_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 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 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 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 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 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 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 enum fann_train_enum FANN_API fann_get_training_algorithm(
   struct fann *ann
)
Return the training algorithm as described by fann_train_enum.
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 void FANN_API fann_set_training_algorithm(
   struct fann *ann,
   enum fann_train_enum training_algorithm
)
Set the training algorithm.
FANN_EXTERNAL float FANN_API fann_get_learning_rate(struct fann *ann)
Return the learning rate.
FANN_EXTERNAL void FANN_API fann_set_learning_rate(struct fann *ann,
float learning_rate)
Set the learning rate.
FANN_EXTERNAL float FANN_API fann_get_learning_momentum(struct fann *ann)
Get the learning momentum.
FANN_EXTERNAL void FANN_API fann_set_learning_momentum(struct fann *ann,
float learning_momentum)
Set the learning momentum.
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 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_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_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_output(
   struct fann *ann,
   enum fann_activationfunc_enum activation_function
)
Set the activation function for the output layer.
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_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_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_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_output(
   struct fann *ann,
   fann_type steepness
)
Set the steepness of the activation steepness in the output layer.
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 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 enum fann_stopfunc_enum FANN_API fann_get_train_stop_function(
   struct fann *ann
)
Returns the the stop 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_callback(struct fann *ann,
fann_callback_type callback)
Sets the callback function for use during training.
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 void FANN_API fann_set_quickprop_decay(struct fann *ann,
float quickprop_decay)
Sets the quickprop decay factor.
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 void FANN_API fann_set_quickprop_mu(struct fann *ann,
float quickprop_mu)
Sets the quickprop mu factor.
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 void FANN_API fann_set_rprop_increase_factor(
   struct fann *ann,
   float rprop_increase_factor
)
The increase factor used during RPROP 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 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 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 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 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 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 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 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 float FANN_API fann_get_sarprop_weight_decay_shift(
   struct fann *ann
)
The sarprop weight decay shift.
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 float FANN_API fann_get_sarprop_step_error_threshold_factor(
   struct fann *ann
)
The sarprop step error threshold factor.
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 float FANN_API fann_get_sarprop_step_error_shift(
   struct fann *ann
)
The get sarprop step error shift.
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 float FANN_API fann_get_sarprop_temperature(struct fann *ann)
The sarprop weight decay shift.
FANN_EXTERNAL void FANN_API fann_set_sarprop_temperature(
   struct fann *ann,
   float sarprop_temperature
)
Set the sarprop_temperature.
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.
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_output(struct fann *ann)
Get the number of output neurons.
Stop criteria used during training.
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.
The activation functions used for the neurons during training.
Error function used during training.
This callback function can be called during training when using fann_train_on_data, fann_train_on_file or fann_cascadetrain_on_data.
\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -1426,17 +1426,17 @@\n _\bF_\bA_\bN_\bN_\b _\bC_\br_\be_\ba_\bt_\bi_\bo_\bn_\b/_\bE_\bx_\be_\bc_\bu_\bt_\bi_\bo_\bn\n _\bF_\bA_\bN_\bN_\b _\bD_\ba_\bt_\ba_\bt_\by_\bp_\be_\bs\n _\bF_\bA_\bN_\bN_\b _\bE_\br_\br_\bo_\br_\b _\bH_\ba_\bn_\bd_\bl_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bF_\bi_\bl_\be_\b _\bI_\bn_\bp_\bu_\bt_\b/_\bO_\bu_\bt_\bp_\bu_\bt\n FANN Training\n _\bF_\bA_\bN_\bN_\b _\bW_\br_\ba_\bp_\bp_\be_\br_\b _\bf_\bo_\br_\b _\bC_\b+_\b+\n _\bI_\bn_\bd_\be_\bx\n-_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bC_\bl_\ba_\bs_\bs_\be_\bs\n _\bC_\bo_\bn_\bs_\bt_\ba_\bn_\bt_\bs\n+_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bF_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n _\bT_\by_\bp_\be_\bs\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n struct fann_train_data\n Structure used to store data, for use with training.\n FANN_EXTERNAL void FANN_API struct\u00a0 fann\u00a0 * ann,\n fann_train(\n"}]}, {"source1": "./usr/share/doc/libfann-dev/html/index/Classes.html", "source2": "./usr/share/doc/libfann-dev/html/index/Classes.html", "unified_diff": "@@ -29,15 +29,15 @@\n \n \n \n \n \n \n \n-\n \n \n
Close
\n \n \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -30,14 +30,14 @@\n _\bF_\bA_\bN_\bN_\b _\bC_\br_\be_\ba_\bt_\bi_\bo_\bn_\b/_\bE_\bx_\be_\bc_\bu_\bt_\bi_\bo_\bn\n _\bF_\bA_\bN_\bN_\b _\bD_\ba_\bt_\ba_\bt_\by_\bp_\be_\bs\n _\bF_\bA_\bN_\bN_\b _\bE_\br_\br_\bo_\br_\b _\bH_\ba_\bn_\bd_\bl_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bF_\bi_\bl_\be_\b _\bI_\bn_\bp_\bu_\bt_\b/_\bO_\bu_\bt_\bp_\bu_\bt\n _\bF_\bA_\bN_\bN_\b _\bT_\br_\ba_\bi_\bn_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bW_\br_\ba_\bp_\bp_\be_\br_\b _\bf_\bo_\br_\b _\bC_\b+_\b+\n _\bI_\bn_\bd_\be_\bx\n-_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n Classes\n _\bC_\bo_\bn_\bs_\bt_\ba_\bn_\bt_\bs\n+_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bF_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n _\bT_\by_\bp_\be_\bs\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n _\bC_\bl_\bo_\bs_\be\n"}]}, {"source1": "./usr/share/doc/libfann-dev/html/index/Constants.html", "source2": "./usr/share/doc/libfann-dev/html/index/Constants.html", "unified_diff": "@@ -33,15 +33,15 @@\n \n \n \n \n \n \n \n-\n \n \n
Close
\n \n \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -176,14 +176,14 @@\n _\bF_\bA_\bN_\bN_\b _\bC_\br_\be_\ba_\bt_\bi_\bo_\bn_\b/_\bE_\bx_\be_\bc_\bu_\bt_\bi_\bo_\bn\n _\bF_\bA_\bN_\bN_\b _\bD_\ba_\bt_\ba_\bt_\by_\bp_\be_\bs\n _\bF_\bA_\bN_\bN_\b _\bE_\br_\br_\bo_\br_\b _\bH_\ba_\bn_\bd_\bl_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bF_\bi_\bl_\be_\b _\bI_\bn_\bp_\bu_\bt_\b/_\bO_\bu_\bt_\bp_\bu_\bt\n _\bF_\bA_\bN_\bN_\b _\bT_\br_\ba_\bi_\bn_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bW_\br_\ba_\bp_\bp_\be_\br_\b _\bf_\bo_\br_\b _\bC_\b+_\b+\n _\bI_\bn_\bd_\be_\bx\n-_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bC_\bl_\ba_\bs_\bs_\be_\bs\n Constants\n+_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bF_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n _\bT_\by_\bp_\be_\bs\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n _\bC_\bl_\bo_\bs_\be\n"}]}, {"source1": "./usr/share/doc/libfann-dev/html/index/Functions.html", "source2": "./usr/share/doc/libfann-dev/html/index/Functions.html", "unified_diff": "@@ -25,15 +25,15 @@\n \n \n \n \n \n \n \n-\n \n \n
Close
\n \n \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -1073,14 +1073,14 @@\n _\bF_\bA_\bN_\bN_\b _\bC_\br_\be_\ba_\bt_\bi_\bo_\bn_\b/_\bE_\bx_\be_\bc_\bu_\bt_\bi_\bo_\bn\n _\bF_\bA_\bN_\bN_\b _\bD_\ba_\bt_\ba_\bt_\by_\bp_\be_\bs\n _\bF_\bA_\bN_\bN_\b _\bE_\br_\br_\bo_\br_\b _\bH_\ba_\bn_\bd_\bl_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bF_\bi_\bl_\be_\b _\bI_\bn_\bp_\bu_\bt_\b/_\bO_\bu_\bt_\bp_\bu_\bt\n _\bF_\bA_\bN_\bN_\b _\bT_\br_\ba_\bi_\bn_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bW_\br_\ba_\bp_\bp_\be_\br_\b _\bf_\bo_\br_\b _\bC_\b+_\b+\n _\bI_\bn_\bd_\be_\bx\n-_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bC_\bl_\ba_\bs_\bs_\be_\bs\n _\bC_\bo_\bn_\bs_\bt_\ba_\bn_\bt_\bs\n+_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n Functions\n _\bT_\by_\bp_\be_\bs\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n _\bC_\bl_\bo_\bs_\be\n"}]}, {"source1": "./usr/share/doc/libfann-dev/html/index/Functions2.html", "source2": "./usr/share/doc/libfann-dev/html/index/Functions2.html", "unified_diff": "@@ -41,15 +41,15 @@\n \n \n \n \n \n \n \n-\n \n \n
Close
\n \n \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -253,14 +253,14 @@\n _\bF_\bA_\bN_\bN_\b _\bC_\br_\be_\ba_\bt_\bi_\bo_\bn_\b/_\bE_\bx_\be_\bc_\bu_\bt_\bi_\bo_\bn\n _\bF_\bA_\bN_\bN_\b _\bD_\ba_\bt_\ba_\bt_\by_\bp_\be_\bs\n _\bF_\bA_\bN_\bN_\b _\bE_\br_\br_\bo_\br_\b _\bH_\ba_\bn_\bd_\bl_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bF_\bi_\bl_\be_\b _\bI_\bn_\bp_\bu_\bt_\b/_\bO_\bu_\bt_\bp_\bu_\bt\n _\bF_\bA_\bN_\bN_\b _\bT_\br_\ba_\bi_\bn_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bW_\br_\ba_\bp_\bp_\be_\br_\b _\bf_\bo_\br_\b _\bC_\b+_\b+\n _\bI_\bn_\bd_\be_\bx\n-_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bC_\bl_\ba_\bs_\bs_\be_\bs\n _\bC_\bo_\bn_\bs_\bt_\ba_\bn_\bt_\bs\n+_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n Functions\n _\bT_\by_\bp_\be_\bs\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n _\bC_\bl_\bo_\bs_\be\n"}]}, {"source1": "./usr/share/doc/libfann-dev/html/index/Functions3.html", "source2": "./usr/share/doc/libfann-dev/html/index/Functions3.html", "unified_diff": "@@ -17,15 +17,15 @@\n \n \n \n \n \n \n \n-\n \n \n
Close
\n \n \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -250,14 +250,14 @@\n _\bF_\bA_\bN_\bN_\b _\bC_\br_\be_\ba_\bt_\bi_\bo_\bn_\b/_\bE_\bx_\be_\bc_\bu_\bt_\bi_\bo_\bn\n _\bF_\bA_\bN_\bN_\b _\bD_\ba_\bt_\ba_\bt_\by_\bp_\be_\bs\n _\bF_\bA_\bN_\bN_\b _\bE_\br_\br_\bo_\br_\b _\bH_\ba_\bn_\bd_\bl_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bF_\bi_\bl_\be_\b _\bI_\bn_\bp_\bu_\bt_\b/_\bO_\bu_\bt_\bp_\bu_\bt\n _\bF_\bA_\bN_\bN_\b _\bT_\br_\ba_\bi_\bn_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bW_\br_\ba_\bp_\bp_\be_\br_\b _\bf_\bo_\br_\b _\bC_\b+_\b+\n _\bI_\bn_\bd_\be_\bx\n-_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bC_\bl_\ba_\bs_\bs_\be_\bs\n _\bC_\bo_\bn_\bs_\bt_\ba_\bn_\bt_\bs\n+_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n Functions\n _\bT_\by_\bp_\be_\bs\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n _\bC_\bl_\bo_\bs_\be\n"}]}, {"source1": "./usr/share/doc/libfann-dev/html/index/Functions4.html", "source2": "./usr/share/doc/libfann-dev/html/index/Functions4.html", "unified_diff": "@@ -17,15 +17,15 @@\n \n \n \n \n \n \n \n-\n \n \n
Close
\n \n \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -41,14 +41,14 @@\n _\bF_\bA_\bN_\bN_\b _\bC_\br_\be_\ba_\bt_\bi_\bo_\bn_\b/_\bE_\bx_\be_\bc_\bu_\bt_\bi_\bo_\bn\n _\bF_\bA_\bN_\bN_\b _\bD_\ba_\bt_\ba_\bt_\by_\bp_\be_\bs\n _\bF_\bA_\bN_\bN_\b _\bE_\br_\br_\bo_\br_\b _\bH_\ba_\bn_\bd_\bl_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bF_\bi_\bl_\be_\b _\bI_\bn_\bp_\bu_\bt_\b/_\bO_\bu_\bt_\bp_\bu_\bt\n _\bF_\bA_\bN_\bN_\b _\bT_\br_\ba_\bi_\bn_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bW_\br_\ba_\bp_\bp_\be_\br_\b _\bf_\bo_\br_\b _\bC_\b+_\b+\n _\bI_\bn_\bd_\be_\bx\n-_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bC_\bl_\ba_\bs_\bs_\be_\bs\n _\bC_\bo_\bn_\bs_\bt_\ba_\bn_\bt_\bs\n+_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n Functions\n _\bT_\by_\bp_\be_\bs\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n _\bC_\bl_\bo_\bs_\be\n"}]}, {"source1": "./usr/share/doc/libfann-dev/html/index/General.html", "source2": "./usr/share/doc/libfann-dev/html/index/General.html", "unified_diff": "@@ -29,15 +29,15 @@\n \n \n \n \n \n \n \n-\n \n \n
Close
\n \n \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -113,14 +113,14 @@\n _\bF_\bA_\bN_\bN_\b _\bC_\br_\be_\ba_\bt_\bi_\bo_\bn_\b/_\bE_\bx_\be_\bc_\bu_\bt_\bi_\bo_\bn\n _\bF_\bA_\bN_\bN_\b _\bD_\ba_\bt_\ba_\bt_\by_\bp_\be_\bs\n _\bF_\bA_\bN_\bN_\b _\bE_\br_\br_\bo_\br_\b _\bH_\ba_\bn_\bd_\bl_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bF_\bi_\bl_\be_\b _\bI_\bn_\bp_\bu_\bt_\b/_\bO_\bu_\bt_\bp_\bu_\bt\n _\bF_\bA_\bN_\bN_\b _\bT_\br_\ba_\bi_\bn_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bW_\br_\ba_\bp_\bp_\be_\br_\b _\bf_\bo_\br_\b _\bC_\b+_\b+\n _\bI_\bn_\bd_\be_\bx\n-Everything\n _\bC_\bl_\ba_\bs_\bs_\be_\bs\n _\bC_\bo_\bn_\bs_\bt_\ba_\bn_\bt_\bs\n+Everything\n _\bF_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n _\bT_\by_\bp_\be_\bs\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n _\bC_\bl_\bo_\bs_\be\n"}]}, {"source1": "./usr/share/doc/libfann-dev/html/index/General2.html", "source2": "./usr/share/doc/libfann-dev/html/index/General2.html", "unified_diff": "@@ -17,15 +17,15 @@\n \n \n \n \n \n \n \n-\n \n \n
Close
\n \n \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -1174,14 +1174,14 @@\n _\bF_\bA_\bN_\bN_\b _\bC_\br_\be_\ba_\bt_\bi_\bo_\bn_\b/_\bE_\bx_\be_\bc_\bu_\bt_\bi_\bo_\bn\n _\bF_\bA_\bN_\bN_\b _\bD_\ba_\bt_\ba_\bt_\by_\bp_\be_\bs\n _\bF_\bA_\bN_\bN_\b _\bE_\br_\br_\bo_\br_\b _\bH_\ba_\bn_\bd_\bl_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bF_\bi_\bl_\be_\b _\bI_\bn_\bp_\bu_\bt_\b/_\bO_\bu_\bt_\bp_\bu_\bt\n _\bF_\bA_\bN_\bN_\b _\bT_\br_\ba_\bi_\bn_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bW_\br_\ba_\bp_\bp_\be_\br_\b _\bf_\bo_\br_\b _\bC_\b+_\b+\n _\bI_\bn_\bd_\be_\bx\n-Everything\n _\bC_\bl_\ba_\bs_\bs_\be_\bs\n _\bC_\bo_\bn_\bs_\bt_\ba_\bn_\bt_\bs\n+Everything\n _\bF_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n _\bT_\by_\bp_\be_\bs\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n _\bC_\bl_\bo_\bs_\be\n"}]}, {"source1": "./usr/share/doc/libfann-dev/html/index/General3.html", "source2": "./usr/share/doc/libfann-dev/html/index/General3.html", "unified_diff": "@@ -41,15 +41,15 @@\n \n \n \n \n \n \n \n-\n \n \n
Close
\n \n \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -262,14 +262,14 @@\n _\bF_\bA_\bN_\bN_\b _\bC_\br_\be_\ba_\bt_\bi_\bo_\bn_\b/_\bE_\bx_\be_\bc_\bu_\bt_\bi_\bo_\bn\n _\bF_\bA_\bN_\bN_\b _\bD_\ba_\bt_\ba_\bt_\by_\bp_\be_\bs\n _\bF_\bA_\bN_\bN_\b _\bE_\br_\br_\bo_\br_\b _\bH_\ba_\bn_\bd_\bl_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bF_\bi_\bl_\be_\b _\bI_\bn_\bp_\bu_\bt_\b/_\bO_\bu_\bt_\bp_\bu_\bt\n _\bF_\bA_\bN_\bN_\b _\bT_\br_\ba_\bi_\bn_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bW_\br_\ba_\bp_\bp_\be_\br_\b _\bf_\bo_\br_\b _\bC_\b+_\b+\n _\bI_\bn_\bd_\be_\bx\n-Everything\n _\bC_\bl_\ba_\bs_\bs_\be_\bs\n _\bC_\bo_\bn_\bs_\bt_\ba_\bn_\bt_\bs\n+Everything\n _\bF_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n _\bT_\by_\bp_\be_\bs\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n _\bC_\bl_\bo_\bs_\be\n"}]}, {"source1": "./usr/share/doc/libfann-dev/html/index/General4.html", "source2": "./usr/share/doc/libfann-dev/html/index/General4.html", "unified_diff": "@@ -17,15 +17,15 @@\n \n \n \n \n \n \n \n-\n \n \n
Close
\n \n \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -268,14 +268,14 @@\n _\bF_\bA_\bN_\bN_\b _\bC_\br_\be_\ba_\bt_\bi_\bo_\bn_\b/_\bE_\bx_\be_\bc_\bu_\bt_\bi_\bo_\bn\n _\bF_\bA_\bN_\bN_\b _\bD_\ba_\bt_\ba_\bt_\by_\bp_\be_\bs\n _\bF_\bA_\bN_\bN_\b _\bE_\br_\br_\bo_\br_\b _\bH_\ba_\bn_\bd_\bl_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bF_\bi_\bl_\be_\b _\bI_\bn_\bp_\bu_\bt_\b/_\bO_\bu_\bt_\bp_\bu_\bt\n _\bF_\bA_\bN_\bN_\b _\bT_\br_\ba_\bi_\bn_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bW_\br_\ba_\bp_\bp_\be_\br_\b _\bf_\bo_\br_\b _\bC_\b+_\b+\n _\bI_\bn_\bd_\be_\bx\n-Everything\n _\bC_\bl_\ba_\bs_\bs_\be_\bs\n _\bC_\bo_\bn_\bs_\bt_\ba_\bn_\bt_\bs\n+Everything\n _\bF_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n _\bT_\by_\bp_\be_\bs\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n _\bC_\bl_\bo_\bs_\be\n"}]}, {"source1": "./usr/share/doc/libfann-dev/html/index/General5.html", "source2": "./usr/share/doc/libfann-dev/html/index/General5.html", "unified_diff": "@@ -17,15 +17,15 @@\n \n \n \n \n \n \n \n-\n \n \n
Close
\n \n \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -65,14 +65,14 @@\n _\bF_\bA_\bN_\bN_\b _\bC_\br_\be_\ba_\bt_\bi_\bo_\bn_\b/_\bE_\bx_\be_\bc_\bu_\bt_\bi_\bo_\bn\n _\bF_\bA_\bN_\bN_\b _\bD_\ba_\bt_\ba_\bt_\by_\bp_\be_\bs\n _\bF_\bA_\bN_\bN_\b _\bE_\br_\br_\bo_\br_\b _\bH_\ba_\bn_\bd_\bl_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bF_\bi_\bl_\be_\b _\bI_\bn_\bp_\bu_\bt_\b/_\bO_\bu_\bt_\bp_\bu_\bt\n _\bF_\bA_\bN_\bN_\b _\bT_\br_\ba_\bi_\bn_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bW_\br_\ba_\bp_\bp_\be_\br_\b _\bf_\bo_\br_\b _\bC_\b+_\b+\n _\bI_\bn_\bd_\be_\bx\n-Everything\n _\bC_\bl_\ba_\bs_\bs_\be_\bs\n _\bC_\bo_\bn_\bs_\bt_\ba_\bn_\bt_\bs\n+Everything\n _\bF_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n _\bT_\by_\bp_\be_\bs\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n _\bC_\bl_\bo_\bs_\be\n"}]}, {"source1": "./usr/share/doc/libfann-dev/html/index/Types.html", "source2": "./usr/share/doc/libfann-dev/html/index/Types.html", "unified_diff": "@@ -41,15 +41,15 @@\n \n \n \n \n \n \n \n-\n \n \n
Close
\n \n \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -50,14 +50,14 @@\n _\bF_\bA_\bN_\bN_\b _\bC_\br_\be_\ba_\bt_\bi_\bo_\bn_\b/_\bE_\bx_\be_\bc_\bu_\bt_\bi_\bo_\bn\n _\bF_\bA_\bN_\bN_\b _\bD_\ba_\bt_\ba_\bt_\by_\bp_\be_\bs\n _\bF_\bA_\bN_\bN_\b _\bE_\br_\br_\bo_\br_\b _\bH_\ba_\bn_\bd_\bl_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bF_\bi_\bl_\be_\b _\bI_\bn_\bp_\bu_\bt_\b/_\bO_\bu_\bt_\bp_\bu_\bt\n _\bF_\bA_\bN_\bN_\b _\bT_\br_\ba_\bi_\bn_\bi_\bn_\bg\n _\bF_\bA_\bN_\bN_\b _\bW_\br_\ba_\bp_\bp_\be_\br_\b _\bf_\bo_\br_\b _\bC_\b+_\b+\n _\bI_\bn_\bd_\be_\bx\n-_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bC_\bl_\ba_\bs_\bs_\be_\bs\n _\bC_\bo_\bn_\bs_\bt_\ba_\bn_\bt_\bs\n+_\bE_\bv_\be_\br_\by_\bt_\bh_\bi_\bn_\bg\n _\bF_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n Types\n [Search ][One of: Everything/Classes/Constants/Functions/Types]\n _\bC_\bl_\bo_\bs_\be\n"}]}]}]}]}]}