Function Index
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S
 save, neural_net
 save_to_fixed, neural_net
 save_train, training_data
 save_train_to_fixed, training_data
 scale_input, neural_net
 scale_input_train_data, training_data
 scale_output, neural_net
 scale_output_train_data, training_data
 scale_train, neural_net
 scale_train_data, training_data
 set_activation_function, neural_net
 set_activation_function_hidden, neural_net
 set_activation_function_layer, neural_net
 set_activation_function_output, neural_net
 set_activation_steepness, neural_net
 set_activation_steepness_hidden, neural_net
 set_activation_steepness_layer, neural_net
 set_activation_steepness_output, neural_net
 set_bit_fail_limit, neural_net
 set_callback, neural_net
 set_cascade_activation_functions, neural_net
 set_cascade_activation_steepnesses, neural_net
 set_cascade_candidate_change_fraction, neural_net
 set_cascade_candidate_limit, neural_net
 set_cascade_candidate_stagnation_epochs, neural_net
 set_cascade_max_cand_epochs, neural_net
 set_cascade_max_out_epochs, neural_net
 set_cascade_num_candidate_groups, neural_net
 set_cascade_output_change_fraction, neural_net
 set_cascade_output_stagnation_epochs, neural_net
 set_cascade_weight_multiplier, neural_net
 set_error_log, neural_net
 set_input_scaling_params, neural_net
 set_learning_momentum, neural_net
 set_learning_rate, neural_net
 set_output_scaling_params, neural_net
 set_quickprop_decay, neural_net
 set_quickprop_mu, neural_net
 set_rprop_decrease_factor, neural_net
 set_rprop_delta_max, neural_net
 set_rprop_delta_min, neural_net
 set_rprop_delta_zero, neural_net
 set_rprop_increase_factor, neural_net
 set_sarprop_step_error_shift, neural_net
 set_sarprop_step_error_threshold_factor, neural_net
 set_sarprop_temperature, neural_net
 set_sarprop_weight_decay_shift, neural_net
 set_scaling_params, neural_net
 set_train_data, training_data
 set_train_error_function, neural_net
 set_train_stop_function, neural_net
 set_training_algorithm, neural_net
 set_weight, neural_net
 set_weight_array, neural_net
 shuffle_train_data, training_data
 string&configuration_file), neural_net::neural_net(const std
 subset_train_data, training_data
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.
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 scale_input(fann_type *input_vector)
Scale data in input vector before feed it to ann based on previously calculated parameters.
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(fann_type *output_vector)
Scale data in output vector before feed it to ann based on previously calculated parameters.
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(training_data &data)
Scale input and output data based on previously calculated parameters.
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 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_hidden(
   activation_function_enum activation_function
)
Set the activation function for all of the hidden layers.
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_output(
   activation_function_enum activation_function
)
Set the activation function for the output layer.
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_hidden(fann_type steepness)
Set the steepness of the activation steepness in all of the hidden layers.
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_output(fann_type steepness)
Set the steepness of the activation steepness in the output layer.
void set_bit_fail_limit(fann_type bit_fail_limit)
Set the bit fail limit used during training.
void set_callback(callback_type callback,
void *user_data)
Sets the callback function for use during training.
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.
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_candidate_change_fraction(
   float cascade_candidate_change_fraction
)
Sets the cascade candidate change fraction.
void set_cascade_candidate_limit(fann_type cascade_candidate_limit)
Sets the candidate limit.
void set_cascade_candidate_stagnation_epochs(
   unsigned int cascade_candidate_stagnation_epochs
)
Sets the number of cascade candidate stagnation epochs.
void set_cascade_max_cand_epochs(unsigned int cascade_max_cand_epochs)
Sets the max candidate epochs.
void set_cascade_max_out_epochs(unsigned int cascade_max_out_epochs)
Sets the maximum out epochs.
void set_cascade_num_candidate_groups(
   unsigned int cascade_num_candidate_groups
)
Sets the number of candidate groups.
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.
void set_cascade_weight_multiplier(fann_type cascade_weight_multiplier)
Sets the weight multiplier.
void set_error_log(FILE *log_file)
Change where errors are logged to.
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.
void set_learning_momentum(float learning_momentum)
Set the learning momentum.
void set_learning_rate(float learning_rate)
Set the learning rate.
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.
void set_quickprop_decay(float quickprop_decay)
Sets the quickprop decay factor.
void set_quickprop_mu(float quickprop_mu)
Sets the quickprop mu factor.
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.
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.
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.
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.
void set_rprop_increase_factor(float rprop_increase_factor)
The increase factor used during RPROP training.
void set_sarprop_step_error_shift(float sarprop_step_error_shift)
Set the sarprop step error shift.
void set_sarprop_step_error_threshold_factor(
   float sarprop_step_error_threshold_factor
)
Set the sarprop step error threshold factor.
void set_sarprop_temperature(float sarprop_temperature)
Set the sarprop_temperature.
void set_sarprop_weight_decay_shift(float sarprop_weight_decay_shift)
Set the sarprop weight decay shift.
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.
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 set_train_error_function(error_function_enum train_error_function)
Set the error function used during training.
void set_train_stop_function(stop_function_enum train_stop_function)
Set the stop function used during training.
void set_training_algorithm(training_algorithm_enum training_algorithm)
Set the training algorithm.
void set_weight(unsigned int from_neuron,
unsigned int to_neuron,
fann_type weight)
Set a connection in the network.
void set_weight_array(connection *connections,
unsigned int num_connections)
Set connections in the network.
void shuffle_train_data()
Shuffles training data, randomizing the order.
neural_net(const std::string &configuration_file)
Constructs a backpropagation neural network from a configuration file, which have been saved by save.
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.
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