FANN (Fast Artificial Neural Network)
- fann_cascadetrain_on_data - Trains on an entire dataset, for a period of time using the Cascade2 training algorithm
- fann_cascadetrain_on_file - Trains on an entire dataset read from file, for a period of time using the Cascade2 training algorithm
- fann_clear_scaling_params - Clears scaling parameters
- fann_copy - Creates a copy of a fann structure
- fann_create_from_file - Constructs a backpropagation neural network from a configuration file
- fann_create_shortcut - Creates a standard backpropagation neural network which is not fully connectected and has shortcut connections
- fann_create_shortcut_array - Creates a standard backpropagation neural network which is not fully connectected and has shortcut connections
- fann_create_sparse - Creates a standard backpropagation neural network, which is not fully connected
- fann_create_sparse_array - Creates a standard backpropagation neural network, which is not fully connected using an array of layer sizes
- fann_create_standard - Creates a standard fully connected backpropagation neural network
- fann_create_standard_array - Creates a standard fully connected backpropagation neural network using an array of layer sizes
- fann_create_train - Creates an empty training data struct
- fann_create_train_from_callback - Creates the training data struct from a user supplied function
- fann_descale_input - Scale data in input vector after get it from ann based on previously calculated parameters
- fann_descale_output - Scale data in output vector after get it from ann based on previously calculated parameters
- fann_descale_train - Descale input and output data based on previously calculated parameters
- fann_destroy - Destroys the entire network and properly freeing all the associated memory
- fann_destroy_train - Destructs the training data
- fann_duplicate_train_data - Returns an exact copy of a fann train data
- fann_get_activation_function - Returns the activation function
- fann_get_activation_steepness - Returns the activation steepness for supplied neuron and layer number
- fann_get_bias_array - Get the number of bias in each layer in the network
- fann_get_bit_fail - The number of fail bits
- fann_get_bit_fail_limit - Returns the bit fail limit used during training
- fann_get_cascade_activation_functions - Returns the cascade activation functions
- fann_get_cascade_activation_functions_count - Returns the number of cascade activation functions
- fann_get_cascade_activation_steepnesses - Returns the cascade activation steepnesses
- fann_get_cascade_activation_steepnesses_count - The number of activation steepnesses
- fann_get_cascade_candidate_change_fraction - Returns the cascade candidate change fraction
- fann_get_cascade_candidate_limit - Return the candidate limit
- fann_get_cascade_candidate_stagnation_epochs - Returns the number of cascade candidate stagnation epochs
- fann_get_cascade_max_cand_epochs - Returns the maximum candidate epochs
- fann_get_cascade_max_out_epochs - Returns the maximum out epochs
- fann_get_cascade_min_cand_epochs - Returns the minimum candidate epochs
- fann_get_cascade_min_out_epochs - Returns the minimum out epochs
- fann_get_cascade_num_candidate_groups - Returns the number of candidate groups
- fann_get_cascade_num_candidates - Returns the number of candidates used during training
- fann_get_cascade_output_change_fraction - Returns the cascade output change fraction
- fann_get_cascade_output_stagnation_epochs - Returns the number of cascade output stagnation epochs
- fann_get_cascade_weight_multiplier - Returns the weight multiplier
- fann_get_connection_array - Get connections in the network
- fann_get_connection_rate - Get the connection rate used when the network was created
- fann_get_errno - Returns the last error number
- fann_get_errstr - Returns the last errstr
- fann_get_layer_array - Get the number of neurons in each layer in the network
- fann_get_learning_momentum - Returns the learning momentum
- fann_get_learning_rate - Returns the learning rate
- fann_get_MSE - Reads the mean square error from the network
- fann_get_network_type - Get the type of neural network it was created as
- fann_get_num_input - Get the number of input neurons
- fann_get_num_layers - Get the number of layers in the neural network
- fann_get_num_output - Get the number of output neurons
- fann_get_quickprop_decay - Returns the decay which is a factor that weights should decrease in each iteration during quickprop training
- fann_get_quickprop_mu - Returns the mu factor
- fann_get_rprop_decrease_factor - Returns the increase factor used during RPROP training
- fann_get_rprop_delta_max - Returns the maximum step-size
- fann_get_rprop_delta_min - Returns the minimum step-size
- fann_get_rprop_delta_zero - Returns the initial step-size
- fann_get_rprop_increase_factor - Returns the increase factor used during RPROP training
- fann_get_sarprop_step_error_shift - Returns the sarprop step error shift
- fann_get_sarprop_step_error_threshold_factor - Returns the sarprop step error threshold factor
- fann_get_sarprop_temperature - Returns the sarprop temperature
- fann_get_sarprop_weight_decay_shift - Returns the sarprop weight decay shift
- fann_get_total_connections - Get the total number of connections in the entire network
- fann_get_total_neurons - Get the total number of neurons in the entire network
- fann_get_train_error_function - Returns the error function used during training
- fann_get_train_stop_function - Returns the stop function used during training
- fann_get_training_algorithm - Returns the training algorithm
- fann_init_weights - Initialize the weights using Widrow + Nguyen’s algorithm
- fann_length_train_data - Returns the number of training patterns in the train data
- fann_merge_train_data - Merges the train data
- fann_num_input_train_data - Returns the number of inputs in each of the training patterns in the train data
- fann_num_output_train_data - Returns the number of outputs in each of the training patterns in the train data
- fann_print_error - Prints the error string
- fann_randomize_weights - Give each connection a random weight between min_weight and max_weight
- fann_read_train_from_file - Reads a file that stores training data
- fann_reset_errno - Resets the last error number
- fann_reset_errstr - Resets the last error string
- fann_reset_MSE - Resets the mean square error from the network
- fann_run - Will run input through the neural network
- fann_save - Saves the entire network to a configuration file
- fann_save_train - Save the training structure to a file
- fann_scale_input - Scale data in input vector before feed it to ann based on previously calculated parameters
- fann_scale_input_train_data - Scales the inputs in the training data to the specified range
- fann_scale_output - Scale data in output vector before feed it to ann based on previously calculated parameters
- fann_scale_output_train_data - Scales the outputs in the training data to the specified range
- fann_scale_train - Scale input and output data based on previously calculated parameters
- fann_scale_train_data - Scales the inputs and outputs in the training data to the specified range
- fann_set_activation_function - Sets the activation function for supplied neuron and layer
- fann_set_activation_function_hidden - Sets the activation function for all of the hidden layers
- fann_set_activation_function_layer - Sets the activation function for all the neurons in the supplied layer
- fann_set_activation_function_output - Sets the activation function for the output layer
- fann_set_activation_steepness - Sets the activation steepness for supplied neuron and layer number
- fann_set_activation_steepness_hidden - Sets the steepness of the activation steepness for all neurons in the all hidden layers
- fann_set_activation_steepness_layer - Sets the activation steepness for all of the neurons in the supplied layer number
- fann_set_activation_steepness_output - Sets the steepness of the activation steepness in the output layer
- fann_set_bit_fail_limit - Set the bit fail limit used during training
- fann_set_callback - Sets the callback function for use during training
- fann_set_cascade_activation_functions - Sets the array of cascade candidate activation functions
- fann_set_cascade_activation_steepnesses - Sets the array of cascade candidate activation steepnesses
- fann_set_cascade_candidate_change_fraction - Sets the cascade candidate change fraction
- fann_set_cascade_candidate_limit - Sets the candidate limit
- fann_set_cascade_candidate_stagnation_epochs - Sets the number of cascade candidate stagnation epochs
- fann_set_cascade_max_cand_epochs - Sets the max candidate epochs
- fann_set_cascade_max_out_epochs - Sets the maximum out epochs
- fann_set_cascade_min_cand_epochs - Sets the min candidate epochs
- fann_set_cascade_min_out_epochs - Sets the minimum out epochs
- fann_set_cascade_num_candidate_groups - Sets the number of candidate groups
- fann_set_cascade_output_change_fraction - Sets the cascade output change fraction
- fann_set_cascade_output_stagnation_epochs - Sets the number of cascade output stagnation epochs
- fann_set_cascade_weight_multiplier - Sets the weight multiplier
- fann_set_error_log - Sets where the errors are logged to
- fann_set_input_scaling_params - Calculate input scaling parameters for future use based on training data
- fann_set_learning_momentum - Sets the learning momentum
- fann_set_learning_rate - Sets the learning rate
- fann_set_output_scaling_params - Calculate output scaling parameters for future use based on training data
- fann_set_quickprop_decay - Sets the quickprop decay factor
- fann_set_quickprop_mu - Sets the quickprop mu factor
- fann_set_rprop_decrease_factor - Sets the decrease factor used during RPROP training
- fann_set_rprop_delta_max - Sets the maximum step-size
- fann_set_rprop_delta_min - Sets the minimum step-size
- fann_set_rprop_delta_zero - Sets the initial step-size
- fann_set_rprop_increase_factor - Sets the increase factor used during RPROP training
- fann_set_sarprop_step_error_shift - Sets the sarprop step error shift
- fann_set_sarprop_step_error_threshold_factor - Sets the sarprop step error threshold factor
- fann_set_sarprop_temperature - Sets the sarprop temperature
- fann_set_sarprop_weight_decay_shift - Sets the sarprop weight decay shift
- fann_set_scaling_params - Calculate input and output scaling parameters for future use based on training data
- fann_set_train_error_function - Sets the error function used during training
- fann_set_train_stop_function - Sets the stop function used during training
- fann_set_training_algorithm - Sets the training algorithm
- fann_set_weight - Set a connection in the network
- fann_set_weight_array - Set connections in the network
- fann_shuffle_train_data - Shuffles training data, randomizing the order
- fann_subset_train_data - Returns an copy of a subset of the train data
- fann_test - Test with a set of inputs, and a set of desired outputs
- fann_test_data - Test a set of training data and calculates the MSE for the training data
- fann_train - Train one iteration with a set of inputs, and a set of desired outputs
- fann_train_epoch - Train one epoch with a set of training data
- fann_train_on_data - Trains on an entire dataset for a period of time
- fann_train_on_file - Trains on an entire dataset, which is read from file, for a period of time