カテゴリー一覧
Fann 関数
このカテゴリーの関数一覧
fann_cascadetrain_on_data － Trains on an entire dataset, for a period of time using the Cascade2 training algorithmfann_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_array － Creates a standard backpropagation neural network which is not fully connectected and has shortcut connections
fann_create_shortcut － Creates a standard backpropagation neural network which is not fully connectected and has shortcut connections
fann_create_sparse_array － Creates a standard backpropagation neural network, which is not fully connected using an array of layer sizes
fann_create_sparse － Creates a standard backpropagation neural network, which is not fully connected
fann_create_standard_array － Creates a standard fully connected backpropagation neural network using an array of layer sizes
fann_create_standard － Creates a standard fully connected backpropagation neural network
fann_create_train_from_callback － Creates the training data struct from a user supplied function
fann_create_train － Creates an empty training data struct
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_train － Destructs the training data
fann_destroy － Destroys the entire network and properly freeing all the associated memory
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_limit － Returns the bit fail limit used during training
fann_get_bit_fail － The number of fail bits
fann_get_cascade_activation_functions_count － Returns the number of cascade activation functions
fann_get_cascade_activation_functions － Returns the cascade activation functions
fann_get_cascade_activation_steepnesses_count － The number of activation steepnesses
fann_get_cascade_activation_steepnesses － Returns the cascade 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_train － Save the training structure to a file
fann_save － Saves the entire network to a configuration file
fann_scale_input_train_data － Scales the inputs in the training data to the specified range
fann_scale_input － Scale data in input 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_output － Scale data in output vector before feed it to ann based on previously calculated parameters
fann_scale_train_data － Scales the inputs and outputs in the training data to the specified range
fann_scale_train － Scale input and output data based on previously calculated parameters
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_function － Sets the activation function for supplied neuron and layer
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_activation_steepness － Sets the activation steepness for supplied neuron and layer number
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_array － Set connections in the network
fann_set_weight － Set a connection 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_data － Test a set of training data and calculates the MSE for the training data
fann_test － Test 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
fann_train － Train one iteration with a set of inputs, and a set of desired outputs
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