Function Index

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
| 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.

> 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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