# FANN Wrapper for C++

#### Overview

The Fann Wrapper for C++ provides two classes: neural_net and training_data. To use the wrapper include doublefann.h, floatfann.h or fixedfann.h before the fann_cpp.h header file. To get started see xor_sample.cpp in the examples directory. The license is LGPL. Copyright © 2004-2006 created by freeg.nosp@m.oldbar@yaho.nosp@m.o.com.

Note: Notes and differences from C API

- The Fann Wrapper for C++ is a minimal wrapper without use of templates or exception handling for efficient use in any environment. Benefits include stricter type checking, simpler memory management and possibly code completion in program editor.
- Method names are the same as the function names in the C API except the fann_ prefix has been removed. Enums in the namespace are similarly defined without the FANN_ prefix.
- The arguments to the methods are the same as the C API except that the struct fann *ann/struct fann_train_data *data arguments are encapsulated so they are not present in the method signatures or are translated into class references.
- The various create methods return a boolean set to true to indicate that the neural network was created, false otherwise. The same goes for the read_train_from_file method.
- The neural network and training data is automatically cleaned up in the destructors and create/read methods.
- To make the destructors virtual define USE_VIRTUAL_DESTRUCTOR before including the header file.
- Additional methods are available on the training_data class to give access to the underlying training data. They are get_input, get_output and set_train_data. Finally fann_duplicate_train_data has been replaced by a copy constructor.

Note: Changes

#### Version 2.2.0

- General update to fann C library 2.2.0 with support for new functionality

#### Version 2.1.0

- General update to fann C library 2.1.0 with support for new functionality
- Due to changes in the C API the C++ API is not fully backward compatible: The create methods have changed names and parameters. The training callback function has different parameters and a set_callback. Some training_data methods have updated names. Get activation function and steepness is available for neurons, not layers.
- Extensions are now part of fann so there is no fann_extensions.h

#### Version 1.2.0

- Changed char pointers to const std::string references
- Added const_casts where the C API required it
- Initialized enums from the C enums instead of numeric constants
- Added a method set_train_data that copies and allocates training
- data in a way that is compatible with the way the C API deallocates
- the data thus making it possible to change training data.
- The get_rprop_increase_factor method did not return its value

#### Version 1.0.0

- Initial version

Summary

FANN Wrapper for C++ | The Fann Wrapper for C++ provides two classes: neural_net and training_data.
FANN | The FANN namespace groups the C++ wrapper definitions
Enumerations |
error_function_enum | Error function used during training.
stop_function_enum | Stop criteria used during training.
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.
activation_function_enum | The activation functions used for the neurons during training.
network_type_enum | Definition of network types used by neural_net::get_network_type
Types |
connection | Describes a connection between two neurons and its weight
callback_type | 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.
training_data | Encapsulation of a training data set struct fann_train_data and associated C API functions.
Functions |
training_data | Default constructor creates an empty neural net.
training_data | Copy constructor constructs a copy of the training data.
~training_data | Provides automatic cleanup of data.
destroy | Destructs the training data.
read_train_from_file | Reads a file that stores training data.
save_train | Save the training structure to a file, with the format as specified in read_train_from_file
save_train_to_fixed | Saves the training structure to a fixed point data file.
shuffle_train_data | Shuffles training data, randomizing the order.
merge_train_data | Merges the data into the data contained in the training_data.
length_train_data | Returns the number of training patterns in the training_data.
num_input_train_data | Returns the number of inputs in each of the training patterns in the training_data.
num_output_train_data | Returns the number of outputs in each of the training patterns in the struct fann_train_data.
get_input | A pointer to the array of input training data
get_output | A pointer to the array of output training data
set_train_data | Set the training data to the input and output data provided.
create_train_from_callback | Creates the training data struct from a user supplied function.
scale_input_train_data | Scales the inputs in the training data to the specified range.
scale_output_train_data | Scales the outputs in the training data to the specified range.
scale_train_data | Scales the inputs and outputs in the training data to the specified range.
subset_train_data | Changes the training data to a subset, starting at position pos and length elements forward.
neural_net | Encapsulation of a neural network struct fann and associated C API functions.
Functions |
neural_net | Default constructor creates an empty neural net.
neural_net | Creates a copy the other neural_net.
copy_from_struct_fann | Set the internal fann struct to a copy of other
~neural_net | Provides automatic cleanup of data.
destroy | Destructs the entire network.
create_standard | Creates a standard fully connected backpropagation neural network.
create_standard_array | Just like create_standard, but with an array of layer sizes instead of individual parameters.
create_sparse | Creates a standard backpropagation neural network, which is not fully connected.
create_sparse_array | Just like create_sparse, but with an array of layer sizes instead of individual parameters.
create_shortcut | Creates a standard backpropagation neural network, which is not fully connected and which also has shortcut connections.
create_shortcut_array | Just like create_shortcut, but with an array of layer sizes instead of individual parameters.
run | 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.
randomize_weights | Give each connection a random weight between min_weight and max_weight
init_weights | Initialize the weights using Widrow + Nguyen’s algorithm.
print_connections | Will print the connections of the ann in a compact matrix, for easy viewing of the internals of the ann.
create_from_file | Constructs a backpropagation neural network from a configuration file, which have been saved by save.
save | Save the entire network to a configuration file.
save_to_fixed | Saves the entire network to a configuration file.
train | Train one iteration with a set of inputs, and a set of desired outputs.
train_epoch | Train one epoch with a set of training data.
train_on_data | Trains on an entire dataset, for a period of time.
train_on_file | Does the same as train_on_data, but reads the training data directly from a file.
test | Test with a set of inputs, and a set of desired outputs.
test_data | Test a set of training data and calculates the MSE for the training data.
get_MSE | Reads the mean square error from the network.
reset_MSE | Resets the mean square error from the network.
set_callback | Sets the callback function for use during training.
print_parameters | Prints all of the parameters and options of the neural network
get_training_algorithm | Return the training algorithm as described by FANN::training_algorithm_enum.
set_training_algorithm | Set the training algorithm.
get_learning_rate | Return the learning rate.
set_learning_rate | Set the learning rate.
get_activation_function | Get the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.
set_activation_function | Set the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.
set_activation_function_layer | Set the activation function for all the neurons in the layer number layer, counting the input layer as layer 0.
set_activation_function_hidden | Set the activation function for all of the hidden layers.
set_activation_function_output | Set the activation function for the output layer.
get_activation_steepness | Get the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.
set_activation_steepness | Set the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.
set_activation_steepness_layer | Set the activation steepness all of the neurons in layer number layer, counting the input layer as layer 0.
set_activation_steepness_hidden | Set the steepness of the activation steepness in all of the hidden layers.
set_activation_steepness_output | Set the steepness of the activation steepness in the output layer.
get_train_error_function | Returns the error function used during training.
set_train_error_function | Set the error function used during training.
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.
set_quickprop_decay | Sets the quickprop decay factor.
get_quickprop_mu | The mu factor is used to increase and decrease the step-size during quickprop training.
set_quickprop_mu | Sets the quickprop mu factor.
get_rprop_increase_factor | The increase factor is a value larger than 1, which is used to increase the step-size during RPROP training.
set_rprop_increase_factor | The increase factor used during RPROP training.
get_rprop_decrease_factor | The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training.
set_rprop_decrease_factor | The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training.
get_rprop_delta_zero | The initial step-size is a small positive number determining how small the initial step-size may be.
set_rprop_delta_zero | The initial step-size is a small positive number determining how small the initial step-size may be.
get_rprop_delta_min | The minimum step-size is a small positive number determining how small the minimum step-size may be.
set_rprop_delta_min | The minimum step-size is a small positive number determining how small the minimum step-size may be.
get_rprop_delta_max | The maximum step-size is a positive number determining how large the maximum step-size may be.
set_rprop_delta_max | The maximum step-size is a positive number determining how large the maximum step-size may be.
get_sarprop_weight_decay_shift | The sarprop weight decay shift.
set_sarprop_weight_decay_shift | Set the sarprop weight decay shift.
get_sarprop_step_error_threshold_factor | The sarprop step error threshold factor.
set_sarprop_step_error_threshold_factor | Set the sarprop step error threshold factor.
get_sarprop_step_error_shift | The get sarprop step error shift.
set_sarprop_step_error_shift | Set the sarprop step error shift.
get_sarprop_temperature | The sarprop weight decay shift.
set_sarprop_temperature | Set the sarprop_temperature.
get_num_input | Get the number of input neurons.
get_num_output | Get the number of output neurons.
get_total_neurons | Get the total number of neurons in the entire network.
get_total_connections | Get the total number of connections in the entire network.
get_decimal_point | Returns the position of the decimal point in the ann.
get_multiplier | Returns the multiplier that fix point data is multiplied with.
get_network_type | Get the type of neural network it was created as.
get_connection_rate | Get the connection rate used when the network was created
get_num_layers | Get the number of layers in the network
get_layer_array | Get the number of neurons in each layer in the network.
get_bias_array | Get the number of bias in each layer in the network.
get_connection_array | Get the connections in the network.
set_weight_array | Set connections in the network.
set_weight | Set a connection in the network.
get_learning_momentum | Get the learning momentum.
set_learning_momentum | Set the learning momentum.
get_train_stop_function | Returns the the stop function used during training.
set_train_stop_function | Set the stop function used during training.
get_bit_fail_limit | Returns the bit fail limit used during training.
set_bit_fail_limit | Set the bit fail limit used during training.
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).
cascadetrain_on_data | Trains on an entire dataset, for a period of time using the Cascade2 training algorithm.
cascadetrain_on_file | Does the same as cascadetrain_on_data, but reads the training data directly from a file.
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.
set_cascade_output_change_fraction | Sets the cascade output change fraction.
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.
set_cascade_output_stagnation_epochs | Sets the number of cascade output stagnation epochs.
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.
set_cascade_candidate_change_fraction | Sets the cascade candidate change fraction.
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.
set_cascade_candidate_stagnation_epochs | Sets the number of cascade candidate stagnation epochs.
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.
set_cascade_weight_multiplier | Sets the weight multiplier.
get_cascade_candidate_limit | The candidate limit is a limit for how much the candidate neuron may be trained.
set_cascade_candidate_limit | Sets the candidate limit.
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.
set_cascade_max_out_epochs | Sets the maximum out epochs.
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.
set_cascade_max_cand_epochs | Sets the max candidate epochs.
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).
get_cascade_activation_functions_count | The number of activation functions in the get_cascade_activation_functions array.
get_cascade_activation_functions | The cascade activation functions array is an array of the different activation functions used by the candidates.
set_cascade_activation_functions | Sets the array of cascade candidate activation functions.
get_cascade_activation_steepnesses_count | The number of activation steepnesses in the get_cascade_activation_functions array.
get_cascade_activation_steepnesses | The cascade activation steepnesses array is an array of the different activation functions used by the candidates.
set_cascade_activation_steepnesses | Sets the array of cascade candidate activation steepnesses.
get_cascade_num_candidate_groups | The number of candidate groups is the number of groups of identical candidates which will be used during training.
set_cascade_num_candidate_groups | Sets the number of candidate groups.
scale_train | Scale input and output data based on previously calculated parameters.
descale_train | Descale input and output data based on previously calculated parameters.
set_input_scaling_params | Calculate scaling parameters for future use based on training data.
set_output_scaling_params | Calculate scaling parameters for future use based on training data.
set_scaling_params | Calculate scaling parameters for future use based on training data.
clear_scaling_params | Clears scaling parameters.
scale_input | Scale data in input vector before feed it to ann based on previously calculated parameters.
scale_output | Scale data in output vector before feed it to ann based on previously calculated parameters.
descale_input | Scale data in input vector after get it from ann based on previously calculated parameters.
descale_output | Scale data in output vector after get it from ann based on previously calculated parameters.
set_error_log | Change where errors are logged to.
get_errno | Returns the last error number.
reset_errno | Resets the last error number.
reset_errstr | Resets the last error string.
get_errstr | Returns the last errstr.
print_error | Prints the last error to stderr.

## FANN

> namespace FANN

The FANN namespace groups the C++ wrapper definitions

Summary

Enumerations |
error_function_enum | Error function used during training.
stop_function_enum | Stop criteria used during training.
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.
activation_function_enum | The activation functions used for the neurons during training.
network_type_enum | Definition of network types used by neural_net::get_network_type
Types |
connection | Describes a connection between two neurons and its weight
callback_type | 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.

### Enumerations

### error_function_enum

Error function used during training.

ERRORFUNC_LINEAR | Standard linear error function.
ERRORFUNC_TANH | Tanh error function, usually better but can require a lower learning rate. This error function agressively targets outputs that differ much from the desired, while not targetting outputs that only differ a little that much. This activation function is not recommended for cascade training and incremental training.

#### See also

neural_net::set_train_error_function, neural_net::get_train_error_function

### stop_function_enum

Stop criteria used during training.

STOPFUNC_MSE | Stop criteria is Mean Square Error (MSE) value.
STOPFUNC_BIT | Stop criteria is number of bits that fail. The number of bits; means the number of output neurons which differ more than the bit fail limit (see neural_net::get_bit_fail_limit, neural_net::set_bit_fail_limit). The bits are counted in all of the training data, so this number can be higher than the number of training data.

#### See also

neural_net::set_train_stop_function, neural_net::get_train_stop_function

### 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. The incremental training looks alters the weights after each time it is presented an input pattern, while batch only alters the weights once after it has been presented to all the patterns.

TRAIN_INCREMENTAL | Standard backpropagation algorithm, where the weights are updated after each training pattern. This means that the weights are updated many times during a single epoch. For this reason some problems, will train very fast with this algorithm, while other more advanced problems will not train very well.

TRAIN_BATCH

Standard backpropagation algorithm, where the weights are updated after calculating the mean square error for the whole training set. This means that the weights are only updated once during a epoch. For this reason some problems, will train slower with this algorithm. But since the mean square error is calculated more correctly than in incremental training, some problems will reach a better solutions with this algorithm.

TRAIN_RPROP

A more advanced batch training algorithm which achieves good results for many problems. The RPROP training algorithm is adaptive, and does therefore not use the learning_rate. Some other parameters can however be set to change the way the RPROP algorithm works, but it is only recommended for users with insight in how the RPROP training algorithm works. The RPROP training algorithm is described by [Riedmiller and Braun, 1993], but the actual learning algorithm used here is the iRPROP- training algorithm which is described by [Igel and Husken, 2000] which is an variety of the standard RPROP training algorithm.

TRAIN_QUICKPROP

A more advanced batch training algorithm which achieves good results for many problems. The quickprop training algorithm uses the learning_rate parameter along with other more advanced parameters, but it is only recommended to change these advanced parameters, for users with insight in how the quickprop training algorithm works. The quickprop training algorithm is described by [Fahlman, 1988].

#### See also

neural_net::set_training_algorithm, neural_net::get_training_algorithm

### activation_function_enum

The activation functions used for the neurons during training. The activation functions can either be defined for a group of neurons by neural_net::set_activation_function_hidden and neural_net::set_activation_function_output or it can be defined for a single neuron by neural_net::set_activation_function.

The steepness of an activation function is defined in the same way by neural_net::set_activation_steepness_hidden, neural_net::set_activation_steepness_output and neural_net::set_activation_steepness.

#### The functions are described with functions where

- x is the input to the activation function,
- y is the output,
- s is the steepness and
- d is the derivation.

FANN_LINEAR | Linear activation function.

- span: -inf < y < inf
- y = x*s, d = 1*s
- Can NOT be used in fixed point.

FANN_THRESHOLD | Threshold activation function.

- x < 0 -> y = 0, x >= 0 -> y = 1
- Can NOT be used during training.

FANN_THRESHOLD_SYMMETRIC | Threshold activation function.

- x < 0 -> y = 0, x >= 0 -> y = 1
- Can NOT be used during training.

FANN_SIGMOID | Sigmoid activation function.

- One of the most used activation functions.
- span: 0 < y < 1
- y = 1/(1 + exp(-2*s*x))
- d = 2*s*y*(1 - y)

FANN_SIGMOID_STEPWISE | Stepwise linear approximation to sigmoid.

- Faster than sigmoid but a bit less precise.

FANN_SIGMOID_SYMMETRIC | Symmetric sigmoid activation function, aka. tanh.

- One of the most used activation functions.
- span: -1 < y < 1
- y = tanh(s*x) = 2/(1 + exp(-2*s*x)) - 1
- d = s*(1-(y*y))

FANN_SIGMOID_SYMMETRIC | Stepwise linear approximation to symmetric sigmoid.

- Faster than symmetric sigmoid but a bit less precise.

FANN_GAUSSIAN | Gaussian activation function.

- 0 when x = -inf, 1 when x = 0 and 0 when x = inf
- span: 0 < y < 1
- y = exp(-x*s*x*s)
- d = -2*x*s*y*s

FANN_GAUSSIAN_SYMMETRIC | Symmetric gaussian activation function.

- -1 when x = -inf, 1 when x = 0 and 0 when x = inf
- span: -1 < y < 1
- y = exp(-x*s*x*s)*2-1
- d = -2*x*s*(y+1)*s

FANN_ELLIOT | Fast (sigmoid like) activation function defined by David Elliott

- span: 0 < y < 1
- y = ((x*s) / 2) / (1 + |x*s|) + 0.5
- d = s*1/(2*(1+|x*s|)*(1+|x*s|))

FANN_ELLIOT_SYMMETRIC | Fast (symmetric sigmoid like) activation function defined by David Elliott

- span: -1 < y < 1
- y = (x*s) / (1 + |x*s|)
- d = s*1/((1+|x*s|)*(1+|x*s|))

FANN_LINEAR_PIECE | Bounded linear activation function.

- span: 0 < y < 1
- y = x*s, d = 1*s

FANN_LINEAR_PIECE_SYMMETRIC | Bounded Linear activation function.

- span: -1 < y < 1
- y = x*s, d = 1*s

FANN_SIN_SYMMETRIC | Periodical sinus activation function.

- span: -1 <= y <= 1
- y = sin(x*s)
- d = s*cos(x*s)

FANN_COS_SYMMETRIC | Periodical cosinus activation function.

- span: -1 <= y <= 1
- y = cos(x*s)
- d = s*-sin(x*s)

#### See also

neural_net::set_activation_function_hidden, neural_net::set_activation_function_output

### network_type_enum

Definition of network types used by neural_net::get_network_type

LAYER | Each layer only has connections to the next layer
SHORTCUT | Each layer has connections to all following layers

#### See Also

neural_net::get_network_type, fann_get_network_type

This enumeration appears in FANN >= 2.1.0

### Types

### connection

Describes a connection between two neurons and its weight

from_neuron | Unique number used to identify source neuron
to_neuron | Unique number used to identify destination neuron
weight | The numerical value of the weight

#### See Also

neural_net::get_connection_array, neural_net::set_weight_array

This structure appears in FANN >= 2.1.0

### callback_type

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.

>     typedef int (*callback_type) (neural_net &net, training_data &train,
>         unsigned int max_epochs, unsigned int epochs_between_reports,
>         float desired_error, unsigned int epochs, void *user_data);

The callback can be set by using neural_net::set_callback and is very usefull for doing custom things during training. It is recommended to use this function when implementing custom training procedures, or when visualizing the training in a GUI etc. The parameters which the callback function takes is the parameters given to the neural_net::train_on_data, plus an epochs parameter which tells how many epochs the training have taken so far.

The callback function should return an integer, if the callback function returns -1, the training will terminate.

#### Example of a callback function that prints information to cout

>     int print_callback(FANN::neural_net &net, FANN::training_data &train,
>         unsigned int max_epochs, unsigned int epochs_between_reports,
>         float desired_error, unsigned int epochs, void *user_data)
>     {
>         cout << "Epochs     " << setw(8) << epochs << ". "
>              << "Current Error: " << left << net.get_MSE() << right << endl;
>         return 0;
>     }

#### See also

neural_net::set_callback, fann_callback_type

## training_data

> class training_data

Encapsulation of a training data set struct fann_train_data and associated C API functions.

Summary

Functions |
training_data | Default constructor creates an empty neural net.
training_data | Copy constructor constructs a copy of the training data.
~training_data | Provides automatic cleanup of data.
destroy | Destructs the training data.
read_train_from_file | Reads a file that stores training data.
save_train | Save the training structure to a file, with the format as specified in read_train_from_file
save_train_to_fixed | Saves the training structure to a fixed point data file.
shuffle_train_data | Shuffles training data, randomizing the order.
merge_train_data | Merges the data into the data contained in the training_data.
length_train_data | Returns the number of training patterns in the training_data.
num_input_train_data | Returns the number of inputs in each of the training patterns in the training_data.
num_output_train_data | Returns the number of outputs in each of the training patterns in the struct fann_train_data.
get_input | A pointer to the array of input training data
get_output | A pointer to the array of output training data
set_train_data | Set the training data to the input and output data provided.
create_train_from_callback | Creates the training data struct from a user supplied function.
scale_input_train_data | Scales the inputs in the training data to the specified range.
scale_output_train_data | Scales the outputs in the training data to the specified range.
scale_train_data | Scales the inputs and outputs in the training data to the specified range.
subset_train_data | Changes the training data to a subset, starting at position pos and length elements forward.

### Functions

### training_data

> training_data( |  | ) : train_data(NULL)

Default constructor creates an empty neural net. Use read_train_from_file, set_train_data or create_train_from_callback to initialize.

### training_data

> training_data( | const | training_data | & | data | )

Copy constructor constructs a copy of the training data. Corresponds to the C API fann_duplicate_train_data function.

### ~training_data

> #ifdef USE_VIRTUAL_DESTRUCTOR virtual #endif ~training_data()

Provides automatic cleanup of data. Define USE_VIRTUAL_DESTRUCTOR if you need the destructor to be virtual.

#### See also

destroy

### destroy

> void destroy_train()

Destructs the training data. Called automatically by the destructor.

#### See also

~training_data

### read_train_from_file

> bool read_train_from_file( | const std:: | string | & | filename | )

Reads a file that stores training data.

#### The file must be formatted like

>     num_train_data num_input num_output
>     inputdata seperated by space
>     outputdata seperated by space

>     .
>     .
>     .

>     inputdata seperated by space
>     outputdata seperated by space

#### See also

neural_net::train_on_data, save_train, fann_read_train_from_file

This function appears in FANN >= 1.0.0

### save_train

> bool save_train( | const std:: | string | & | filename | )

Save the training structure to a file, with the format as specified in read_train_from_file

#### Return

The function returns true on success and false on failure.

#### See also

read_train_from_file, save_train_to_fixed, fann_save_train

This function appears in FANN >= 1.0.0.

### save_train_to_fixed

> bool save_train_to_fixed( | const std:: | string | & | filename,
> | unsigned | int |  | decimal_point | )

Saves the training structure to a fixed point data file.

This function is very usefull for testing the quality of a fixed point network.

#### Return

The function returns true on success and false on failure.

#### See also

save_train, fann_save_train_to_fixed

This function appears in FANN >= 1.0.0.

### shuffle_train_data

> void shuffle_train_data()

Shuffles training data, randomizing the order. This is recommended for incremental training, while it have no influence during batch training.

This function appears in FANN >= 1.1.0.

### merge_train_data

> void merge_train_data( | const | training_data | & | data | )

Merges the data into the data contained in the training_data.

This function appears in FANN >= 1.1.0.

### length_train_data

> unsigned int length_train_data()

Returns the number of training patterns in the training_data.

#### See also

num_input_train_data, num_output_train_data, fann_length_train_data

This function appears in FANN >= 2.0.0.

### num_input_train_data

> unsigned int num_input_train_data()

Returns the number of inputs in each of the training patterns in the training_data.

#### See also

num_output_train_data, length_train_data, fann_num_input_train_data

This function appears in FANN >= 2.0.0.

### num_output_train_data

> unsigned int num_output_train_data()

Returns the number of outputs in each of the training patterns in the struct fann_train_data.

#### See also

num_input_train_data, length_train_data, fann_num_output_train_data

This function appears in FANN >= 2.0.0.

### get_input

> fann_type **get_input()

#### Returns

A pointer to the array of input training data

#### See also

get_output, set_train_data

### get_output

> fann_type **get_output()

#### Returns

A pointer to the array of output training data

#### See also

get_input, set_train_data

### set_train_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.

A copy of the data is made so there are no restrictions on the allocation of the input/output data and the caller is responsible for the deallocation of the data pointed to by input and output.

#### Parameters

num_data | The number of training data
num_input | The number of inputs per training data
num_output | The number of ouputs per training data
input | The set of inputs (a pointer to an array of pointers to arrays of floating point data)
output | The set of desired outputs (a pointer to an array of pointers to arrays of floating point data)

#### See also

get_input, get_output

### create_train_from_callback

> 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. As the training data are numerable (data 1, data 2...), the user must write a function that receives the number of the training data set (input,output) and returns the set.

#### Parameters

num_data | The number of training data
num_input | The number of inputs per training data
num_output | The number of ouputs per training data
user_function | The user suplied function

#### Parameters for the user function

num | The number of the training data set
num_input | The number of inputs per training data
num_output | The number of ouputs per training data
input | The set of inputs
output | The set of desired outputs

#### See also

training_data::read_train_from_file, neural_net::train_on_data, fann_create_train_from_callback

This function appears in FANN >= 2.1.0

### scale_input_train_data

> void scale_input_train_data( | fann_type | new_min,
> | fann_type | new_max | )

Scales the inputs in the training data to the specified range.

#### See also

scale_output_train_data, scale_train_data, fann_scale_input_train_data

This function appears in FANN >= 2.0.0.

### scale_output_train_data

> void scale_output_train_data( | fann_type | new_min,
> | fann_type | new_max | )

Scales the outputs in the training data to the specified range.

#### See also

scale_input_train_data, scale_train_data, fann_scale_output_train_data

This function appears in FANN >= 2.0.0.

### scale_train_data

> 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.

#### See also

scale_output_train_data, scale_input_train_data, fann_scale_train_data

This function appears in FANN >= 2.0.0.

### subset_train_data

> 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. Use the copy constructor to work on a new copy of the training data.

>     FANN::training_data full_data_set;
>     full_data_set.read_train_from_file("somefile.train");
>     FANN::training_data *small_data_set = new FANN::training_data(full_data_set);
>     small_data_set->subset_train_data(0, 2); // Only use first two
>     // Use small_data_set ...
>     delete small_data_set;

#### See also

fann_subset_train_data

This function appears in FANN >= 2.0.0.

## neural_net

> class neural_net

Encapsulation of a neural network struct fann and associated C API functions.

Summary

Functions |
neural_net | Default constructor creates an empty neural net.
neural_net | Creates a copy the other neural_net.
copy_from_struct_fann | Set the internal fann struct to a copy of other
~neural_net | Provides automatic cleanup of data.
destroy | Destructs the entire network.
create_standard | Creates a standard fully connected backpropagation neural network.
create_standard_array | Just like create_standard, but with an array of layer sizes instead of individual parameters.
create_sparse | Creates a standard backpropagation neural network, which is not fully connected.
create_sparse_array | Just like create_sparse, but with an array of layer sizes instead of individual parameters.
create_shortcut | Creates a standard backpropagation neural network, which is not fully connected and which also has shortcut connections.
create_shortcut_array | Just like create_shortcut, but with an array of layer sizes instead of individual parameters.
run | 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.
randomize_weights | Give each connection a random weight between min_weight and max_weight
init_weights | Initialize the weights using Widrow + Nguyen’s algorithm.
print_connections | Will print the connections of the ann in a compact matrix, for easy viewing of the internals of the ann.
create_from_file | Constructs a backpropagation neural network from a configuration file, which have been saved by save.
save | Save the entire network to a configuration file.
save_to_fixed | Saves the entire network to a configuration file.
train | Train one iteration with a set of inputs, and a set of desired outputs.
train_epoch | Train one epoch with a set of training data.
train_on_data | Trains on an entire dataset, for a period of time.
train_on_file | Does the same as train_on_data, but reads the training data directly from a file.
test | Test with a set of inputs, and a set of desired outputs.
test_data | Test a set of training data and calculates the MSE for the training data.
get_MSE | Reads the mean square error from the network.
reset_MSE | Resets the mean square error from the network.
set_callback | Sets the callback function for use during training.
print_parameters | Prints all of the parameters and options of the neural network
get_training_algorithm | Return the training algorithm as described by FANN::training_algorithm_enum.
set_training_algorithm | Set the training algorithm.
get_learning_rate | Return the learning rate.
set_learning_rate | Set the learning rate.
get_activation_function | Get the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.
set_activation_function | Set the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.
set_activation_function_layer | Set the activation function for all the neurons in the layer number layer, counting the input layer as layer 0.
set_activation_function_hidden | Set the activation function for all of the hidden layers.
set_activation_function_output | Set the activation function for the output layer.
get_activation_steepness | Get the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.
set_activation_steepness | Set the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.
set_activation_steepness_layer | Set the activation steepness all of the neurons in layer number layer, counting the input layer as layer 0.
set_activation_steepness_hidden | Set the steepness of the activation steepness in all of the hidden layers.
set_activation_steepness_output | Set the steepness of the activation steepness in the output layer.
get_train_error_function | Returns the error function used during training.
set_train_error_function | Set the error function used during training.
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.
set_quickprop_decay | Sets the quickprop decay factor.
get_quickprop_mu | The mu factor is used to increase and decrease the step-size during quickprop training.
set_quickprop_mu | Sets the quickprop mu factor.
get_rprop_increase_factor | The increase factor is a value larger than 1, which is used to increase the step-size during RPROP training.
set_rprop_increase_factor | The increase factor used during RPROP training.
get_rprop_decrease_factor | The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training.
set_rprop_decrease_factor | The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training.
get_rprop_delta_zero | The initial step-size is a small positive number determining how small the initial step-size may be.
set_rprop_delta_zero | The initial step-size is a small positive number determining how small the initial step-size may be.
get_rprop_delta_min | The minimum step-size is a small positive number determining how small the minimum step-size may be.
set_rprop_delta_min | The minimum step-size is a small positive number determining how small the minimum step-size may be.
get_rprop_delta_max | The maximum step-size is a positive number determining how large the maximum step-size may be.
set_rprop_delta_max | The maximum step-size is a positive number determining how large the maximum step-size may be.
get_sarprop_weight_decay_shift | The sarprop weight decay shift.
set_sarprop_weight_decay_shift | Set the sarprop weight decay shift.
get_sarprop_step_error_threshold_factor | The sarprop step error threshold factor.
set_sarprop_step_error_threshold_factor | Set the sarprop step error threshold factor.
get_sarprop_step_error_shift | The get sarprop step error shift.
set_sarprop_step_error_shift | Set the sarprop step error shift.
get_sarprop_temperature | The sarprop weight decay shift.
set_sarprop_temperature | Set the sarprop_temperature.
get_num_input | Get the number of input neurons.
get_num_output | Get the number of output neurons.
get_total_neurons | Get the total number of neurons in the entire network.
get_total_connections | Get the total number of connections in the entire network.
get_decimal_point | Returns the position of the decimal point in the ann.
get_multiplier | Returns the multiplier that fix point data is multiplied with.
get_network_type | Get the type of neural network it was created as.
get_connection_rate | Get the connection rate used when the network was created
get_num_layers | Get the number of layers in the network
get_layer_array | Get the number of neurons in each layer in the network.
get_bias_array | Get the number of bias in each layer in the network.
get_connection_array | Get the connections in the network.
set_weight_array | Set connections in the network.
set_weight | Set a connection in the network.
get_learning_momentum | Get the learning momentum.
set_learning_momentum | Set the learning momentum.
get_train_stop_function | Returns the the stop function used during training.
set_train_stop_function | Set the stop function used during training.
get_bit_fail_limit | Returns the bit fail limit used during training.
set_bit_fail_limit | Set the bit fail limit used during training.
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).
cascadetrain_on_data | Trains on an entire dataset, for a period of time using the Cascade2 training algorithm.
cascadetrain_on_file | Does the same as cascadetrain_on_data, but reads the training data directly from a file.
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.
set_cascade_output_change_fraction | Sets the cascade output change fraction.
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.
set_cascade_output_stagnation_epochs | Sets the number of cascade output stagnation epochs.
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.
set_cascade_candidate_change_fraction | Sets the cascade candidate change fraction.
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.
set_cascade_candidate_stagnation_epochs | Sets the number of cascade candidate stagnation epochs.
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.
set_cascade_weight_multiplier | Sets the weight multiplier.
get_cascade_candidate_limit | The candidate limit is a limit for how much the candidate neuron may be trained.
set_cascade_candidate_limit | Sets the candidate limit.
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.
set_cascade_max_out_epochs | Sets the maximum out epochs.
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.
set_cascade_max_cand_epochs | Sets the max candidate epochs.
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).
get_cascade_activation_functions_count | The number of activation functions in the get_cascade_activation_functions array.
get_cascade_activation_functions | The cascade activation functions array is an array of the different activation functions used by the candidates.
set_cascade_activation_functions | Sets the array of cascade candidate activation functions.
get_cascade_activation_steepnesses_count | The number of activation steepnesses in the get_cascade_activation_functions array.
get_cascade_activation_steepnesses | The cascade activation steepnesses array is an array of the different activation functions used by the candidates.
set_cascade_activation_steepnesses | Sets the array of cascade candidate activation steepnesses.
get_cascade_num_candidate_groups | The number of candidate groups is the number of groups of identical candidates which will be used during training.
set_cascade_num_candidate_groups | Sets the number of candidate groups.
scale_train | Scale input and output data based on previously calculated parameters.
descale_train | Descale input and output data based on previously calculated parameters.
set_input_scaling_params | Calculate scaling parameters for future use based on training data.
set_output_scaling_params | Calculate scaling parameters for future use based on training data.
set_scaling_params | Calculate scaling parameters for future use based on training data.
clear_scaling_params | Clears scaling parameters.
scale_input | Scale data in input vector before feed it to ann based on previously calculated parameters.
scale_output | Scale data in output vector before feed it to ann based on previously calculated parameters.
descale_input | Scale data in input vector after get it from ann based on previously calculated parameters.
descale_output | Scale data in output vector after get it from ann based on previously calculated parameters.
set_error_log | Change where errors are logged to.
get_errno | Returns the last error number.
reset_errno | Resets the last error number.
reset_errstr | Resets the last error string.
get_errstr | Returns the last errstr.
print_error | Prints the last error to stderr.

### Functions

### neural_net

> neural_net( |  | ) : ann(NULL)

Default constructor creates an empty neural net. Use one of the create functions to create the neural network.

#### See also

create_standard, create_sparse, create_shortcut, create_standard_array, create_sparse_array, create_shortcut_array

### neural_net

> neural_net( | struct | fann | * | other | )

Creates a copy the other neural_net.

#### See also

copy_from_struct_fann

### copy_from_struct_fann

> void copy_from_struct_fann( | struct | fann | * | other | )

Set the internal fann struct to a copy of other

### ~neural_net

> #ifdef USE_VIRTUAL_DESTRUCTOR virtual #endif ~neural_net()

Provides automatic cleanup of data. Define USE_VIRTUAL_DESTRUCTOR if you need the destructor to be virtual.

#### See also

destroy

### destroy

> void destroy()

Destructs the entire network. Called automatically by the destructor.

#### See also

~neural_net

### create_standard

> bool create_standard( | unsigned | int | num_layers,
> |  |  | ... | )

Creates a standard fully connected backpropagation neural network.

There will be a bias neuron in each layer (except the output layer), and this bias neuron will be connected to all neurons in the next layer. When running the network, the bias nodes always emits 1.

#### Parameters

num_layers | The total number of layers including the input and the output layer.
... | Integer values determining the number of neurons in each layer starting with the input layer and ending with the output layer.

#### Returns

Boolean true if the network was created, false otherwise.

#### Example

>     const unsigned int num_layers = 3;
>     const unsigned int num_input = 2;
>     const unsigned int num_hidden = 3;
>     const unsigned int num_output = 1;

>     FANN::neural_net net;
>     net.create_standard(num_layers, num_input, num_hidden, num_output);

#### See also

create_standard_array, create_sparse, create_shortcut, fann_create_standard_array

This function appears in FANN >= 2.0.0.

### create_standard_array

> 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.

#### See also

create_standard, create_sparse, create_shortcut, fann_create_standard

This function appears in FANN >= 2.0.0.

### create_sparse

> bool create_sparse( |  | float | connection_rate,
> | unsigned | int | num_layers,
> |  |  | ... | )

Creates a standard backpropagation neural network, which is not fully connected.

#### Parameters

connection_rate | The connection rate controls how many connections there will be in the network. If the connection rate is set to 1, the network will be fully connected, but if it is set to 0.5 only half of the connections will be set. A connection rate of 1 will yield the same result as fann_create_standard
num_layers | The total number of layers including the input and the output layer.
... | Integer values determining the number of neurons in each layer starting with the input layer and ending with the output layer.

#### Returns

Boolean true if the network was created, false otherwise.

#### See also

create_standard, create_sparse_array, create_shortcut, fann_create_sparse

This function appears in FANN >= 2.0.0.

### create_sparse_array

> 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.

See create_sparse for a description of the parameters.

#### See also

create_standard, create_sparse, create_shortcut, fann_create_sparse_array

This function appears in FANN >= 2.0.0.

### create_shortcut

> bool create_shortcut( | unsigned | int | num_layers,
> |  |  | ... | )

Creates a standard backpropagation neural network, which is not fully connected and which also has shortcut connections.

Shortcut connections are connections that skip layers. A fully connected network with shortcut connections, is a network where all neurons are connected to all neurons in later layers. Including direct connections from the input layer to the output layer.

See create_standard for a description of the parameters.

#### See also

create_standard, create_sparse, create_shortcut_array, fann_create_shortcut

This function appears in FANN >= 2.0.0.

### create_shortcut_array

> 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.

See create_standard_array for a description of the parameters.

#### See also

create_standard, create_sparse, create_shortcut, fann_create_shortcut_array

This function appears in FANN >= 2.0.0.

### run

> 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.

#### See also

test, fann_run

This function appears in FANN >= 1.0.0.

### randomize_weights

> void randomize_weights( | fann_type | min_weight,
> | fann_type | max_weight | )

Give each connection a random weight between min_weight and max_weight

From the beginning the weights are random between -0.1 and 0.1.

#### See also

init_weights, fann_randomize_weights

This function appears in FANN >= 1.0.0.

### init_weights

> void init_weights( | const | training_data | & | data | )

Initialize the weights using Widrow + Nguyen’s algorithm.

This function behaves similarly to fann_randomize_weights. It will use the algorithm developed by Derrick Nguyen and Bernard Widrow to set the weights in such a way as to speed up training. This technique is not always successful, and in some cases can be less efficient than a purely random initialization.

The algorithm requires access to the range of the input data (ie, largest and smallest input), and therefore accepts a second argument, data, which is the training data that will be used to train the network.

#### See also

randomize_weights, training_data::read_train_from_file, fann_init_weights

This function appears in FANN >= 1.1.0.

### print_connections

> void print_connections()

Will print the connections of the ann in a compact matrix, for easy viewing of the internals of the ann.

The output from fann_print_connections on a small (2 2 1) network trained on the xor problem

>     Layer / Neuron 012345
>     L   1 / N    3 BBa...
>     L   1 / N    4 BBA...
>     L   1 / N    5 ......
>     L   2 / N    6 ...BBA
>     L   2 / N    7 ......

This network have five real neurons and two bias neurons. This gives a total of seven neurons named from 0 to 6. The connections between these neurons can be seen in the matrix. “.” is a place where there is no connection, while a character tells how strong the connection is on a scale from a-z. The two real neurons in the hidden layer (neuron 3 and 4 in layer 1) has connection from the three neurons in the previous layer as is visible in the first two lines. The output neuron (6) has connections form the three neurons in the hidden layer 3 - 5 as is visible in the fourth line.

To simplify the matrix output neurons is not visible as neurons that connections can come from, and input and bias neurons are not visible as neurons that connections can go to.

This function appears in FANN >= 1.2.0.

### create_from_file

> bool create_from_file( | const std:: | string | & | configuration_file | )

Constructs a backpropagation neural network from a configuration file, which have been saved by save.

#### See also

save, save_to_fixed, fann_create_from_file

This function appears in FANN >= 1.0.0.

### save

> bool save( | const std:: | string | & | configuration_file | )

Save the entire network to a configuration file.

The configuration file contains all information about the neural network and enables create_from_file to create an exact copy of the neural network and all of the parameters associated with the neural network.

These two parameters (set_callback, set_error_log) are NOT saved to the file because they cannot safely be ported to a different location. Also temporary parameters generated during training like get_MSE is not saved.

#### Return

The function returns 0 on success and -1 on failure.

#### See also

create_from_file, save_to_fixed, fann_save

This function appears in FANN >= 1.0.0.

### save_to_fixed

> int save_to_fixed( | const std:: | string | & | configuration_file | )

Saves the entire network to a configuration file. But it is saved in fixed point format no matter which format it is currently in.

This is usefull for training a network in floating points, and then later executing it in fixed point.

The function returns the bit position of the fix point, which can be used to find out how accurate the fixed point network will be. A high value indicates high precision, and a low value indicates low precision.

A negative value indicates very low precision, and a very strong possibility for overflow. (the actual fix point will be set to 0, since a negative fix point does not make sence).

Generally, a fix point lower than 6 is bad, and should be avoided. The best way to avoid this, is to have less connections to each neuron, or just less neurons in each layer.

The fixed point use of this network is only intended for use on machines that have no floating point processor, like an iPAQ. On normal computers the floating point version is actually faster.

#### See also

create_from_file, save, fann_save_to_fixed

This function appears in FANN >= 1.0.0.

### train

> void train( | fann_type | * | input,
> | fann_type | * | desired_output | )

Train one iteration with a set of inputs, and a set of desired outputs. This training is always incremental training (see FANN::training_algorithm_enum), since only one pattern is presented.

#### Parameters

ann | The neural network structure
input | an array of inputs. This array must be exactly fann_get_num_input long.
desired_output | an array of desired outputs. This array must be exactly fann_get_num_output long.

#### See also

train_on_data, train_epoch, fann_train

This function appears in FANN >= 1.0.0.

### train_epoch

> float train_epoch( | const | training_data | & | data | )

Train one epoch with a set of training data.

Train one epoch with the training data stored in data. One epoch is where all of the training data is considered exactly once.

This function returns the MSE error as it is calculated either before or during the actual training. This is not the actual MSE after the training epoch, but since calculating this will require to go through the entire training set once more, it is more than adequate to use this value during training.

The training algorithm used by this function is chosen by the fann_set_training_algorithm function.

#### See also

train_on_data, test_data, fann_train_epoch

This function appears in FANN >= 1.2.0.

### train_on_data

> 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.

This training uses the training algorithm chosen by set_training_algorithm, and the parameters set for these training algorithms.

#### Parameters

ann | The neural network
data | The data, which should be used during training
max_epochs | The maximum number of epochs the training should continue
epochs_between_reports | The number of epochs between printing a status report to stdout. A value of zero means no reports should be printed.
desired_error | The desired get_MSE or get_bit_fail, depending on which stop function is chosen by set_train_stop_function.

Instead of printing out reports every epochs_between_reports, a callback function can be called (see set_callback).

#### See also

train_on_file, train_epoch, fann_train_on_data

This function appears in FANN >= 1.0.0.

### train_on_file

> 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.

#### See also

train_on_data, fann_train_on_file

This function appears in FANN >= 1.0.0.

### test

> fann_type * test( | fann_type | * | input,
> | fann_type | * | desired_output | )

Test with a set of inputs, and a set of desired outputs. This operation updates the mean square error, but does not change the network in any way.

#### See also

test_data, train, fann_test

This function appears in FANN >= 1.0.0.

### test_data

> float test_data( | const | training_data | & | data | )

Test a set of training data and calculates the MSE for the training data.

This function updates the MSE and the bit fail values.

#### See also

test, get_MSE, get_bit_fail, fann_test_data

This function appears in FANN >= 1.2.0.

### get_MSE

> float get_MSE()

Reads the mean square error from the network.

Reads the mean square error from the network. This value is calculated during training or testing, and can therefore sometimes be a bit off if the weights have been changed since the last calculation of the value.

#### See also

test_data, fann_get_MSE

This function appears in FANN >= 1.1.0.

### reset_MSE

> void reset_MSE()

Resets the mean square error from the network.

This function also resets the number of bits that fail.

#### See also

get_MSE, get_bit_fail_limit, fann_reset_MSE

This function appears in FANN >= 1.1.0

### set_callback

> void set_callback( | callback_type |  | callback,
> | void | * | user_data | )

Sets the callback function for use during training. The user_data is passed to the callback. It can point to arbitrary data that the callback might require and can be NULL if it is not used.

See FANN::callback_type for more information about the callback function.

The default callback function simply prints out some status information.

This function appears in FANN >= 2.0.0.

### print_parameters

> void print_parameters()

Prints all of the parameters and options of the neural network

#### See also

fann_print_parameters

This function appears in FANN >= 1.2.0.

### get_training_algorithm

> training_algorithm_enum get_training_algorithm()

Return the training algorithm as described by FANN::training_algorithm_enum. This training algorithm is used by train_on_data and associated functions.

Note that this algorithm is also used during cascadetrain_on_data, although only FANN::TRAIN_RPROP and FANN::TRAIN_QUICKPROP is allowed during cascade training.

The default training algorithm is FANN::TRAIN_RPROP.

#### See also

set_training_algorithm, FANN::training_algorithm_enum, fann_get_training_algorithm

This function appears in FANN >= 1.0.0.

### set_training_algorithm

> void set_training_algorithm( | training_algorithm_enum | training_algorithm | )

Set the training algorithm.

More info available in get_training_algorithm

This function appears in FANN >= 1.0.0.

### get_learning_rate

> float get_learning_rate()

Return the learning rate.

The learning rate is used to determine how aggressive training should be for some of the training algorithms (FANN::TRAIN_INCREMENTAL, FANN::TRAIN_BATCH, FANN::TRAIN_QUICKPROP). Do however note that it is not used in FANN::TRAIN_RPROP.

The default learning rate is 0.7.

#### See also

set_learning_rate, set_training_algorithm, fann_get_learning_rate

This function appears in FANN >= 1.0.0.

### set_learning_rate

> void set_learning_rate( | float | learning_rate | )

Set the learning rate.

More info available in get_learning_rate

This function appears in FANN >= 1.0.0.

### get_activation_function

> 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.

It is not possible to get activation functions for the neurons in the input layer.

Information about the individual activation functions is available at FANN::activation_function_enum.

#### Returns

The activation function for the neuron or -1 if the neuron is not defined in the neural network.

#### See also

set_activation_function_layer, set_activation_function_hidden, set_activation_function_output, set_activation_steepness, set_activation_function, fann_get_activation_function

This function appears in FANN >= 2.1.0

### set_activation_function

> 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.

It is not possible to set activation functions for the neurons in the input layer.

When choosing an activation function it is important to note that the activation functions have different range. FANN::SIGMOID is e.g. in the 0 - 1 range while FANN::SIGMOID_SYMMETRIC is in the -1 - 1 range and FANN::LINEAR is unbound.

Information about the individual activation functions is available at FANN::activation_function_enum.

The default activation function is FANN::SIGMOID_STEPWISE.

#### See also

set_activation_function_layer, set_activation_function_hidden, set_activation_function_output, set_activation_steepness, get_activation_function, fann_set_activation_function

This function appears in FANN >= 2.0.0.

### set_activation_function_layer

> 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.

It is not possible to set activation functions for the neurons in the input layer.

#### See also

set_activation_function, set_activation_function_hidden, set_activation_function_output, set_activation_steepness_layer, fann_set_activation_function_layer

This function appears in FANN >= 2.0.0.

### set_activation_function_hidden

> void set_activation_function_hidden(
> | activation_function_enum | activation_function
> )

Set the activation function for all of the hidden layers.

#### See also

set_activation_function, set_activation_function_layer, set_activation_function_output, set_activation_steepness_hidden, fann_set_activation_function_hidden

This function appears in FANN >= 1.0.0.

### set_activation_function_output

> void set_activation_function_output(
> | activation_function_enum | activation_function
> )

Set the activation function for the output layer.

#### See also

set_activation_function, set_activation_function_layer, set_activation_function_hidden, set_activation_steepness_output, fann_set_activation_function_output

This function appears in FANN >= 1.0.0.

### get_activation_steepness

> 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.

It is not possible to get activation steepness for the neurons in the input layer.

The steepness of an activation function says something about how fast the activation function goes from the minimum to the maximum. A high value for the activation function will also give a more agressive training.

When training neural networks where the output values should be at the extremes (usually 0 and 1, depending on the activation function), a steep activation function can be used (e.g. 1.0).

The default activation steepness is 0.5.

#### Returns

The activation steepness for the neuron or -1 if the neuron is not defined in the neural network.

#### See also

set_activation_steepness_layer, set_activation_steepness_hidden, set_activation_steepness_output, set_activation_function, set_activation_steepness, fann_get_activation_steepness

This function appears in FANN >= 2.1.0

### set_activation_steepness

> 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.

It is not possible to set activation steepness for the neurons in the input layer.

The steepness of an activation function says something about how fast the activation function goes from the minimum to the maximum. A high value for the activation function will also give a more agressive training.

When training neural networks where the output values should be at the extremes (usually 0 and 1, depending on the activation function), a steep activation function can be used (e.g. 1.0).

The default activation steepness is 0.5.

#### See also

set_activation_steepness_layer, set_activation_steepness_hidden, set_activation_steepness_output, set_activation_function, get_activation_steepness, fann_set_activation_steepness

This function appears in FANN >= 2.0.0.

### set_activation_steepness_layer

> 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.

It is not possible to set activation steepness for the neurons in the input layer.

#### See also

set_activation_steepness, set_activation_steepness_hidden, set_activation_steepness_output, set_activation_function_layer, fann_set_activation_steepness_layer

This function appears in FANN >= 2.0.0.

### set_activation_steepness_hidden

> void set_activation_steepness_hidden( | fann_type | steepness | )

Set the steepness of the activation steepness in all of the hidden layers.

#### See also

set_activation_steepness, set_activation_steepness_layer, set_activation_steepness_output, set_activation_function_hidden, fann_set_activation_steepness_hidden

This function appears in FANN >= 1.2.0.

### set_activation_steepness_output

> void set_activation_steepness_output( | fann_type | steepness | )

Set the steepness of the activation steepness in the output layer.

#### See also

set_activation_steepness, set_activation_steepness_layer, set_activation_steepness_hidden, set_activation_function_output, fann_set_activation_steepness_output

This function appears in FANN >= 1.2.0.

### get_train_error_function

> error_function_enum get_train_error_function()

Returns the error function used during training.

The error functions is described further in FANN::error_function_enum

The default error function is FANN::ERRORFUNC_TANH

#### See also

set_train_error_function, fann_get_train_error_function

This function appears in FANN >= 1.2.0.

### set_train_error_function

> void set_train_error_function( | error_function_enum | train_error_function | )

Set the error function used during training.

The error functions is described further in FANN::error_function_enum

#### See also

get_train_error_function, fann_set_train_error_function

This function appears in FANN >= 1.2.0.

### get_quickprop_decay

> 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. This is used to make sure that the weights do not become too high during training.

The default decay is -0.0001.

#### See also

set_quickprop_decay, fann_get_quickprop_decay

This function appears in FANN >= 1.2.0.

### set_quickprop_decay

> void set_quickprop_decay( | float | quickprop_decay | )

Sets the quickprop decay factor.

#### See also

get_quickprop_decay, fann_set_quickprop_decay

This function appears in FANN >= 1.2.0.

### get_quickprop_mu

> float get_quickprop_mu()

The mu factor is used to increase and decrease the step-size during quickprop training. The mu factor should always be above 1, since it would otherwise decrease the step-size when it was suppose to increase it.

The default mu factor is 1.75.

#### See also

set_quickprop_mu, fann_get_quickprop_mu

This function appears in FANN >= 1.2.0.

### set_quickprop_mu

> void set_quickprop_mu( | float | quickprop_mu | )

Sets the quickprop mu factor.

#### See also

get_quickprop_mu, fann_set_quickprop_mu

This function appears in FANN >= 1.2.0.

### get_rprop_increase_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.

The default increase factor is 1.2.

#### See also

set_rprop_increase_factor, fann_get_rprop_increase_factor

This function appears in FANN >= 1.2.0.

### set_rprop_increase_factor

> void set_rprop_increase_factor( | float | rprop_increase_factor | )

The increase factor used during RPROP training.

#### See also

get_rprop_increase_factor, fann_set_rprop_increase_factor

This function appears in FANN >= 1.2.0.

### get_rprop_decrease_factor

> 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.

The default decrease factor is 0.5.

#### See also

set_rprop_decrease_factor, fann_get_rprop_decrease_factor

This function appears in FANN >= 1.2.0.

### set_rprop_decrease_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.

#### See also

get_rprop_decrease_factor, fann_set_rprop_decrease_factor

This function appears in FANN >= 1.2.0.

### get_rprop_delta_zero

> float get_rprop_delta_zero()

The initial step-size is a small positive number determining how small the initial step-size may be.

The default value delta zero is 0.1.

#### See also

set_rprop_delta_zero, fann_get_rprop_delta_zero

This function appears in FANN >= 2.1.0.

### set_rprop_delta_zero

> 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.

#### See also

get_rprop_delta_zero, fann_set_rprop_delta_zero

This function appears in FANN >= 2.1.0.

### get_rprop_delta_min

> float get_rprop_delta_min()

The minimum step-size is a small positive number determining how small the minimum step-size may be.

The default value delta min is 0.0.

#### See also

set_rprop_delta_min, fann_get_rprop_delta_min

This function appears in FANN >= 1.2.0.

### set_rprop_delta_min

> 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.

#### See also

get_rprop_delta_min, fann_set_rprop_delta_min

This function appears in FANN >= 1.2.0.

### get_rprop_delta_max

> float get_rprop_delta_max()

The maximum step-size is a positive number determining how large the maximum step-size may be.

The default delta max is 50.0.

#### See also

set_rprop_delta_max, get_rprop_delta_min, fann_get_rprop_delta_max

This function appears in FANN >= 1.2.0.

### set_rprop_delta_max

> 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.

#### See also

get_rprop_delta_max, get_rprop_delta_min, fann_set_rprop_delta_max

This function appears in FANN >= 1.2.0.

### get_sarprop_weight_decay_shift

> float get_sarprop_weight_decay_shift()

The sarprop weight decay shift.

The default delta max is -6.644.

#### See also

set_sarprop_weight_decay_shift, <fann get_sarprop_weight_decay_shift>

This function appears in FANN >= 2.1.0.

### set_sarprop_weight_decay_shift

> void set_sarprop_weight_decay_shift( | float | sarprop_weight_decay_shift | )

Set the sarprop weight decay shift.

This function appears in FANN >= 2.1.0.

#### See also

get_sarprop_weight_decay_shift, fann_set_sarprop_weight_decay_shift

### get_sarprop_step_error_threshold_factor

> float get_sarprop_step_error_threshold_factor()

The sarprop step error threshold factor.

The default delta max is 0.1.

#### See also

set_sarprop_step_error_threshold_factor, <fann get_sarprop_step_error_threshold_factor>

This function appears in FANN >= 2.1.0.

### set_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.

This function appears in FANN >= 2.1.0.

#### See also

get_sarprop_step_error_threshold_factor, fann_set_sarprop_step_error_threshold_factor

### get_sarprop_step_error_shift

> float get_sarprop_step_error_shift()

The get sarprop step error shift.

The default delta max is 1.385.

#### See also

set_sarprop_step_error_shift, <fann get_sarprop_step_error_shift>

This function appears in FANN >= 2.1.0.

### set_sarprop_step_error_shift

> void set_sarprop_step_error_shift( | float | sarprop_step_error_shift | )

Set the sarprop step error shift.

This function appears in FANN >= 2.1.0.

#### See also

get_sarprop_step_error_shift, fann_set_sarprop_step_error_shift

### get_sarprop_temperature

> float get_sarprop_temperature()

The sarprop weight decay shift.

The default delta max is 0.015.

#### See also

set_sarprop_temperature, <fann get_sarprop_temperature>

This function appears in FANN >= 2.1.0.

### set_sarprop_temperature

> void set_sarprop_temperature( | float | sarprop_temperature | )

Set the sarprop_temperature.

This function appears in FANN >= 2.1.0.

#### See also

get_sarprop_temperature, fann_set_sarprop_temperature

### get_num_input

> unsigned int get_num_input()

Get the number of input neurons.

This function appears in FANN >= 1.0.0.

### get_num_output

> unsigned int get_num_output()

Get the number of output neurons.

This function appears in FANN >= 1.0.0.

### get_total_neurons

> unsigned int get_total_neurons()

Get the total number of neurons in the entire network. This number does also include the bias neurons, so a 2-4-2 network has 2+4+2 +2(bias) = 10 neurons.

This function appears in FANN >= 1.0.0.

### get_total_connections

> unsigned int get_total_connections()

Get the total number of connections in the entire network.

This function appears in FANN >= 1.0.0.

### get_decimal_point

> unsigned int get_decimal_point()

Returns the position of the decimal point in the ann.

This function is only available when the ANN is in fixed point mode.

The decimal point is described in greater detail in the tutorial <Fixed Point Usage>.

#### See also

<Fixed Point Usage>, get_multiplier, save_to_fixed, training_data::save_train_to_fixed, fann_get_decimal_point

This function appears in FANN >= 1.0.0.

### get_multiplier

> unsigned int get_multiplier()

Returns the multiplier that fix point data is multiplied with.

This function is only available when the ANN is in fixed point mode.

The multiplier is the used to convert between floating point and fixed point notation. A floating point number is multiplied with the multiplier in order to get the fixed point number and visa versa.

The multiplier is described in greater detail in the tutorial <Fixed Point Usage>.

#### See also

<Fixed Point Usage>, get_decimal_point, save_to_fixed, training_data::save_train_to_fixed, fann_get_multiplier

This function appears in FANN >= 1.0.0.

### get_network_type

> network_type_enum get_network_type()

Get the type of neural network it was created as.

#### Returns

The neural network type from enum FANN::network_type_enum

#### See Also

fann_get_network_type

This function appears in FANN >= 2.1.0

### get_connection_rate

> float get_connection_rate()

Get the connection rate used when the network was created

#### Returns

The connection rate

#### See also

fann_get_connection_rate

This function appears in FANN >= 2.1.0

### get_num_layers

> unsigned int get_num_layers()

Get the number of layers in the network

#### Returns

The number of layers in the neural network

#### See also

fann_get_num_layers

This function appears in FANN >= 2.1.0

### get_layer_array

> void get_layer_array( | unsigned | int | * | layers | )

Get the number of neurons in each layer in the network.

Bias is not included so the layers match the create methods.

The layers array must be preallocated to at least sizeof(unsigned int) * get_num_layers() long.

#### See also

fann_get_layer_array

This function appears in FANN >= 2.1.0

### get_bias_array

> void get_bias_array( | unsigned | int | * | bias | )

Get the number of bias in each layer in the network.

The bias array must be preallocated to at least sizeof(unsigned int) * get_num_layers() long.

#### See also

fann_get_bias_array

This function appears in FANN >= 2.1.0

### get_connection_array

> void get_connection_array( | connection | * | connections | )

Get the connections in the network.

The connections array must be preallocated to at least sizeof(struct fann_connection) * get_total_connections() long.

#### See also

fann_get_connection_array

This function appears in FANN >= 2.1.0

### set_weight_array

> void set_weight_array( |  | connection | * | connections,
> | unsigned | int |  | num_connections | )

Set connections in the network.

Only the weights can be changed, connections and weights are ignored if they do not already exist in the network.

The array must have sizeof(struct fann_connection) * num_connections size.

#### See also

fann_set_weight_array

This function appears in FANN >= 2.1.0

### set_weight

> void set_weight( | unsigned | int | from_neuron,
> | unsigned | int | to_neuron,
> |  | fann_type | weight | )

Set a connection in the network.

Only the weights can be changed. The connection/weight is ignored if it does not already exist in the network.

#### See also

fann_set_weight

This function appears in FANN >= 2.1.0

### get_learning_momentum

> float get_learning_momentum()

Get the learning momentum.

The learning momentum can be used to speed up FANN::TRAIN_INCREMENTAL training. A too high momentum will however not benefit training. Setting momentum to 0 will be the same as not using the momentum parameter. The recommended value of this parameter is between 0.0 and 1.0.

The default momentum is 0.

#### See also

set_learning_momentum, set_training_algorithm

This function appears in FANN >= 2.0.0.

### set_learning_momentum

> void set_learning_momentum( | float | learning_momentum | )

Set the learning momentum.

More info available in get_learning_momentum

This function appears in FANN >= 2.0.0.

### get_train_stop_function

> stop_function_enum get_train_stop_function()

Returns the the stop function used during training.

The stop function is described further in FANN::stop_function_enum

The default stop function is FANN::STOPFUNC_MSE

#### See also

get_train_stop_function, get_bit_fail_limit

This function appears in FANN >= 2.0.0.

### set_train_stop_function

> void set_train_stop_function( | stop_function_enum | train_stop_function | )

Set the stop function used during training.

The stop function is described further in FANN::stop_function_enum

#### See also

get_train_stop_function

This function appears in FANN >= 2.0.0.

### get_bit_fail_limit

> fann_type get_bit_fail_limit()

Returns the bit fail limit used during training.

The bit fail limit is used during training when the FANN::stop_function_enum is set to FANN_STOPFUNC_BIT.

The limit is the maximum accepted difference between the desired output and the actual output during training. Each output that diverges more than this limit is counted as an error bit. This difference is divided by two when dealing with symmetric activation functions, so that symmetric and not symmetric activation functions can use the same limit.

The default bit fail limit is 0.35.

#### See also

set_bit_fail_limit

This function appears in FANN >= 2.0.0.

### set_bit_fail_limit

> void set_bit_fail_limit( | fann_type | bit_fail_limit | )

Set the bit fail limit used during training.

#### See also

get_bit_fail_limit

This function appears in FANN >= 2.0.0.

### get_bit_fail

> 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). The bits are counted in all of the training data, so this number can be higher than the number of training data.

This value is reset by reset_MSE and updated by all the same functions which also updates the MSE value (e.g. test_data, train_epoch)

#### See also

FANN::stop_function_enum, get_MSE

This function appears in FANN >= 2.0.0

### cascadetrain_on_data

> 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. This algorithm adds neurons to the neural network while training, which means that it needs to start with an ANN without any hidden layers. The neural network should also use shortcut connections, so create_shortcut should be used to create the ANN like this:

>     net.create_shortcut(2, train_data.num_input_train_data(), train_data.num_output_train_data());

This training uses the parameters set using the set_cascade_..., but it also uses another training algorithm as it’s internal training algorithm. This algorithm can be set to either FANN::TRAIN_RPROP or FANN::TRAIN_QUICKPROP by set_training_algorithm, and the parameters set for these training algorithms will also affect the cascade training.

#### Parameters

data | The data, which should be used during training
max_neuron | The maximum number of neurons to be added to neural network
neurons_between_reports | The number of neurons between printing a status report to stdout. A value of zero means no reports should be printed.
desired_error | The desired fann_get_MSE or fann_get_bit_fail, depending on which stop function is chosen by fann_set_train_stop_function.

Instead of printing out reports every neurons_between_reports, a callback function can be called (see set_callback).

#### See also

train_on_data, cascadetrain_on_file, fann_cascadetrain_on_data

This function appears in FANN >= 2.0.0.

### cascadetrain_on_file

> 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.

#### See also

fann_cascadetrain_on_data, fann_cascadetrain_on_file

This function appears in FANN >= 2.0.0.

### get_cascade_output_change_fraction

> 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. If the training stagnates, the training of the output connections will be ended and new candidates will be prepared.

#### This means

If the MSE does not change by a fraction of get_cascade_output_change_fraction during a period of get_cascade_output_stagnation_epochs, the training of the output connections is stopped because the training has stagnated.

If the cascade output change fraction is low, the output connections will be trained more and if the fraction is high they will be trained less.

The default cascade output change fraction is 0.01, which is equalent to a 1% change in MSE.

#### See also

set_cascade_output_change_fraction, get_MSE, get_cascade_output_stagnation_epochs, fann_get_cascade_output_change_fraction

This function appears in FANN >= 2.0.0.

### set_cascade_output_change_fraction

> void set_cascade_output_change_fraction( | float | cascade_output_change_fraction | )

Sets the cascade output change fraction.

#### See also

get_cascade_output_change_fraction, fann_set_cascade_output_change_fraction

This function appears in FANN >= 2.0.0.

### get_cascade_output_stagnation_epochs

> 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.

See more info about this parameter in get_cascade_output_change_fraction.

The default number of cascade output stagnation epochs is 12.

#### See also

set_cascade_output_stagnation_epochs, get_cascade_output_change_fraction, fann_get_cascade_output_stagnation_epochs

This function appears in FANN >= 2.0.0.

### set_cascade_output_stagnation_epochs

> void set_cascade_output_stagnation_epochs(
> | unsigned | int | cascade_output_stagnation_epochs
> )

Sets the number of cascade output stagnation epochs.

#### See also

get_cascade_output_stagnation_epochs, fann_set_cascade_output_stagnation_epochs

This function appears in FANN >= 2.0.0.

### get_cascade_candidate_change_fraction

> 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. If the training stagnates, the training of the candidate neurons will be ended and the best candidate will be selected.

#### This means

If the MSE does not change by a fraction of get_cascade_candidate_change_fraction during a period of get_cascade_candidate_stagnation_epochs, the training of the candidate neurons is stopped because the training has stagnated.

If the cascade candidate change fraction is low, the candidate neurons will be trained more and if the fraction is high they will be trained less.

The default cascade candidate change fraction is 0.01, which is equalent to a 1% change in MSE.

#### See also

set_cascade_candidate_change_fraction, get_MSE, get_cascade_candidate_stagnation_epochs, fann_get_cascade_candidate_change_fraction

This function appears in FANN >= 2.0.0.

### set_cascade_candidate_change_fraction

> void set_cascade_candidate_change_fraction(
> | float | cascade_candidate_change_fraction
> )

Sets the cascade candidate change fraction.

#### See also

get_cascade_candidate_change_fraction, fann_set_cascade_candidate_change_fraction

This function appears in FANN >= 2.0.0.

### get_cascade_candidate_stagnation_epochs

> 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.

See more info about this parameter in get_cascade_candidate_change_fraction.

The default number of cascade candidate stagnation epochs is 12.

#### See also

set_cascade_candidate_stagnation_epochs, get_cascade_candidate_change_fraction, fann_get_cascade_candidate_stagnation_epochs

This function appears in FANN >= 2.0.0.

### set_cascade_candidate_stagnation_epochs

> void set_cascade_candidate_stagnation_epochs(
> | unsigned | int | cascade_candidate_stagnation_epochs
> )

Sets the number of cascade candidate stagnation epochs.

#### See also

get_cascade_candidate_stagnation_epochs, fann_set_cascade_candidate_stagnation_epochs

This function appears in FANN >= 2.0.0.

### get_cascade_weight_multiplier

> 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. This parameter is usually between 0 and 1, and is used to make the training a bit less aggressive.

The default weight multiplier is 0.4

#### See also

set_cascade_weight_multiplier, fann_get_cascade_weight_multiplier

This function appears in FANN >= 2.0.0.

### set_cascade_weight_multiplier

> void set_cascade_weight_multiplier( | fann_type | cascade_weight_multiplier | )

Sets the weight multiplier.

#### See also

get_cascade_weight_multiplier, fann_set_cascade_weight_multiplier

This function appears in FANN >= 2.0.0.

### get_cascade_candidate_limit

> fann_type get_cascade_candidate_limit()

The candidate limit is a limit for how much the candidate neuron may be trained. The limit is a limit on the proportion between the MSE and candidate score.

Set this to a lower value to avoid overfitting and to a higher if overfitting is not a problem.

The default candidate limit is 1000.0

#### See also

set_cascade_candidate_limit, fann_get_cascade_candidate_limit

This function appears in FANN >= 2.0.0.

### set_cascade_candidate_limit

> void set_cascade_candidate_limit( | fann_type | cascade_candidate_limit | )

Sets the candidate limit.

#### See also

get_cascade_candidate_limit, fann_set_cascade_candidate_limit

This function appears in FANN >= 2.0.0.

### get_cascade_max_out_epochs

> 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.

The default max out epochs is 150

#### See also

set_cascade_max_out_epochs, fann_get_cascade_max_out_epochs

This function appears in FANN >= 2.0.0.

### set_cascade_max_out_epochs

> void set_cascade_max_out_epochs( | unsigned | int | cascade_max_out_epochs | )

Sets the maximum out epochs.

#### See also

get_cascade_max_out_epochs, fann_set_cascade_max_out_epochs

This function appears in FANN >= 2.0.0.

### get_cascade_max_cand_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.

The default max candidate epochs is 150

#### See also

set_cascade_max_cand_epochs, fann_get_cascade_max_cand_epochs

This function appears in FANN >= 2.0.0.

### set_cascade_max_cand_epochs

> void set_cascade_max_cand_epochs( | unsigned | int | cascade_max_cand_epochs | )

Sets the max candidate epochs.

#### See also

get_cascade_max_cand_epochs, fann_set_cascade_max_cand_epochs

This function appears in FANN >= 2.0.0.

### get_cascade_num_candidates

> 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).

The actual candidates is defined by the get_cascade_activation_functions and get_cascade_activation_steepnesses arrays. These arrays define the activation functions and activation steepnesses used for the candidate neurons. If there are 2 activation functions in the activation function array and 3 steepnesses in the steepness array, then there will be 2x3=6 different candidates which will be trained. These 6 different candidates can be copied into several candidate groups, where the only difference between these groups is the initial weights. If the number of groups is set to 2, then the number of candidate neurons will be 2x3x2=12. The number of candidate groups is defined by set_cascade_num_candidate_groups.

The default number of candidates is 6x4x2 = 48

#### See also

get_cascade_activation_functions, get_cascade_activation_functions_count, get_cascade_activation_steepnesses, get_cascade_activation_steepnesses_count, get_cascade_num_candidate_groups, fann_get_cascade_num_candidates

This function appears in FANN >= 2.0.0.

### get_cascade_activation_functions_count

> unsigned int get_cascade_activation_functions_count()

The number of activation functions in the get_cascade_activation_functions array.

The default number of activation functions is 6.

#### See also

get_cascade_activation_functions, set_cascade_activation_functions, fann_get_cascade_activation_functions_count

This function appears in FANN >= 2.0.0.

### get_cascade_activation_functions

> activation_function_enum * get_cascade_activation_functions()

The cascade activation functions array is an array of the different activation functions used by the candidates.

See get_cascade_num_candidates for a description of which candidate neurons will be generated by this array.

#### See also

get_cascade_activation_functions_count, set_cascade_activation_functions, FANN::activation_function_enum

This function appears in FANN >= 2.0.0.

### set_cascade_activation_functions

> 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. The array must be just as long as defined by the count.

See get_cascade_num_candidates for a description of which candidate neurons will be generated by this array.

#### See also

get_cascade_activation_steepnesses_count, get_cascade_activation_steepnesses, fann_set_cascade_activation_functions

This function appears in FANN >= 2.0.0.

### get_cascade_activation_steepnesses_count

> unsigned int get_cascade_activation_steepnesses_count()

The number of activation steepnesses in the get_cascade_activation_functions array.

The default number of activation steepnesses is 4.

#### See also

get_cascade_activation_steepnesses, set_cascade_activation_functions, fann_get_cascade_activation_steepnesses_count

This function appears in FANN >= 2.0.0.

### get_cascade_activation_steepnesses

> fann_type *get_cascade_activation_steepnesses()

The cascade activation steepnesses array is an array of the different activation functions used by the candidates.

See get_cascade_num_candidates for a description of which candidate neurons will be generated by this array.

The default activation steepnesses is {0.25, 0.50, 0.75, 1.00}

#### See also

set_cascade_activation_steepnesses, get_cascade_activation_steepnesses_count, fann_get_cascade_activation_steepnesses

This function appears in FANN >= 2.0.0.

### set_cascade_activation_steepnesses

> void set_cascade_activation_steepnesses(
> |  | fann_type | * | cascade_activation_steepnesses,
> | unsigned | int |  | cascade_activation_steepnesses_count
> )

Sets the array of cascade candidate activation steepnesses. The array must be just as long as defined by the count.

See get_cascade_num_candidates for a description of which candidate neurons will be generated by this array.

#### See also

get_cascade_activation_steepnesses, get_cascade_activation_steepnesses_count, fann_set_cascade_activation_steepnesses

This function appears in FANN >= 2.0.0.

### get_cascade_num_candidate_groups

> 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.

This number can be used to have more candidates without having to define new parameters for the candidates.

See get_cascade_num_candidates for a description of which candidate neurons will be generated by this parameter.

The default number of candidate groups is 2

#### See also

set_cascade_num_candidate_groups, fann_get_cascade_num_candidate_groups

This function appears in FANN >= 2.0.0.

### set_cascade_num_candidate_groups

> void set_cascade_num_candidate_groups(
> | unsigned | int | cascade_num_candidate_groups
> )

Sets the number of candidate groups.

#### See also

get_cascade_num_candidate_groups, fann_set_cascade_num_candidate_groups

This function appears in FANN >= 2.0.0.

### scale_train

> void scale_train( | training_data | & | data | )

Scale input and output data based on previously calculated parameters.

#### See also

descale_train, fann_scale_train

This function appears in FANN >= 2.1.0.

### descale_train

> void descale_train( | training_data | & | data | )

Descale input and output data based on previously calculated parameters.

#### See also

scale_train, fann_descale_train

This function appears in FANN >= 2.1.0.

### set_input_scaling_params

> 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.

#### See also

set_output_scaling_params, fann_set_input_scaling_params

This function appears in FANN >= 2.1.0.

### set_output_scaling_params

> 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.

#### See also

set_input_scaling_params, fann_set_output_scaling_params

This function appears in FANN >= 2.1.0.

### set_scaling_params

> 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.

#### See also

clear_scaling_params, fann_set_scaling_params

This function appears in FANN >= 2.1.0.

### clear_scaling_params

> bool clear_scaling_params()

Clears scaling parameters.

#### See also

set_scaling_params, fann_clear_scaling_params

This function appears in FANN >= 2.1.0.

### scale_input

> void scale_input( | fann_type | * | input_vector | )

Scale data in input vector before feed it to ann based on previously calculated parameters.

#### See also

descale_input, scale_output, fann_scale_input

This function appears in FANN >= 2.1.0.

### scale_output

> void scale_output( | fann_type | * | output_vector | )

Scale data in output vector before feed it to ann based on previously calculated parameters.

#### See also

descale_output, scale_input, fann_scale_output

This function appears in FANN >= 2.1.0.

### descale_input

> void descale_input( | fann_type | * | input_vector | )

Scale data in input vector after get it from ann based on previously calculated parameters.

#### See also

scale_input, descale_output, fann_descale_input

This function appears in FANN >= 2.1.0.

### descale_output

> void descale_output( | fann_type | * | output_vector | )

Scale data in output vector after get it from ann based on previously calculated parameters.

#### See also

scale_output, descale_input, fann_descale_output

This function appears in FANN >= 2.1.0.

### set_error_log

> void set_error_log( | FILE | * | log_file | )

Change where errors are logged to.

If log_file is NULL, no errors will be printed.

If neural_net is empty i.e. ann is NULL, the default log will be set. The default log is the log used when creating a neural_net. This default log will also be the default for all new structs that are created.

The default behavior is to log them to stderr.

#### See also

struct fann_error, fann_set_error_log

This function appears in FANN >= 1.1.0.

### get_errno

> unsigned int get_errno()

Returns the last error number.

#### See also

fann_errno_enum, fann_reset_errno, fann_get_errno

This function appears in FANN >= 1.1.0.

### reset_errno

> void reset_errno()

Resets the last error number.

This function appears in FANN >= 1.1.0.

### reset_errstr

> void reset_errstr()

Resets the last error string.

This function appears in FANN >= 1.1.0.

### get_errstr

> std::string get_errstr()

Returns the last errstr.

This function calls fann_reset_errno and fann_reset_errstr

This function appears in FANN >= 1.1.0.

### print_error

> void print_error()

Prints the last error to stderr.

This function appears in FANN >= 1.1.0.

> 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.
Close