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AI::FANN zef:jjatria last updated on 2021-12-15

3bd5f6105ccebe2920c7704cf3ef8976313c07d4/

NAME

AI::FANN

SYNOPSIS

``` raku

See below for details on export tags

use AI::FANN :enum;

Hidden

Input | Output

\ | /

given AI::FANN.new: layers => [ 2, 3, 1 ] { LEAVE .?destroy; # Make sure to clean up after yourself

# A sample data set for solving the XOR problem
my $data = AI::FANN::TrainData.new: pairs => [
    [ -1, -1 ] => [ -1 ],
    [ -1,  1 ] => [  1 ],
    [  1, -1 ] => [  1 ],
    [  1,  1 ] => [ -1 ],
];

LEAVE $data.?destroy;

.activation-function: FANN_SIGMOID_SYMMETRIC;

# Train for up to 500,000 epochs
# or until the MSE is less than 0.001
# with no reports to STDOUT
.train: $data,
    desired-error          => 0.001,
    max-epochs             => 500_000,
    epochs-between-reports => 0;

say .run: [ 1, -1 ];

}

OUTPUT:

(0.9508717060089111)

# DESCRIPTION

This distribution provides native bindings for the Fast Artificial Neural
Network library (FANN). The aim of the library is to be easy to use, which
makes it a good entry point and suitable for working on machine learning
prototypes.

Creating networks, training them, and running them on input data can be done
without much knowledge of the internals of ANNs, although the ANNs created
will still be powerful and effective. Users with more experience and desiring
more control will also find methods to parameterize most of the aspects of the
ANNs, allowing for the creation of specialized and highly optimal ANNs.

## Installation

The bindings for Raku make use of the system version of FANN. Please refer to
your platform's instructions on how to install the library, or follow the
instructions for [compiling from source](https://github.com/libfann/fann#to-install).

## Error handling

The default behaviour for libfann is to print errors to standard error.
In order to give the user more control over how to handle these errors,
AI::FANN will raise exceptions whenever an error is encountered. When
possible, these will be raised before an actual call to libfann is ever made.

When this is not possible, errors raised by libfann will be wrapped into
exceptions of type X::AI::FANN. When capturing these, a string version of
the error will be available in its `message` method, while its `code` method
will return the error as a member of the [AI::FANN::Error](#aifannerror) enum.

# METHODS

The methods described below include readers, mutators, and methods that
operate on the internal state of the network in more complex ways.

Some methods, like [num-input](#num-input) are only for reading the
internal state of the network, and will always return the value that was
requested.

Other methods, like [activation-function](#activation-function) will act as
both readers and mutators depending on the arguments that are passed.

When acting as readers, named parameters may be used to specify the scope
of the reading. Some of these may be mandatory.

When acting as mutators, the new value should be passed as one or more
positional arguments, with any named parameters specifying the possible scope
of the mutation. All mutators always return the calling object, to allow
for chaining. These will be marked in the signatures as `returns self`.

Most other methods, like [reset-error](#reset-error) or [train](#train), will
also return the calling object, and may take named parameters. Some methods
have different return values, like [test](#test) or [save](#save) that reflect
the result of the operation. In all cases, the signature should specify the
return value.

The sections below follow roughly the same structure as that used
in the documentation of [libfann](http://libfann.github.io/fann/docs).

Whenever possible, the underlying method that is being called will be
indicated next to the method signatures.

Please refer to the libfann documentation for additional details.

## Creation and Execution

### new

``` raku
# fann_create_shortcut
# fann_create_sparse
# fann_create_standard
multi method new (
           :@layers,
    Num()  :$connection-rate,
    Bool() :$shortcut,
) returns AI::FANN

# fann_create_from_file
multi method new (
    IO()   :$path,
) returns AI::FANN

Creates a new AI::FANN neural network. The constructor can be called in one of two ways.

If the path parameter is set, it will be coerced to a IO::Path and the network will be created based on the contents of that file (see save for how this file can be created).

Alternatively, a list of integers can be passed as the layers parameter to specify the number of neurons in each layer, with the input layer being the first in the list, the output layer being the last in the list, and any remaining ones describing hidden layers.

By default, this will create a 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.

To create a neural network that is not fully connected, a connection-rate parameter can be set to a number between 0 and 1, where 0 is a network with no connections, and 1 is a fully connected network.

If the shortcut flag is set, the resulting network will be fully connected, and it will have connections between neurons in non-contiguous 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.

The connection-rate and shortcut parameters are not compatible, and using both is an error.

run

``` raku

fann_run

multi method run ( CArray[num32] $input ) returns CArray[num32]

multi method run ( *@input ) returns List

Run the input through the neural network, returning an array of outputs. The
output array will have one value per neuron in the output layer.

The type of the return value depends on the type of the input.

If the input is provided as a [CArray[num32]][CArray] object, it will be used
as-is and the return value will be of the same type. This is the fastest way
to call this method.

If the input is passed as a [List] or [Array], it will be internally converted
to its C representation, and the return value will be a [List] object.

### bit-fail

``` raku
# fann_get_bit_fail
method bit-fail returns Int

Returns the number of fail bits, or the number of output neurons which differ more than the bit fail limit (see 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-error and updated by all the same functions which also update the mean square error (eg. test).

connection-rate

``` raku

fann_get_connection_rate

method connection-rate returns Num

Get the connection rate used when the network was created.

### num-input

``` raku
# fann_get_num_input
method num-input returns Int

Get the number of input neurons.

num-layers

``` raku

fann_get_num_layers

method num-layers returns Int

Get the number of layers in the network.

### num-output

``` raku
# fann_get_num_output
method num-output returns Int

Get the number of output neurons.

total-connections

``` raku

fann_get_total_connection

method total-connections returns Int

Get the total number of connections in the entire network.

### total-neurons

``` raku
# fann_get_total_neurons
method total-neurons returns Int

Get the total number of neurons in the entire network. This number includes the bias neurons, so a 2-4-2 network has 2+4+2 neurons, plus 2 bias neurons (one for each layer except the output one) for a total of 10.

network-type

``` raku

fann_get_network_type

method network-type returns AI::FANN::NetType

Get the type of neural network it was created as.

### layer-array

``` raku
# fann_get_layer_array
method layer-array returns List

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

Bias is not included so the layers match the ones used in the constructor.

bias-array

``` raku

fann_get_bias_array

method bias-array returns List

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

### connection-array

``` raku
# fann_get_connection_array
method connection-array returns List

Get the connections in the network as a List of AI::FANN::Connection.

These objects encapsulate a connection between two neurons. They hold a number identifying the source and target neurons, which can be read with the from-neuron and to-neuron methods respectively; and the weight of the connection, which can be read with the weight method.

The weight method returns a writable container, which means that a new value can be set by using it on the left side of an assignment. Connection objects thus modified can then be passed to the weights method described below to update the connections of the network.

weights

``` raku multi method weights () returns List

fann_set_weight

multi method weights ( Num() $weight, Int() :$from! where * >= 0, Int() :$to! where * >= 0, ) returns self

multi method weights ( *@connections where { .all ~~ AI::FANN::Connection }, ) returns self

Called with no arguments, returns the list of all connection weights as a
[List] of [Num]. The weights will be in the same order as the connections
returned by [connection-array](#connection-array).

This method can also be used as a setter if called with either a weight as
a positional argument and the numbers identifying the source and target
neurons as the `:from` and `:to` named parameters respectively.

Alternatively, one or more AI::FANN::Connection objects (such as those
returned by [connection-array](#connection-array) can be passed as positional
arguments, in which case the weight in each connection will be used as the new
value. See the documentation of that method for details.

Using this method as a setter returns the calling ANN, to allow for chaining.

### randomize-weights

``` raku
# fann_randomize_weights
method randomize-weights (
    Range:D $range,
) returns self

Give each connection a random weight between the endpoints of the specified Range object.

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

This method is an alias for randomise-weights.

randomise-weights

``` raku

fann_randomize_weights

method randomise-weights ( Range:D $range, ) returns self

Give each connection a random weight between the endpoints of the specified
[Range] object.

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

This method is an alias for [randomize-weights](#randomize-weights).

### init-weights

``` raku
# fann_init_weights
method init-weights (
    AI::FANN::TrainData:D $data,
) returns self

Initialize the weights using Widrow + Nguyen’s algorithm.

This function behaves similarly to 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 requires an AI::FANN::TrainData as its only positional argument. This should be the same data set used to train the network.

``` raku

fann_print_connections

method print-connections returns self

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

As an example, this is the output from 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 has 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.

A period (".") indicates 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) have connections from the three
neurons in the previous layer as is visible in the first two lines. The output
neuron (6) has connections from the three neurons in the hidden layer 3 - 5,
as shown in the fourth line.

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

### print-parameters

``` raku
# fann_print_parameters
method print-parameters returns self

Prints all of the parameters and options of the network.

clone

``` raku

fann_copy

method clone returns AI::FANN

Returns an exact copy of the calling AI::FANN object.

### destroy

``` raku
# fann_destroy
method destroy returns Nil

Destroy the internal representation of this network. It's a good idea to make sure to call this for every object that has been created.

File Input / Output

save

``` raku

fann_save

method save ( IO() $path ) returns Bool

Save the entire network to a configuration file.

The configuration file contains all information about the neural network and
can be passed as the `path` parameter to the constructor to create an exact
copy of the network and all of the associated parameters.

The only parameters that are not saved are the callback, error log, and user
data, since they cannot safely be ported to a different location. Note that
temporary parameters generated during training, like the mean square error,
are also not saved.

## Training

The methods in this section support fixed topology training.

When using this method of training, the size and topology of the ANN is
determined in advance and the training alters the weights in order to minimize
the difference between the desired output values and the actual output values.

For evolving topology training, see the [Cascade Training](#cascade-training)
section below.

### train

``` raku
multi method train (
    @input,
    @output,
) returns self

# fann_train
multi method train (
    CArray[num32] $input,
    CArray[num32] $output,
) returns self

# fann_train_epoch
# fann_train_on_data
multi method train (
    AI::FANN::TrainData:D $data,
    Int() :$max-epochs,
    Int() :$epochs-between-reports,
    Num() :$desired-error,
) returns self

# fann_train_epoch
# fann_train_on_file
multi method train (
    IO() $path,
    Int() :$max-epochs,
    Int() :$epochs-between-reports,
    Num() :$desired-error,
) returns self

This method is used to train the neural network.

The first two candidates train a single iteration using the specified set of inputs and desired outputs in the input and output parameters. Inputs and outputs can be passed as CArray[num32] objects, or as arrays of numeric values, which will be converted internally to their C representation.

Since only one pattern is presented, training done this way is always incremental training (FANN_TRAIN_INCREMENTAL in the AI::FANN::Train enum).

The last two candidates train instead on an entire dataset. The first one takes a mandatory AI::FANN::TrainData object, while the second takes instead a filename that will be used to generate a training dataset internally. Both of these candidates will default to running a single iteration or "epoch". They can instead be used to train for a period of time by specifying the maximum number of iterations, the target error, and the number of iterations between reports. See callback for the code that gets executed to generate this report.

In both cases, the training uses the algorithm set with training-algorithm, and the parameters set for these training algorithms (see Training Algorithm Parameters below).

test

``` raku multi method test ( @input, @output, ) returns List

fann_test

multi method test ( CArray[num32] $input, CArray[num32] $output, ) returns CArray[num32]

multi method test ( AI::FANN::TrainData $data, ) returns Num

multi method train ( IO() $path, ) returns Num

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

Inputs and outputs can be passed as CArray[num32] objects, or as arrays of
numeric values, which will be converted internally to their C representation.

These candidates return the same as the equivalent invokations of [run](#run).

Two more calling patterns are offered as shortcuts.

A AI::FANN::TrainData object can be passed as the `data` parameter, in which
case the network will be tested with all the input and output data it
contains.

Alternatively, the `path` parameter can be set to a value that can be coerced
to a [IO::Path] object. In this case, an AI::FANN::TrainData will be
internally read from the contents of this file and used as above.

These candidates return the updated mean square error for the network.

### callback

``` raku
multi method callback (
    :$delete where :so,
) returns self

# fann_set_callback
method callback (
    &callback where {
        .cando: \(
            AI::FANN            $fann,
            AI::FANN::TrainData $data,
            uint32              $max-epochs,
            uint32              $epochs-between-reports,
            num32               $desired-error,
            uint32              $epoch,
        );
    }
) returns self

If called with a Callable as the first positional argument, this method will set that as the training callback. If called with a single :delete argument that evaluates to True, any previously set callback will be cleared, and the default callback will be restored.

The default callback function simply prints out some status information.

The callback will be called during training if using a AI::FANN::TrainData object either directly (with the :data argument to train) or indirectly (with the :path argument to the same method). It will be called once during the first epoch, and again every time the epoch is divisible by the value provided in the :epochs-between-reports argument to train.

The callback will be called with the AI::FANN object being trained, the AI::FANN::TrainData object that is being used for training, as well as the maximum number of allowed training epochs, the number of epochs between reports, and the target error for training that were set when training started as positional arguments. Additionally, the current epoch will also be passed as the final argument to the callback.

The callback can interrupt the training by returning False or a value that, when coerced into an Int evaluates to -1.

activation-function

``` raku

fann_get_activation_function

multi method activation-function ( Int :$layer!, Int :$neuron!, ) returns AI::FANN::ActivationFunc

fann_set_activation_function

fann_set_activation_function_layer

multi method activation-function ( AI::FANN::ActivationFunc $function, Int :$layer!, Int :$neuron, ) returns self

fann_set_activation_function_hidden

fann_set_activation_function_output

multi method activation-function ( AI::FANN::ActivationFunc $function, Bool() :$hidden, Bool() :$output, ) returns self

If called with no positional arguments, this method returns the activation
function for the neuron number and layer specified in the `:neuron` and
`:layer` parameters respectively, counting the input layer as layer 0. It is
not possible to get activation functions for the neurons in the input layer:
trying to do so is an error.

If called with a member of the
[AI::FANN::ActivationFunc](#aifannactivationfunc) enum as the first positional
argument, then this function will instead _set_ this as the activation
function for the specified layer and neuron, and return the calling AI::FANN
object.

When used as a setter, specifying the layer is always required. This can
be done with the `:layer` parameter, as described above, or with the `:hidden`
or `:output` flags. The `:hidden` flag will set the activation function for
all neurons in _all_ hidden layers, while the `:output` flag will do so only
for those in the output layer.

When setting the activation function using the `:layer` parameter, the
`:neuron` parameter is optional. If none is set, all neurons in the specified
layer will be modified.

### activation-steepness

``` raku
# fann_get_activation_steepness
multi method activation-steepness (
    Int    :$layer!,
    Int    :$neuron!,
) returns Num

# fann_set_activation_steepness
# fann_set_activation_steepness_layer
multi method activation-steepness (
    Num()   $steepness,
    Int    :$layer!,
    Int    :$neuron,
) returns self

# fann_set_activation_steepness_hidden
# fann_set_activation_steepness_output
multi method activation-steepness (
    Num()   $steepness,
    Bool() :$hidden,
    Bool() :$output,
) returns self

If called with no positional arguments, this method returns the activation steepness for the neuron number and layer specified in the :neuron and :layer parameters respectively, counting the input layer as layer 0. It is not possible to get activation functions for the neurons in the input layer: trying to do so is an error.

If called with a positional argument, it will be coerced to a Num and this function will instead set this as the activation steepness for the specified layer and neuron and return the calling AI::FANN object.

When used as a setter, specifying the layer is always required. This can be done with the :layer parameter, as described above, or with the :hidden or output flags. The :hidden flag will set the activation function for all neurons in all hidden layers, while the output flag will do so only for those in the output layer.

When setting the activation steepness using the :layer parameter, the :neuron parameter is optional. If none is set, all neurons in the specified layer will be modified.

training-algorithm

``` raku

fann_get_training_algorithm

multi method training-algorithm returns AI::FANN::Train

fann_set_training_algorithm

multi method training-algorithm ( AI::FANN::Train $algorithm, ) returns self

If called with no positional arguments, this method returns the training
algorithm as per the [AI::FANN::Train](#aifanntrain) enum. The training
algorithm is used eg. when running [train](#train) or
[cascade-train](#cascade-train) with a AI::FANN::TrainData object.

If a member of that enum is passed as the first positional argument, this
method instead sets that as the new training algorithm and returns it.

Note that only `FANN_TRAIN_RPROP` and `FANN_TRAIN_QUICKPROP` are allowed
during cascade training.

The default training algorithm is `FANN_TRAIN_RPROP`.

### train-error-function

``` raku
# fann_get_train_error_function
multi method train-error-function returns AI::FANN::ErrorFunc

# fann_set_train_error_function
multi method train-error-function (
    AI::FANN::ErrorFunc $function,
) returns self

If called with no positional arguments, this method returns the error function used during training as per the AI::FANN::ErrorFunc enum.

If a member of that enum is passed as the first positional argument, this method instead sets that as the new training error function and returns it.

The default training error function if FANN_ERRORFUNC_TANH.

train-stop-function

``` raku

fann_get_train_stop_function

multi method train-stop-function returns AI::FANN::StopFunc

fann_set_train_stop_function

multi method train-stop-function ( AI::FANN::StopFunc $function, ) returns self

If called with no positional arguments, this method returns the stop function
used during training as per the [AI::FANN::StopFunc](#aifannstopfunc) enum.

If a member of that enum is passed as the first positional argument, this
method instead sets that as the new training stop function and returns it.

The default training stop function if `FANN_STOPFUNC_MSE`.

### bit-fail-limit

``` raku
# fann_get_bit_fail_limit
multi method bit-fail-limit returns Num

# fann_set_bit_fail_limit
multi method bit-fail-limit (
    Num() $limit,
) returns self

If called with no positional arguments, this method returns the bit fail limit used during training. If called with a positional argument, it will be coerced to a Num and set as the new limit.

The bit fail limit is used during training when the stop function is set to FANN_STOPFUNC_BIT (see train-stop-function).

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 asymmetric activation functions can use the same limit.

The default bit fail limit is 0.35.

learning-rate

``` raku multi method learning-rate returns Num

multi method learning-rate ( Num() $rate, ) returns self

If called with no positional arguments, this method returns the learning rate
used during training. If called with a positional argument, it will be coerced
to a [Num] and set as the new 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.

### learning-momentum

``` raku
multi method learning-momentum returns Num

multi method learning-momentum (
    Num() $momentum,
) returns self

If called with no positional arguments, this method returns the learning momentum used during training. If called with a positional argument, it will be coerced to a Num and set as the new learning momentum.

The learning momentum can be used to speed up FANN_TRAIN_INCREMENTAL training. Too high a momentum will however not benefit training. Setting the momentum to 0 will be the same as not using the momentum parameter. The recommended value of this parameter is between 0 and 1.

The default momentum is 0.

scale

``` raku

fann_scale_train

multi method scale ( AI::FANN::TrainData:D $data, ) returns self

fann_scale_input

fann_scale_output

multi method scale ( CArray[num32] :$input, CArray[num32] :$output, ) returns self

fann_scale_input

fann_scale_output

multi method scale ( :@input, :@output, ) returns self

This method will scale a set of inputs and outputs according to the scaling
parameters set in this network (see [scaling](#scaling) for how these are
calculated and set).

If called with an AI::FANN::TrainData object, the scaling will apply to its
input and output data. Alternatively, the `:input` and `:output` named
parameters can be set to either [CArray[num32]][CArray] or to [Array] objects
with the data to scale, which will be modified in-place according to the
scaling parameters calculated for inputs and outputs respectively. See
[descale](#descale) for a way to reverse this manipulation.

Calling this method before setting scaling parameters (with
[scaling](#scaling)) is an error. Calling this method after clearing the
scaling parameters is not.

### descale

``` raku
# fann_descale_train
multi method descale (
    AI::FANN::TrainData:D $data,
) returns self

# fann_descale_input
# fann_descale_output
multi method descale (
    CArray[num32] :$input,
    CArray[num32] :$output,
) returns self

# fann_descale_input
# fann_descale_output
multi method descale (
    :@input,
    :@output,
) returns self

This method will reverse the scaling performed by scale.

If called with an AI::FANN::TrainData object, the descaling will apply to its input and output data. Alternatively, the :input and :output named parameters can be set to either CArray[num32] or to Array objects with the data to descale, which will be modified in-place according to the scaling parameters calculated for inputs and outputs respectively.

Calling this method before setting scaling parameters (with scaling) is an error. Calling this method after clearing the scaling parameters is not.

scaling

``` raku

fann_set_scaling_params

fann_set_input_scaling_params

fann_set_output_scaling_params

multi method scaling ( AI::FANN::TrainData:D $data, Range :$output, Range :$input, ) returns self

fann_clear_scaling_params

multi method scaling ( :$delete! where :so, ) returns self

Takes an AI::FANN::TrainData object that will be used to calculate the
scaling parameters as a positional parameter, and [Range] objects representing
the desired range for input and output values in the `:input` and `:output`
named parameters respectively. At least one of these must be specified.

The scaling parameters set by this method can be cleared with the `:delete`
flag. This will reset them a default value of -1..1.

### reset-error

``` raku
# fann_reset_MSE
method reset-error returns self

Resets the mean square error from the network, and the number of bits that fail.

mean-square-error

``` raku

fann_get_MSE

method mean-square-error returns Num

Reads the mean square error from the network. This value is calculated during
training or testing (see [train](#train) and [test](#test) above), and can
therefore sometimes be a bit off if the weights have been changed since the
last calculation of the value.

## Training Algorithm Parameters

These methods control the parameters used for specific training algorithms.

### quickprop-decay

``` raku
multi method quickprop-decay returns Num

multi method quickprop-decay (
    Num() $value where * <= 0,
) returns self

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.

If called with no positional arguments, this method returns the current decay value. If called with a positional argument, it will be coerced to a Num and set as the new decay.

The default decay is -0.0001.

quickprop-mu

``` raku multi method quickprop-mu returns Num

multi method quickprop-mu ( Num() $value, ) returns self

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 supposed to increase it.

If called with no positional arguments, this method returns the current
mu factor. If called with a positional argument, it will be coerced
to a [Num] and set as the new mu factor.

The default mu factor is 1.75.

### rprop-increase

``` raku
multi method rprop-increase returns Num

multi method rprop-increase (
    Num() $value where * > 1,
) returns self

The increase factor is a value larger than 1, which is used to increase the step-size during RPROP training.

If called with no positional arguments, this method returns the current increase factor. If called with a positional argument, it will be coerced to a Num and set as the new increase factor.

The default increase factor is 1.2.

rprop-decrease

``` raku multi method rprop-decrease returns Num

multi method rprop-decrease ( Num() $value where * < 1, ) returns self

The increase factor is a value larger than 1, which is used to decrease the
step-size during RPROP training.

If called with no positional arguments, this method returns the current
decrease factor. If called with a positional argument, it will be coerced
to a [Num] and set as the new decrease factor.

The default increase factor is 0.5.

### rprop-delta-range

``` raku
multi method rprop-delta-range returns Range

multi method rprop-delta-range (
    Range $value where { not .infinite },
) returns self

The delta range determines the minimum and maximum allowed values for the step-size used during RPROP training.

If called with no positional arguments, this method returns the current delta range. If called with a Range as a positional argument, it will be set as the new delta range.

The default delta range is 0..50.

rprop-delta-zero

``` raku multi method rprop-delta-zero returns Num

multi method rprop-delta-zero ( Num() $value where * > 0, ) returns self

The delta zero is a positive number determining the initial step size used
during RPROP training.

If called with no positional arguments, this method returns the current
initial step size. If called with a positional argument, it will be coerced
to a [Num] and set as the new initial step size.

The default delta zero is 0.1.

### sarprop-weight-decay-shift

``` raku
multi method sarprop-weight-decay-shift returns Num

multi method sarprop-weight-decay-shift (
    Num() $value,
) returns self

If called with no positional arguments, this method returns the current weight decay shift used during SARPROP training. If called with a positional argument, it will be coerced to a Num and set as the new weight decay shift.

The default value is -6.644.

sarprop-error-threshold

``` raku multi method sarprop-error-threshold returns Num

multi method sarprop-error-threshold ( Num() $value, ) returns self

If called with no positional arguments, this method returns the current error
threshold factor used during SARPROP training. If called with a positional
argument, it will be coerced to a [Num] and set as the new error threshold
factor.

The default value is 0.1.

### sarprop-step-error-shift

``` raku
multi method sarprop-step-error-shift returns Num

multi method sarprop-step-error-shift (
    Num() $value,
) returns self

If called with no positional arguments, this method returns the current step error shift used during SARPROP training. If called with a positional argument, it will be coerced to a Num and set as the new step error shift.

The default value is 1.385.

sarprop-temperature

``` raku multi method sarprop-temperature returns Num

multi method sarprop-temperature ( Num() $value, ) returns self

If called with no positional arguments, this method returns the current decay
shift used during SARPROP training. If called with a positional argument, it
will be coerced to a [Num] and set as the new decay shift.

The default value is 0.015.

## Cascade Training

Cascade training differs from ordinary training in that it starts with an
empty neural network and then adds neurons one by one, while it trains the
neural network. The main benefit of this approach is that you do not have to
guess the number of hidden layers and neurons prior to training, but cascade
training has also proved better at solving some problems.

The basic idea of cascade training is that a number of candidate neurons are
trained separate from the real network, then the most promising of these
candidate neurons is inserted into the neural network. Then the output
connections are trained and new candidate neurons are prepared. The candidate
neurons are created as shortcut connected neurons in a new hidden layer, which
means that the final neural network will consist of a number of hidden layers
with one shortcut connected neuron in each.

For methods supporting ordinary, or fixed topology training, see the
[Training](#training) section above.

### cascade-train

``` raku
# fann_cascadetrain_on_data
multi method cascade-train (
    AI::FANN::TrainData:D $data,
    Int() :$max-neurons!,
    Int() :$neurons-between-reports!,
    Num() :$desired-error!,
) returns self

# fann_cascadetrain_on_file
multi method cascade-train (
    IO() $path,
    Int() :$max-neurons!,
    Int() :$neurons-between-reports!,
    Num() :$desired-error!,
) returns self

Trains the network on an entire dataset for a period of time using the Cascade2 training algorithm. The dataset can be passed as an AI::FANN::TrainData object in the data parameter. Alternatively, if the path is set, it will be coerced to an IO::Path object and the training data will be read from there instead.

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 the shortcut flag should be used when invoking new, like this

``` raku my $ann = AI::FANN.new: :shortcut, layers => [ $data.num-input, $data.num-output ];

### cascade-num-candidates

``` raku
# fann_get_cascade_num_candidates
multi method cascade-num-candidates returns Int

Returns the number of candidates used during training.

The number of candidates is calculated by multiplying the value returned by cascade-activation-functions-count, cascade-activation-steepnesses-count, and cascade-num-candidate-groups.

The actual candidates is defined by the cascade-activation-functions and 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 can be set with cascade-num-candidate-groups.

The default number of candidates is 6x4x2 = 48

cascade-num-candidate-groups

``` raku

fann_get_cascade_num_candidate_groups

multi method cascade-num-candidate-groups returns Int

fann_set_cascade_num_candidate_groups

multi method cascade-num-candidate-groups ( Int $groups ) returns self

If called with no positional arguments, this method returns the number of
candidate groups used during training. If called with an Int as a positional
argument, it will be set as the new value.

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 [cascade-num-candidates](#cascade-num-candidates) for a description of
which candidate neurons will be generated by this parameter.

The default number of candidate groups is 2

### cascade-candidate-limit

``` raku
# fann_get_cascade_candidate_limit
multi method cascade-candidate-limit returns Num

# fann_set_cascade_candidate_limit
multi method cascade-candidate-limit ( Num() $value ) returns self

The candidate limit is a limit for how much the candidate neuron may be trained. It limits 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.

If called with no positional arguments, this method returns the current candidate limit. If called with a positional argument, it will be coerced to a Num and set as the new candidate limit.

The default candidate limit is 1000.

cascade-weight-multiplier

``` raku

fann_get_cascade_weight_multiplier

multi method cascade-weight-multiplier returns Num

fann_set_cascade_weight_multiplier

multi method cascade-weight-multiplier ( Num() $value ) returns self

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.

If called with no positional arguments, this method returns the current
weight multiplier. If called with a positional argument, it will be coerced
to a [Num] and set as the new weight multiplier.

The default weight multiplier is 0.4

### cascade-output-change-fraction

``` raku
# fann_get_cascade_output_change_fraction
multi method cascade-output-change-fraction returns Num

# fann_set_cascade_output_change_fraction
multi method cascade-output-change-fraction ( Num() $value ) returns self

The cascade output change fraction is a number between 0 and 1 determining how large a fraction the mean-square-error should change within 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.

If the MSE does not change by a fraction of the value returned by this method during a period of 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.

If called with no positional arguments, this method returns the current output change fraction. If called with a positional argument, it will be coerced to a Num and set as the new fraction.

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

cascade-candidate-change-fraction

``` raku

fann_get_cascade_candidate_change_fraction

multi method cascade-candidate-change-fraction returns Num

fann_set_cascade_candidate_change_fraction

multi method cascade-candidate-change-fraction ( Num() $value ) returns self

The cascade candidate change fraction is a number between 0 and 1 determining
how large a fraction the [mean-square-error](#mean-square-error) should change
within [cascade-output-stagnation-epochs](#cascade-output-stagnation-epochs)
during training of the candidate neurons, in order for the training not to
stagnate. If the training stagnates, the training of candidate neurons will be
ended and the best candidate will be selected.

If the MSE does not change by a fraction of the value returned by this method
during a period of
[cascade-candidate-stagnation-epochs](#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.

If called with no positional arguments, this method returns the current
candidate change fraction. If called with a positional argument, it will be
coerced to a [Num] and set as the new fraction.

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

### cascade-candidate-stagnation-epochs

``` raku
# fann_get_cascade_candidate_stagnation_epochs
multi method cascade-candidate-stagnation-epochs returns Num

# fann_set_cascade_candidate_stagnation_epochs
multi method cascade-candidate-stagnation-epochs ( Num() $value ) returns self

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 cascade-candidate-change-fraction.

If called with no positional arguments, this method returns the current candidate stagnation epochs. If called with a positional argument, it will be coerced to a Num and set as the new candidate stagnation epochs.

The default number of cascade candidate stagnation epochs is 12.

cascade-output-stagnation-epochs

``` raku

fann_get_cascade_output_stagnation_epochs

multi method cascade-output-stagnation-epochs returns Num

fann_set_cascade_output_stagnation_epochs

multi method cascade-output-stagnation-epochs ( Num() $value ) returns self

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
[cascade-output-change-fraction](#cascade-output-change-fraction).

If called with no positional arguments, this method returns the current
output stagnation epochs. If called with a positional argument, it will be
coerced to a [Num] and set as the new output stagnation epochs.

The default number of cascade output stagnation epochs is 12.

### cascade-activation-steepnesses-count

``` raku
# fann_get_cascade_activation_steepnesses_count
multi method cascade-activation_steepnesses_count returns Int

Returns the number of activation steepnesses in the list returned by cascade-activation-functions.

The default number of activation steepnesses is 4.

cascade-candidate-epochs

``` raku

fann_get_cascade_min_cand_epochs

fann_get_cascade_max_cand_epochs

multi method cascade-candidate-epochs returns Range

fann_set_cascade_min_cand_epochs

fann_set_cascade_max_cand_epochs

multi method cascade-candidate-epochs ( Range $value where { not .infinite }, ) returns self

multi method cascade-candidate-epochs ( Int :$min, Int :$max, ) returns self

The candidate epochs determines the minimum and maximum number of epochs the
input connections to the candidates may be trained before adding a new
candidate neuron.

If called with no positional arguments, this method returns the current
candidate epoch range. If called with a [Range] as a positional argument, it will
be set as the new candidate epoch range. This method can also be called with
a value for the minimum or maximum end of the range as the `:min` and `:max`
named parameters respectively.

The default candidate epoch range is 50..150

### cascade-output-epochs

``` raku
# fann_get_cascade_min_out_epochs
# fann_get_cascade_max_out_epochs
multi method cascade-output-epochs returns Range

# fann_set_cascade_min_out_epochs
# fann_set_cascade_max_out_epochs
multi method cascade-output-epochs (
    Range $value where { not .infinite },
) returns self

multi method cascade-output-epochs (
    Int :$min,
    Int :$max,
) returns self

The output epochs determines the minimum and maximum number of epochs the output connections may be trained after adding a new candidate neuron.

If called with no positional arguments, this method returns the current output epoch range. If called with a Range as a positional argument, it will be set as the new output epoch range. This method can also be called with a value for the minimum or maximum end of the range as the :min and :max named parameters respectively.

The default output epoch range is 50..150

cascade-activation-steepnesses

``` raku

fann_get_cascade_activation_steepnesses

multi method cascade-activation-steepnesses returns List

fann_set_cascade_activation_steepnesses

multi method cascade-activation-steepnesses ( CArray[num32] $steepnesses, ) returns self

multi method cascade-activation-steepnesses ( *@steepnesses, ) returns self

If called with no positional arguments, this method returns the array of
activation steepnesses used by the candidates. See
[cascade-num-candidates](#cascade-num-candidates) for a description of which
candidate neurons will be generated by this array.

If called with a [CArray[num32]][CArray] object as the first positional
argument, this method will instead use that as the new value. Alternatively,
the values that would be in that array can be passed as positional arguments
and they'll be internally converted to a C representation to use instead.

In either case, the new array must be just as long as defined by the count
(see [cascade-activation-steepnesses-count](#cascade-activation-steepnesses-count)).

The default activation steepnesses are [ 0.25, 0.50, 0.75, 1.00 ].

### cascade-activation-functions

``` raku
# fann_get_cascade_activation_functions
multi method cascade-activation-functions returns List

# fann_set_cascade_activation_functions
multi method cascade-activation-functions (
    CArray[num32] $functions,
) returns self

multi method cascade-activation-functions (
    *@functions,
) returns self

If called with no positional arguments, this method returns the array of activation functions used by the candidates. See cascade-num-candidates for a description of which candidate neurons will be generated by this array.

If called with a CArray[num32] object as the first positional argument, this method will instead use that as the new value. Alternatively, the values that would be in that array can be passed as positional arguments and they'll be internally converted to a C representation to use instead.

In either case, the new array must be just as long as defined by the count (see cascade-activation-functions-count).

The default activation functions are [ FANN_SIGMOID, FANN_SIGMOID_SYMMETRIC, FANN_GAUSSIAN, FANN_GAUSSIAN_SYMMETRIC, FANN_ELLIOT, FANN_ELLIOT_SYMMETRIC, FANN_SIN_SYMMETRIC, FANN_COS_SYMMETRIC, FANN_SIN, FANN_COS ].

EXPORT TAGS

AI::FANN exports nothing by default. However, the following enums are available and can be exported using the :enum tag to export all enums, or the :error tag to export only the AI::FANN::Error enum.

AI::FANN::NetType

AI::FANN::ActivationFunc

The activation functions used for the neurons during training. The activation functions can either be defined for a group of neurons by calling activation-function with the :hidden or :output parameters or it can be defined for a single neuron or layer with the :layer and :neuron parameters.

The functions are described with functions where

The steepness of an activation function is defined in the same way by calling activation-steepness.

See the documentation for those functions for details.

AI::FANN::Train

The training algorithms used when training on AI::FANN::TrainData with functions like train with the :path or :data arguments. The incremental training 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.

AI::FANN::ErrorFunc

Error function used during training.

AI::FANN::StopFunc

Stop criteria used during training.

AI::FANN::Error

Used to define error events on AI::FANN and AI::FANN::TrainData objects.

REFERENCES

COPYRIGHT AND LICENSE

Copyright 2021 José Joaquín Atria

This library is free software; you can redistribute it and/or modify it under the Artistic License 2.0.