#ifndef FANN_CPP_H_INCLUDED
#define FANN_CPP_H_INCLUDED

#include <memory>
#include <iterator>
/*
 *  Fast Artificial Neural Network (fann) C++ Wrapper
 *  Copyright (C) 2004-2006 created by freegoldbar (at) yahoo dot com
 *
 *  This wrapper is free software; you can redistribute it and/or
 *  modify it under the terms of the GNU Lesser General Public
 *  License as published by the Free Software Foundation; either
 *  version 2.1 of the License, or (at your option) any later version.
 *
 *  This wrapper is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
 *  Lesser General Public License for more details.
 *
 *  You should have received a copy of the GNU Lesser General Public
 *  License along with this library; if not, write to the Free Software
 *  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 *
 */

/*
 *  Title: FANN C++ Wrapper
 *
 *  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 (C) 2004-2006 created by <freegoldbar@yahoo.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.
 *  -  C++ style constructors have been implemented to make network creation
 *       easier
 *  -  The neural network and training data is automatically cleaned
 *       up in the destructors
 *  -  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.
 *
 */

#include <assert.h>
#include <stdarg.h>
#include <string>
#include "fann_data_cpp.h"
#include "fann_training_data_cpp.h"

/* Namespace: FANN
    The FANN namespace groups the C++ wrapper definitions */
namespace FANN {
/* Class: neural_net
    <neural_net> is the main neural network class used for both training and execution

    Encapsulation of a neural network <struct fann> and
    associated C API functions.
*/
class neural_net {
 public:
  /* Constructor: neural_net(network_type_enum net_type, unsigned int num_layers, const unsigned int
     *layers)

      Creates a neural network of the desired <network_type_enum> net_type, based on array of
     layers.

      Parameters:
          net_type - The desired network type of the neural network
          num_layers - The total number of layers including the input and the output layer.
          layers - array of the layer sizes

      NOTE: if layers does not have the same size as num_layers, the result is undefined

      Example:
        >FANN::neural_net net(LAYER, 3, (unsigned int[]) {2, 3, 1});

      This function appears in FANN >= 2.3.0.
   */
  neural_net(network_type_enum net_type, unsigned int num_layers, const unsigned int *layers) {
    switch (net_type) {
      case LAYER:
        ann = fann_create_standard_array(num_layers, layers);
        break;
      case SHORTCUT:
        ann = fann_create_shortcut_array(num_layers, layers);
        break;
    }
    assert(ann != NULL);
  }

  /* Constructor: neural_net(network_type_enum net_type, InputIterator layersBeginIterator,
     InputIterator layersEndIterator)

      Creates a neural network of the desired <network_type_enum> net_type, based on iterator to the
     layers.

      Parameters:
          net_type - The desired network type of the neural network
          layersBeginIterator - begin iterator to the collection of unsigned int layers
          layersEndIterator - end iterator to the collection of unsigned int layers

      Example:
        >vector<unsigned int> layers{2, 3, 4, 5};
        >neural_net net(LAYER, layers.begin(), layers.end());

      This function appears in FANN >= 2.3.0.
   */
  template <class InputIterator>
  neural_net(network_type_enum net_type, InputIterator layersBeginIterator,
             InputIterator layersEndIterator) {
    unsigned int num_layers =
        static_cast<unsigned int>(std::distance(layersBeginIterator, layersEndIterator));
    unsigned int *layers = new unsigned int[num_layers];
    std::copy(layersBeginIterator, layersEndIterator, layers);

    switch (net_type) {
      case LAYER:
        ann = fann_create_standard_array(num_layers, layers);
        break;
      case SHORTCUT:
        ann = fann_create_shortcut_array(num_layers, layers);
        break;
    }
    delete[] layers;
    assert(ann != NULL);
  }

  /* Constructor: neural_net(network_type_enum net_type, unsigned int num_layers, ...)

          Creates a neural network of the desired <network_type_enum> net_type.

          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.

      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(num_layers, num_input, num_hidden, num_output);

          This function appears in FANN >= 2.3.0.
  */
  neural_net(network_type_enum net_type, unsigned int num_layers, ...) {
    std::unique_ptr<unsigned int[]> data(new unsigned int[num_layers]);

    va_list layers;
    va_start(layers, num_layers);
    for (unsigned int i = 0; i < num_layers; i++) data.get()[i] = va_arg(layers, unsigned int);
    va_end(layers);

    switch (net_type) {
      case LAYER:
        ann = fann_create_standard_array(num_layers, data.get());
        break;
      case SHORTCUT:
        ann = fann_create_shortcut_array(num_layers, data.get());
        break;
    }
    assert(ann != NULL);
  }

  /* Constructor: neural_net(float connection_rate, unsigned int num_layers, ...)

          Creates a standard backpropagation neural network, which is sparsely connected, this will
     default the <network_type_enum> to <LAYER>

          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.

          This function appears in FANN >= 2.3.0.
  */
  neural_net(float connection_rate, unsigned int num_layers, ...) {
    std::unique_ptr<unsigned int[]> data(new unsigned int[num_layers]);

    va_list layers;
    va_start(layers, num_layers);
    for (unsigned int i = 0; i < num_layers; i++) data.get()[i] = va_arg(layers, unsigned int);
    va_end(layers);

    ann = fann_create_sparse_array(connection_rate, num_layers, data.get());
    assert(ann != NULL);
  }

  /* Constructor: neural_net(float connection_rate, unsigned int num_layers, const unsigned int
     *layers)

          Creates a standard backpropagation neural network, which is sparsely connected, this will
     default the <network_type_enum> to <LAYER>

          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. layers - Integer values determining the number of neurons in each
     layer starting with the input layer and ending with the output layer.

          This function appears in FANN >= 2.3.0.
  */
  neural_net(float connection_rate, unsigned int num_layers, const unsigned int *layers) {
    ann = fann_create_sparse_array(connection_rate, num_layers, layers);
    assert(ann != NULL);
  }

  /* Constructor: neural_net(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>

     This function appears in FANN >= 2.3.0.
   */
  neural_net(const std::string &configuration_file) {
    ann = fann_create_from_file(configuration_file.c_str());
    assert(ann != NULL);
  }

  /* Constructor neural_net(const neural_net &other)

      Creates a copy the other neural_net.
      */
  neural_net(const neural_net &other) : ann(NULL) { copy_from_struct_fann(other.ann); }

  /* Constructor: neural_net(struct fann *other)

     Creates a copy the other neural_net.
      */
  neural_net(struct fann *other) { copy_from_struct_fann(other); }

  /* Constructor: neural_net() - DEPRECATED

      Creates an empty neural net.
      Use one of the create functions to create the neural network.

     NOTE: As of version 2.3.0 it recommended to create neural networks using the constructors
     instead of creating an empty network and setting the internal structure of that. This method is
     hence discouraged and will be deprecated later on.

      See also:
                  <create_standard>, <create_sparse>, <create_shortcut>,
                  <create_standard_array>, <create_sparse_array>, <create_shortcut_array>
  */
  neural_net() : ann(NULL) {}

  /* Method: copy_from_struct_fann - DEPRECATED

     Set the internal fann struct to a copy of other

     NOTE: As of version 2.3.0 it recommended to create neural networks using the constructors
     instead of creating an empty network and setting the internal structure of that. This method is
     hence discouraged and will be deprecated later on.
  */
  void copy_from_struct_fann(struct fann *other) {
    destroy();
    if (other != NULL) ann = fann_copy(other);
  }

  /* Destructor: ~neural_net

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

      See also:
          <destroy>
  */
#ifdef USE_VIRTUAL_DESTRUCTOR
  virtual
#endif

      ~neural_net() {
    destroy();
  }

  /* Method: destroy

      Destructs the entire network. Called automatically by the destructor.

      See also:
          <~neural_net>
  */
  void destroy() {
    if (ann != NULL) {
      user_context *user_data = static_cast<user_context *>(fann_get_user_data(ann));
      if (user_data != NULL) delete user_data;

      fann_destroy(ann);
      ann = NULL;
    }
  }

  /* Method: create_standard - DEPRECATED

          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);

     NOTE: As of version 2.3.0 it recommended to create neural networks using the constructors
     instead of creating an empty network and setting the internal structure of that. This method is
     hence discouraged and will be deprecated later on.

          See also:
                  <create_standard_array>, <create_sparse>, <create_shortcut>,
                  <fann_create_standard_array>

          This function appears in FANN >= 2.0.0.
  */
  bool create_standard(unsigned int num_layers, ...) {
    std::unique_ptr<unsigned int[]> data(new unsigned int[num_layers]);

    va_list layers;
    va_start(layers, num_layers);
    for (unsigned int i = 0; i < num_layers; i++) data.get()[i] = va_arg(layers, unsigned int);
    va_end(layers);

    bool status = create_standard_array(num_layers, data.get());
    return status;
  }

  /* Method: create_standard_array - DEPRECATED

     Just like <create_standard>, but with an array of layer sizes
     instead of individual parameters.

     NOTE: As of version 2.3.0 it recommended to create neural networks using the constructors
     instead of creating an empty network and setting the internal structure of that. This method is
     hence discouraged and will be deprecated later on.

          See also:
                  <create_standard>, <create_sparse>, <create_shortcut>,
                  <fann_create_standard>

          This function appears in FANN >= 2.0.0.
  */
  bool create_standard_array(unsigned int num_layers, const unsigned int *layers) {
    destroy();
    ann = fann_create_standard_array(num_layers, layers);
    return (ann != NULL);
  }

  /* Method: create_sparse - DEPRECATED

          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.

     NOTE: As of version 2.3.0 it recommended to create neural networks using the constructors
     instead of creating an empty network and setting the internal structure of that. This method is
     hence discouraged and will be deprecated later on.

          See also:
                  <create_standard>, <create_sparse_array>, <create_shortcut>,
                  <fann_create_sparse>

          This function appears in FANN >= 2.0.0.
  */
  bool create_sparse(float connection_rate, unsigned int num_layers, ...) {
    std::unique_ptr<unsigned int[]> data(new unsigned int[num_layers]);

    va_list layers;
    va_start(layers, num_layers);
    for (unsigned int i = 0; i < num_layers; i++) data.get()[i] = va_arg(layers, unsigned int);
    va_end(layers);

    bool status = create_sparse_array(connection_rate, num_layers, data.get());
    return status;
  }

  /* Method: create_sparse_array - DEPRECATED
     Just like <create_sparse>, but with an array of layer sizes
     instead of individual parameters.

     See <create_sparse> for a description of the parameters.

     NOTE: As of version 2.3.0 it recommended to create neural networks using the constructors
     instead of creating an empty network and setting the internal structure of that. This method is
     hence discouraged and will be deprecated later on.

     See also:
                  <create_standard>, <create_sparse>, <create_shortcut>,
                  <fann_create_sparse_array>

          This function appears in FANN >= 2.0.0.
  */
  bool create_sparse_array(float connection_rate, unsigned int num_layers,
                           const unsigned int *layers) {
    destroy();
    ann = fann_create_sparse_array(connection_rate, num_layers, layers);
    return (ann != NULL);
  }

  /* Method: create_shortcut - DEPRECATED

          Creates a standard backpropagation neural network, which is 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.

      NOTE: As of version 2.3.0 it recommended to create neural networks using the constructors
     instead of creating an empty network and setting the internal structure of that. This method is
     hence discouraged and will be deprecated later on.

          See also:
                  <create_standard>, <create_sparse>, <create_shortcut_array>,
                  <fann_create_shortcut>

          This function appears in FANN >= 2.0.0.
  */
  bool create_shortcut(unsigned int num_layers, ...) {
    std::unique_ptr<unsigned int[]> data(new unsigned int[num_layers]);

    va_list layers;
    va_start(layers, num_layers);
    for (unsigned int i = 0; i < num_layers; i++) data.get()[i] = va_arg(layers, unsigned int);
    va_end(layers);

    bool status = create_shortcut_array(num_layers, data.get());
    return status;
  }

  /* Method: create_shortcut_array - DEPRECATED

     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.

      NOTE: As of version 2.3.0 it recommended to create neural networks using the constructors
     instead of creating an empty network and setting the internal structure of that. This method is
     hence discouraged and will be deprecated later on.

          See also:
                  <create_standard>, <create_sparse>, <create_shortcut>,
                  <fann_create_shortcut_array>

          This function appears in FANN >= 2.0.0.
  */
  bool create_shortcut_array(unsigned int num_layers, const unsigned int *layers) {
    destroy();
    ann = fann_create_shortcut_array(num_layers, layers);
    return (ann != NULL);
  }

  /* Method: 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.

          See also:
                  <test>, <fann_run>

          This function appears in FANN >= 1.0.0.
  */
  fann_type *run(fann_type *input) {
    if (ann == NULL) {
      return NULL;
    }
    return fann_run(ann, input);
  }

  /* Method: randomize_weights

          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.
  */
  void randomize_weights(fann_type min_weight, fann_type max_weight) {
    if (ann != NULL) {
      fann_randomize_weights(ann, min_weight, max_weight);
    }
  }

  /* Method: init_weights

          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.
  */
  void init_weights(const training_data &data) {
    if ((ann != NULL) && (data.train_data != NULL)) {
      fann_init_weights(ann, data.train_data);
    }
  }

  /* Method: 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.
  */
  void print_connections() {
    if (ann != NULL) {
      fann_print_connections(ann);
    }
  }

  /* Method: create_from_file - DEPRECATED

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

     NOTE: As of version 2.3.0 it recommended to create neural networks using the constructors
     instead of creating an empty network and setting the internal structure of that. This method is
     hence discouraged and will be deprecated later on.

     See also:
          <save>, <save_to_fixed>, <fann_create_from_file>

     This function appears in FANN >= 1.0.0.
   */
  bool create_from_file(const std::string &configuration_file) {
    destroy();
    ann = fann_create_from_file(configuration_file.c_str());
    return (ann != NULL);
  }

  /* Method: save

     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.
   */
  bool save(const std::string &configuration_file) {
    if (ann == NULL) {
      return false;
    }
    if (fann_save(ann, configuration_file.c_str()) == -1) {
      return false;
    }
    return true;
  }

  /* Method: save_to_fixed

     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 useful 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.
  */
  int save_to_fixed(const std::string &configuration_file) {
    int fixpoint = 0;
    if (ann != NULL) {
      fixpoint = fann_save_to_fixed(ann, configuration_file.c_str());
    }
    return fixpoint;
  }

#ifndef FIXEDFANN

  /* Method: train

     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.
   */
  void train(fann_type *input, fann_type *desired_output) {
    if (ann != NULL) {
      fann_train(ann, input, desired_output);
    }
  }

  /* Method: train_epoch
      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.
   */
  float train_epoch(const training_data &data) {
    float mse = 0.0f;
    if ((ann != NULL) && (data.train_data != NULL)) {
      mse = fann_train_epoch(ann, data.train_data);
    }
    return mse;
  }

  /* Method: train_on_data

     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.
  */
  void train_on_data(const training_data &data, unsigned int max_epochs,
                     unsigned int epochs_between_reports, float desired_error) {
    if ((ann != NULL) && (data.train_data != NULL)) {
      fann_train_on_data(ann, data.train_data, max_epochs, epochs_between_reports, desired_error);
    }
  }

  /* Method: train_on_file

     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.
  */
  void train_on_file(const std::string &filename, unsigned int max_epochs,
                     unsigned int epochs_between_reports, float desired_error) {
    if (ann != NULL) {
      fann_train_on_file(ann, filename.c_str(), max_epochs, epochs_between_reports, desired_error);
    }
  }

#endif /* NOT FIXEDFANN */

  /* Method: test

     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.
  */
  fann_type *test(fann_type *input, fann_type *desired_output) {
    fann_type *output = NULL;
    if (ann != NULL) {
      output = fann_test(ann, input, desired_output);
    }
    return output;
  }

  /* Method: test_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.
   */
  float test_data(const training_data &data) {
    float mse = 0.0f;
    if ((ann != NULL) && (data.train_data != NULL)) {
      mse = fann_test_data(ann, data.train_data);
    }
    return mse;
  }

  /* Method: 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.
   */
  float get_MSE() {
    float mse = 0.0f;
    if (ann != NULL) {
      mse = fann_get_MSE(ann);
    }
    return mse;
  }

  /* Method: 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
   */
  void reset_MSE() {
    if (ann != NULL) {
      fann_reset_MSE(ann);
    }
  }

#ifndef FIXEDFANN

  /* Method: set_callback

     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.
   */
  void set_callback(callback_type callback, void *user_data) {
    if (ann != NULL) {
      // Allocated data is also deleted in the destroy method called by the destructor
      user_context *user_instance = static_cast<user_context *>(fann_get_user_data(ann));
      if (user_instance != NULL) delete user_instance;

      user_instance = new user_context();
      user_instance->user_callback = callback;
      user_instance->user_data = user_data;
      user_instance->net = this;
      fann_set_user_data(ann, user_instance);

      if (callback != NULL)
        fann_set_callback(ann, &FANN::neural_net::internal_callback);
      else
        fann_set_callback(ann, NULL);
    }
  }

#endif /* NOT FIXEDFANN */

  /* Method: 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.
  */
  void print_parameters() {
    if (ann != NULL) {
      fann_print_parameters(ann);
    }
  }

  /* Method: 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.
   */
  training_algorithm_enum get_training_algorithm() {
    fann_train_enum training_algorithm = FANN_TRAIN_INCREMENTAL;
    if (ann != NULL) {
      training_algorithm = fann_get_training_algorithm(ann);
    }
    return static_cast<training_algorithm_enum>(training_algorithm);
  }

  /* Method: set_training_algorithm

     Set the training algorithm.

     More info available in <get_training_algorithm>

     This function appears in FANN >= 1.0.0.
   */
  void set_training_algorithm(training_algorithm_enum training_algorithm) {
    if (ann != NULL) {
      fann_set_training_algorithm(ann, static_cast<fann_train_enum>(training_algorithm));
    }
  }

  /* Method: 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.
   */
  float get_learning_rate() {
    float learning_rate = 0.0f;
    if (ann != NULL) {
      learning_rate = fann_get_learning_rate(ann);
    }
    return learning_rate;
  }

  /* Method: set_learning_rate

     Set the learning rate.

     More info available in <get_learning_rate>

     This function appears in FANN >= 1.0.0.
   */
  void set_learning_rate(float learning_rate) {
    if (ann != NULL) {
      fann_set_learning_rate(ann, learning_rate);
    }
  }

  /*************************************************************************************************************/

  /* Method: get_activation_function

     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
   */
  activation_function_enum get_activation_function(int layer, int neuron) {
    unsigned int activation_function = 0;
    if (ann != NULL) {
      activation_function = fann_get_activation_function(ann, layer, neuron);
    }
    return static_cast<activation_function_enum>(activation_function);
  }

  /* Method: set_activation_function

     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 unbounded.

     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.
   */
  void set_activation_function(activation_function_enum activation_function, int layer,
                               int neuron) {
    if (ann != NULL) {
      fann_set_activation_function(ann, static_cast<fann_activationfunc_enum>(activation_function),
                                   layer, neuron);
    }
  }

  /* Method: set_activation_function_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.
   */
  void set_activation_function_layer(activation_function_enum activation_function, int layer) {
    if (ann != NULL) {
      fann_set_activation_function_layer(
          ann, static_cast<fann_activationfunc_enum>(activation_function), layer);
    }
  }

  /* Method: set_activation_function_hidden

     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.
   */
  void set_activation_function_hidden(activation_function_enum activation_function) {
    if (ann != NULL) {
      fann_set_activation_function_hidden(
          ann, static_cast<fann_activationfunc_enum>(activation_function));
    }
  }

  /* Method: set_activation_function_output

     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.
   */
  void set_activation_function_output(activation_function_enum activation_function) {
    if (ann != NULL) {
      fann_set_activation_function_output(
          ann, static_cast<fann_activationfunc_enum>(activation_function));
    }
  }

  /* Method: get_activation_steepness

     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 aggressive 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
   */
  fann_type get_activation_steepness(int layer, int neuron) {
    fann_type activation_steepness = 0;
    if (ann != NULL) {
      activation_steepness = fann_get_activation_steepness(ann, layer, neuron);
    }
    return activation_steepness;
  }

  /* Method: set_activation_steepness

     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 aggressive 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.
   */
  void set_activation_steepness(fann_type steepness, int layer, int neuron) {
    if (ann != NULL) {
      fann_set_activation_steepness(ann, steepness, layer, neuron);
    }
  }

  /* Method: set_activation_steepness_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.
   */
  void set_activation_steepness_layer(fann_type steepness, int layer) {
    if (ann != NULL) {
      fann_set_activation_steepness_layer(ann, steepness, layer);
    }
  }

  /* Method: set_activation_steepness_hidden

     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.
   */
  void set_activation_steepness_hidden(fann_type steepness) {
    if (ann != NULL) {
      fann_set_activation_steepness_hidden(ann, steepness);
    }
  }

  /* Method: set_activation_steepness_output

     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.
   */
  void set_activation_steepness_output(fann_type steepness) {
    if (ann != NULL) {
      fann_set_activation_steepness_output(ann, steepness);
    }
  }

  /*************************************************************************************************************/

  /* Method: 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.
    */
  error_function_enum get_train_error_function() {
    fann_errorfunc_enum train_error_function = FANN_ERRORFUNC_LINEAR;
    if (ann != NULL) {
      train_error_function = fann_get_train_error_function(ann);
    }
    return static_cast<error_function_enum>(train_error_function);
  }

  /* Method: set_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.
   */
  void set_train_error_function(error_function_enum train_error_function) {
    if (ann != NULL) {
      fann_set_train_error_function(ann, static_cast<fann_errorfunc_enum>(train_error_function));
    }
  }

  /* Method: 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.
   */
  float get_quickprop_decay() {
    float quickprop_decay = 0.0f;
    if (ann != NULL) {
      quickprop_decay = fann_get_quickprop_decay(ann);
    }
    return quickprop_decay;
  }

  /* Method: set_quickprop_decay

     Sets the quickprop decay factor.

     See also:
          <get_quickprop_decay>, <fann_set_quickprop_decay>

     This function appears in FANN >= 1.2.0.
  */
  void set_quickprop_decay(float quickprop_decay) {
    if (ann != NULL) {
      fann_set_quickprop_decay(ann, quickprop_decay);
    }
  }

  /* Method: 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.
  */
  float get_quickprop_mu() {
    float quickprop_mu = 0.0f;
    if (ann != NULL) {
      quickprop_mu = fann_get_quickprop_mu(ann);
    }
    return quickprop_mu;
  }

  /* Method: set_quickprop_mu

      Sets the quickprop mu factor.

     See also:
          <get_quickprop_mu>, <fann_set_quickprop_mu>

     This function appears in FANN >= 1.2.0.
  */
  void set_quickprop_mu(float quickprop_mu) {
    if (ann != NULL) {
      fann_set_quickprop_mu(ann, quickprop_mu);
    }
  }

  /* Method: 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.
  */
  float get_rprop_increase_factor() {
    float factor = 0.0f;
    if (ann != NULL) {
      factor = fann_get_rprop_increase_factor(ann);
    }
    return factor;
  }

  /* Method: set_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.
  */
  void set_rprop_increase_factor(float rprop_increase_factor) {
    if (ann != NULL) {
      fann_set_rprop_increase_factor(ann, rprop_increase_factor);
    }
  }

  /* Method: 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.
  */
  float get_rprop_decrease_factor() {
    float factor = 0.0f;
    if (ann != NULL) {
      factor = fann_get_rprop_decrease_factor(ann);
    }
    return factor;
  }

  /* Method: set_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.
  */
  void set_rprop_decrease_factor(float rprop_decrease_factor) {
    if (ann != NULL) {
      fann_set_rprop_decrease_factor(ann, rprop_decrease_factor);
    }
  }

  /* Method: 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.
  */
  float get_rprop_delta_zero() {
    float delta = 0.0f;
    if (ann != NULL) {
      delta = fann_get_rprop_delta_zero(ann);
    }
    return delta;
  }

  /* Method: set_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.
  */
  void set_rprop_delta_zero(float rprop_delta_zero) {
    if (ann != NULL) {
      fann_set_rprop_delta_zero(ann, rprop_delta_zero);
    }
  }

  /* Method: 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.
  */
  float get_rprop_delta_min() {
    float delta = 0.0f;
    if (ann != NULL) {
      delta = fann_get_rprop_delta_min(ann);
    }
    return delta;
  }

  /* Method: set_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.
  */
  void set_rprop_delta_min(float rprop_delta_min) {
    if (ann != NULL) {
      fann_set_rprop_delta_min(ann, rprop_delta_min);
    }
  }

  /* Method: 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.
  */
  float get_rprop_delta_max() {
    float delta = 0.0f;
    if (ann != NULL) {
      delta = fann_get_rprop_delta_max(ann);
    }
    return delta;
  }

  /* Method: set_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.
  */
  void set_rprop_delta_max(float rprop_delta_max) {
    if (ann != NULL) {
      fann_set_rprop_delta_max(ann, rprop_delta_max);
    }
  }

  /* Method: 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.
  */
  float get_sarprop_weight_decay_shift() {
    float res = 0.0f;
    if (ann != NULL) {
      res = fann_get_rprop_delta_max(ann);
    }
    return res;
  }

  /* Method: set_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>
  */
  void set_sarprop_weight_decay_shift(float sarprop_weight_decay_shift) {
    if (ann != NULL) {
      fann_set_sarprop_weight_decay_shift(ann, sarprop_weight_decay_shift);
    }
  }

  /* Method: 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.
  */
  float get_sarprop_step_error_threshold_factor() {
    float res = 0.0f;
    if (ann != NULL) {
      res = fann_get_rprop_delta_max(ann);
    }
    return res;
  }

  /* Method: set_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>
  */
  void set_sarprop_step_error_threshold_factor(float sarprop_step_error_threshold_factor) {
    if (ann != NULL) {
      fann_set_sarprop_step_error_threshold_factor(ann, sarprop_step_error_threshold_factor);
    }
  }

  /* Method: 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.
  */
  float get_sarprop_step_error_shift() {
    float res = 0.0f;
    if (ann != NULL) {
      res = fann_get_rprop_delta_max(ann);
    }
    return res;
  }

  /* Method: set_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>
  */
  void set_sarprop_step_error_shift(float sarprop_step_error_shift) {
    if (ann != NULL) {
      fann_set_sarprop_step_error_shift(ann, sarprop_step_error_shift);
    }
  }

  /* Method: 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.
      */
  float get_sarprop_temperature() {
    float res = 0.0f;
    if (ann != NULL) {
      res = fann_get_rprop_delta_max(ann);
    }
    return res;
  }

  /* Method: set_sarprop_temperature

     Set the sarprop_temperature.

          This function appears in FANN >= 2.1.0.

      See also:
          <get_sarprop_temperature>, <fann_set_sarprop_temperature>
  */
  void set_sarprop_temperature(float sarprop_temperature) {
    if (ann != NULL) {
      fann_set_sarprop_temperature(ann, sarprop_temperature);
    }
  }

  /* Method: get_num_input

     Get the number of input neurons.

          This function appears in FANN >= 1.0.0.
  */
  unsigned int get_num_input() {
    unsigned int num_input = 0;
    if (ann != NULL) {
      num_input = fann_get_num_input(ann);
    }
    return num_input;
  }

  /* Method: get_num_output

     Get the number of output neurons.

          This function appears in FANN >= 1.0.0.
  */
  unsigned int get_num_output() {
    unsigned int num_output = 0;
    if (ann != NULL) {
      num_output = fann_get_num_output(ann);
    }
    return num_output;
  }

  /* Method: 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.
  */
  unsigned int get_total_neurons() {
    if (ann == NULL) {
      return 0;
    }
    return fann_get_total_neurons(ann);
  }

  /* Method: get_total_connections

     Get the total number of connections in the entire network.

          This function appears in FANN >= 1.0.0.
  */
  unsigned int get_total_connections() {
    if (ann == NULL) {
      return 0;
    }
    return fann_get_total_connections(ann);
  }

#ifdef FIXEDFANN
  /* Method: 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.
  */
  unsigned int get_decimal_point() {
    if (ann == NULL) {
      return 0;
    }
    return fann_get_decimal_point(ann);
  }

  /* Method: 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.
  */
  unsigned int get_multiplier() {
    if (ann == NULL) {
      return 0;
    }
    return fann_get_multiplier(ann);
  }
#endif /* FIXEDFANN */

  /*********************************************************************/

  /* Method: 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
  */
  network_type_enum get_network_type() {
    fann_nettype_enum network_type = FANN_NETTYPE_LAYER;
    if (ann != NULL) {
      network_type = fann_get_network_type(ann);
    }
    return static_cast<network_type_enum>(network_type);
  }

  /* Method: 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
  */
  float get_connection_rate() {
    if (ann == NULL) {
      return 0;
    }
    return fann_get_connection_rate(ann);
  }

  /* Method: 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
  */
  unsigned int get_num_layers() {
    if (ann == NULL) {
      return 0;
    }
    return fann_get_num_layers(ann);
  }

  /* Method: get_layer_array

      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
  */
  void get_layer_array(unsigned int *layers) {
    if (ann != NULL) {
      fann_get_layer_array(ann, layers);
    }
  }

  /* Method: get_bias_array

      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
  */
  void get_bias_array(unsigned int *bias) {
    if (ann != NULL) {
      fann_get_bias_array(ann, bias);
    }
  }

  /* Method: get_connection_array

      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
  */
  void get_connection_array(connection *connections) {
    if (ann != NULL) {
      fann_get_connection_array(ann, connections);
    }
  }

  /* Method: set_weight_array

      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
  */
  void set_weight_array(connection *connections, unsigned int num_connections) {
    if (ann != NULL) {
      fann_set_weight_array(ann, connections, num_connections);
    }
  }

  /* Method: set_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
  */
  void set_weight(unsigned int from_neuron, unsigned int to_neuron, fann_type weight) {
    if (ann != NULL) {
      fann_set_weight(ann, from_neuron, to_neuron, weight);
    }
  }

  /* Method: get_weights

      Get all the network weights.

      See also:
          <fann_get_weights>

      This function appears in FANN >= x.y.z
  */
  void get_weights(fann_type *weights) {
    if (ann != NULL) {
      fann_get_weights(ann, weights);
    }
  }

  /* Method: set_weights

      Set network weights.

      See also:
          <fann_set_weights>

      This function appears in FANN >= x.y.z
  */
  void set_weights(fann_type *weights) {
    if (ann != NULL) {
      fann_set_weights(ann, weights);
    }
  }

  /*********************************************************************/

  /* Method: 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.
   */
  float get_learning_momentum() {
    float learning_momentum = 0.0f;
    if (ann != NULL) {
      learning_momentum = fann_get_learning_momentum(ann);
    }
    return learning_momentum;
  }

  /* Method: set_learning_momentum

     Set the learning momentum.

     More info available in <get_learning_momentum>

     This function appears in FANN >= 2.0.0.
   */
  void set_learning_momentum(float learning_momentum) {
    if (ann != NULL) {
      fann_set_learning_momentum(ann, learning_momentum);
    }
  }

  /* Method: 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.
   */
  stop_function_enum get_train_stop_function() {
    enum fann_stopfunc_enum stopfunc = FANN_STOPFUNC_MSE;
    if (ann != NULL) {
      stopfunc = fann_get_train_stop_function(ann);
    }
    return static_cast<stop_function_enum>(stopfunc);
  }

  /* Method: set_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.
   */
  void set_train_stop_function(stop_function_enum train_stop_function) {
    if (ann != NULL) {
      fann_set_train_stop_function(ann, static_cast<enum fann_stopfunc_enum>(train_stop_function));
    }
  }

  /* Method: 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.
   */
  fann_type get_bit_fail_limit() {
    fann_type bit_fail_limit = 0.0f;

    if (ann != NULL) {
      bit_fail_limit = fann_get_bit_fail_limit(ann);
    }
    return bit_fail_limit;
  }

  /* Method: set_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.
   */
  void set_bit_fail_limit(fann_type bit_fail_limit) {
    if (ann != NULL) {
      fann_set_bit_fail_limit(ann, bit_fail_limit);
    }
  }

  /* Method: 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
  */
  unsigned int get_bit_fail() {
    unsigned int bit_fail = 0;
    if (ann != NULL) {
      bit_fail = fann_get_bit_fail(ann);
    }
    return bit_fail;
  }

#ifndef FIXEDFANN
  /*********************************************************************/

  /* Method: cascadetrain_on_data

     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.
  */
  void cascadetrain_on_data(const training_data &data, unsigned int max_neurons,
                            unsigned int neurons_between_reports, float desired_error) {
    if ((ann != NULL) && (data.train_data != NULL)) {
      fann_cascadetrain_on_data(ann, data.train_data, max_neurons, neurons_between_reports,
                                desired_error);
    }
  }

  /* Method: cascadetrain_on_file

     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.
  */
  void cascadetrain_on_file(const std::string &filename, unsigned int max_neurons,
                            unsigned int neurons_between_reports, float desired_error) {
    if (ann != NULL) {
      fann_cascadetrain_on_file(ann, filename.c_str(), max_neurons, neurons_between_reports,
                                desired_error);
    }
  }

  /* Method: 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.
   */
  float get_cascade_output_change_fraction() {
    float change_fraction = 0.0f;
    if (ann != NULL) {
      change_fraction = fann_get_cascade_output_change_fraction(ann);
    }
    return change_fraction;
  }

  /* Method: set_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.
   */
  void set_cascade_output_change_fraction(float cascade_output_change_fraction) {
    if (ann != NULL) {
      fann_set_cascade_output_change_fraction(ann, cascade_output_change_fraction);
    }
  }

  /* Method: 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.
   */
  unsigned int get_cascade_output_stagnation_epochs() {
    unsigned int stagnation_epochs = 0;
    if (ann != NULL) {
      stagnation_epochs = fann_get_cascade_output_stagnation_epochs(ann);
    }
    return stagnation_epochs;
  }

  /* Method: set_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.
   */
  void set_cascade_output_stagnation_epochs(unsigned int cascade_output_stagnation_epochs) {
    if (ann != NULL) {
      fann_set_cascade_output_stagnation_epochs(ann, cascade_output_stagnation_epochs);
    }
  }

  /* Method: 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.
   */
  float get_cascade_candidate_change_fraction() {
    float change_fraction = 0.0f;
    if (ann != NULL) {
      change_fraction = fann_get_cascade_candidate_change_fraction(ann);
    }
    return change_fraction;
  }

  /* Method: set_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.
   */
  void set_cascade_candidate_change_fraction(float cascade_candidate_change_fraction) {
    if (ann != NULL) {
      fann_set_cascade_candidate_change_fraction(ann, cascade_candidate_change_fraction);
    }
  }

  /* Method: 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.
   */
  unsigned int get_cascade_candidate_stagnation_epochs() {
    unsigned int stagnation_epochs = 0;
    if (ann != NULL) {
      stagnation_epochs = fann_get_cascade_candidate_stagnation_epochs(ann);
    }
    return stagnation_epochs;
  }

  /* Method: set_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.
   */
  void set_cascade_candidate_stagnation_epochs(unsigned int cascade_candidate_stagnation_epochs) {
    if (ann != NULL) {
      fann_set_cascade_candidate_stagnation_epochs(ann, cascade_candidate_stagnation_epochs);
    }
  }

  /* Method: 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.
   */
  fann_type get_cascade_weight_multiplier() {
    fann_type weight_multiplier = 0;
    if (ann != NULL) {
      weight_multiplier = fann_get_cascade_weight_multiplier(ann);
    }
    return weight_multiplier;
  }

  /* Method: set_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.
   */
  void set_cascade_weight_multiplier(fann_type cascade_weight_multiplier) {
    if (ann != NULL) {
      fann_set_cascade_weight_multiplier(ann, cascade_weight_multiplier);
    }
  }

  /* Method: 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.
   */
  fann_type get_cascade_candidate_limit() {
    fann_type candidate_limit = 0;
    if (ann != NULL) {
      candidate_limit = fann_get_cascade_candidate_limit(ann);
    }
    return candidate_limit;
  }

  /* Method: set_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.
   */
  void set_cascade_candidate_limit(fann_type cascade_candidate_limit) {
    if (ann != NULL) {
      fann_set_cascade_candidate_limit(ann, cascade_candidate_limit);
    }
  }

  /* Method: 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.
   */
  unsigned int get_cascade_max_out_epochs() {
    unsigned int max_out_epochs = 0;
    if (ann != NULL) {
      max_out_epochs = fann_get_cascade_max_out_epochs(ann);
    }
    return max_out_epochs;
  }

  /* Method: set_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.
   */
  void set_cascade_max_out_epochs(unsigned int cascade_max_out_epochs) {
    if (ann != NULL) {
      fann_set_cascade_max_out_epochs(ann, cascade_max_out_epochs);
    }
  }

  /* Method: 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.
   */
  unsigned int get_cascade_max_cand_epochs() {
    unsigned int max_cand_epochs = 0;
    if (ann != NULL) {
      max_cand_epochs = fann_get_cascade_max_cand_epochs(ann);
    }
    return max_cand_epochs;
  }

  /* Method: set_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.
   */
  void set_cascade_max_cand_epochs(unsigned int cascade_max_cand_epochs) {
    if (ann != NULL) {
      fann_set_cascade_max_cand_epochs(ann, cascade_max_cand_epochs);
    }
  }

  /* Method: 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.
   */
  unsigned int get_cascade_num_candidates() {
    unsigned int num_candidates = 0;
    if (ann != NULL) {
      num_candidates = fann_get_cascade_num_candidates(ann);
    }
    return num_candidates;
  }

  /* Method: get_cascade_activation_functions_count

     The number of activation functions in the <get_cascade_activation_functions> array.

     The default number of activation functions is 10.

     See also:
                  <get_cascade_activation_functions>, <set_cascade_activation_functions>,
          <fann_get_cascade_activation_functions_count>

          This function appears in FANN >= 2.0.0.
   */
  unsigned int get_cascade_activation_functions_count() {
    unsigned int activation_functions_count = 0;
    if (ann != NULL) {
      activation_functions_count = fann_get_cascade_activation_functions_count(ann);
    }
    return activation_functions_count;
  }

  /* Method: 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.
   */
  activation_function_enum *get_cascade_activation_functions() {
    enum fann_activationfunc_enum *activation_functions = NULL;
    if (ann != NULL) {
      activation_functions = fann_get_cascade_activation_functions(ann);
    }
    return reinterpret_cast<activation_function_enum *>(activation_functions);
  }

  /* Method: set_cascade_activation_functions

     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.
   */
  void set_cascade_activation_functions(activation_function_enum *cascade_activation_functions,
                                        unsigned int cascade_activation_functions_count) {
    if (ann != NULL) {
      fann_set_cascade_activation_functions(
          ann, reinterpret_cast<enum fann_activationfunc_enum *>(cascade_activation_functions),
          cascade_activation_functions_count);
    }
  }

  /* Method: 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.
   */
  unsigned int get_cascade_activation_steepnesses_count() {
    unsigned int activation_steepness_count = 0;
    if (ann != NULL) {
      activation_steepness_count = fann_get_cascade_activation_steepnesses_count(ann);
    }
    return activation_steepness_count;
  }

  /* Method: 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.
   */
  fann_type *get_cascade_activation_steepnesses() {
    fann_type *activation_steepnesses = NULL;
    if (ann != NULL) {
      activation_steepnesses = fann_get_cascade_activation_steepnesses(ann);
    }
    return activation_steepnesses;
  }

  /* Method: set_cascade_activation_steepnesses

     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.
   */
  void set_cascade_activation_steepnesses(fann_type *cascade_activation_steepnesses,
                                          unsigned int cascade_activation_steepnesses_count) {
    if (ann != NULL) {
      fann_set_cascade_activation_steepnesses(ann, cascade_activation_steepnesses,
                                              cascade_activation_steepnesses_count);
    }
  }

  /* Method: 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.
   */
  unsigned int get_cascade_num_candidate_groups() {
    unsigned int num_candidate_groups = 0;
    if (ann != NULL) {
      num_candidate_groups = fann_get_cascade_num_candidate_groups(ann);
    }
    return num_candidate_groups;
  }

  /* Method: set_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.
   */
  void set_cascade_num_candidate_groups(unsigned int cascade_num_candidate_groups) {
    if (ann != NULL) {
      fann_set_cascade_num_candidate_groups(ann, cascade_num_candidate_groups);
    }
  }

  /*********************************************************************/

  /* Method: scale_train

     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.
   */
  void scale_train(training_data &data) {
    if (ann != NULL) {
      fann_scale_train(ann, data.train_data);
    }
  }

  /* Method: descale_train

     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.
   */
  void descale_train(training_data &data) {
    if (ann != NULL) {
      fann_descale_train(ann, data.train_data);
    }
  }

  /* Method: set_input_scaling_params

     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.
   */
  bool set_input_scaling_params(const training_data &data, float new_input_min,
                                float new_input_max) {
    bool status = false;
    if (ann != NULL) {
      status =
          (fann_set_input_scaling_params(ann, data.train_data, new_input_min, new_input_max) != -1);
    }
    return status;
  }

  /* Method: set_output_scaling_params

     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.
   */
  bool set_output_scaling_params(const training_data &data, float new_output_min,
                                 float new_output_max) {
    bool status = false;
    if (ann != NULL) {
      status = (fann_set_output_scaling_params(ann, data.train_data, new_output_min,
                                               new_output_max) != -1);
    }
    return status;
  }

  /* Method: set_scaling_params

     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.
   */
  bool set_scaling_params(const training_data &data, float new_input_min, float new_input_max,
                          float new_output_min, float new_output_max) {
    bool status = false;
    if (ann != NULL) {
      status = (fann_set_scaling_params(ann, data.train_data, new_input_min, new_input_max,
                                        new_output_min, new_output_max) != -1);
    }
    return status;
  }

  /* Method: clear_scaling_params

     Clears scaling parameters.

     See also:
                  <set_scaling_params>, <fann_clear_scaling_params>

          This function appears in FANN >= 2.1.0.
   */
  bool clear_scaling_params() {
    bool status = false;
    if (ann != NULL) {
      status = (fann_clear_scaling_params(ann) != -1);
    }
    return status;
  }

  /* Method: scale_input

     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.
   */
  void scale_input(fann_type *input_vector) {
    if (ann != NULL) {
      fann_scale_input(ann, input_vector);
    }
  }

  /* Method: scale_output

     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.
   */
  void scale_output(fann_type *output_vector) {
    if (ann != NULL) {
      fann_scale_output(ann, output_vector);
    }
  }

  /* Method: descale_input

     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.
   */
  void descale_input(fann_type *input_vector) {
    if (ann != NULL) {
      fann_descale_input(ann, input_vector);
    }
  }

  /* Method: descale_output

     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.
   */
  void descale_output(fann_type *output_vector) {
    if (ann != NULL) {
      fann_descale_output(ann, output_vector);
    }
  }

#endif /* FIXEDFANN */

  /*********************************************************************/

  /* Method: set_error_log

     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.
   */
  void set_error_log(FILE *log_file) {
    fann_set_error_log(reinterpret_cast<struct fann_error *>(ann), log_file);
  }

  /* Method: 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.
   */
  unsigned int get_errno() { return fann_get_errno(reinterpret_cast<struct fann_error *>(ann)); }

  /* Method: reset_errno

     Resets the last error number.

     This function appears in FANN >= 1.1.0.
   */
  void reset_errno() { fann_reset_errno(reinterpret_cast<struct fann_error *>(ann)); }

  /* Method: reset_errstr

     Resets the last error string.

     This function appears in FANN >= 1.1.0.
   */
  void reset_errstr() { fann_reset_errstr(reinterpret_cast<struct fann_error *>(ann)); }

  /* Method: get_errstr

     Returns the last errstr.

     This function calls <fann_reset_errno> and <fann_reset_errstr>

     This function appears in FANN >= 1.1.0.
   */
  std::string get_errstr() {
    return std::string(fann_get_errstr(reinterpret_cast<struct fann_error *>(ann)));
  }

  /* Method: print_error

     Prints the last error to stderr.

     This function appears in FANN >= 1.1.0.
   */
  void print_error() { fann_print_error(reinterpret_cast<struct fann_error *>(ann)); }

  /* Function: disable_seed_rand

     Disables the automatic random generator seeding that happens in FANN.

     Per default FANN will always seed the random generator when creating a new network,
     unless FANN_NO_SEED is defined during compilation of the library. This method can
     disable this at runtime.

     This function appears in FANN >= 2.3.0
  */
  void disable_seed_rand() { fann_disable_seed_rand(); }

  /* Function: enable_seed_rand

     Enables the automatic random generator seeding that happens in FANN.

     Per default FANN will always seed the random generator when creating a new network,
     unless FANN_NO_SEED is defined during compilation of the library. This method can
     disable this at runtime.

     This function appears in FANN >= 2.3.0
  */
  void enable_seed_rand() { fann_enable_seed_rand(); }

  /*********************************************************************/

 private:
  // Structure used by set_callback to hold information about a user callback
  typedef struct user_context_type {
    callback_type user_callback;  // Pointer to user callback function
    void *user_data;              // Arbitrary data pointer passed to the callback
    neural_net *net;              // This pointer for the neural network
  } user_context;

#ifndef FIXEDFANN

  // Internal callback used to convert from pointers to class references
  static int FANN_API internal_callback(struct fann *ann, struct fann_train_data *train,
                                        unsigned int max_epochs,
                                        unsigned int epochs_between_reports, float desired_error,
                                        unsigned int epochs) {
    user_context *user_data = static_cast<user_context *>(fann_get_user_data(ann));
    if (user_data != NULL) {
      FANN::training_data data;
      data.train_data = train;

      int result =
          (*user_data->user_callback)(*user_data->net, data, max_epochs, epochs_between_reports,
                                      desired_error, epochs, user_data->user_data);

      data.train_data = NULL;  // Prevent automatic cleanup
      return result;
    } else {
      return -1;  // This should not occur except if out of memory
    }
  }

#endif /* NOT FIXEDFANN */

 protected:
  // Pointer the encapsulated fann neural net structure
  struct fann *ann;
};

/*************************************************************************/
}  // namespace FANN

#endif /* FANN_CPP_H_INCLUDED */
