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added c++ constructors for all create methods of FANN, to be used ins…
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…tead of create_... methodes - also created some more tests
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@steffennissen
steffennissen committed Nov 8, 2015
1 parent 95285d5 commit 4d8b0b8
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208 changes: 181 additions & 27 deletions src/include/fann_cpp.h
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Original file line number Diff line number Diff line change
Expand Up @@ -77,43 +77,176 @@ namespace FANN {
*/
class neural_net {
public:
/* Constructor: neural_net
Default constructor creates an empty neural net.
Use one of the create functions to create the neural network.
/* Constructor: neural_net(network_type_enum net_type, unsigned int num_layers, const unsigned int *layers)
See also:
<create_standard>, <create_sparse>, <create_shortcut>,
<create_standard_array>, <create_sparse_array>, <create_shortcut_array>
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, 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() : ann(NULL) {
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

Creates a copy the other neural_net.
/* Constructor: neural_net(float connection_rate, unsigned int num_layers, ...)
See also:
<copy_from_struct_fann>
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
/* Constructor: neural_net(struct fann *other)
Creates a copy the other neural_net.
See also:
<copy_from_struct_fann>
*/
neural_net(struct fann *other) {
copy_from_struct_fann(other);
}

/* Method: copy_from_struct_fann
/* 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();
Expand Down Expand Up @@ -155,7 +288,7 @@ namespace FANN {
}
}

/* Method: create_standard
/* Method: create_standard - DEPRECATED
Creates a standard fully connected backpropagation neural network.
Expand All @@ -180,6 +313,9 @@ namespace FANN {
>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>
Expand All @@ -200,11 +336,14 @@ namespace FANN {
return status;
}

/* Method: create_standard_array
/* 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>
Expand All @@ -217,7 +356,7 @@ namespace FANN {
return (ann != NULL);
}

/* Method: create_sparse
/* Method: create_sparse - DEPRECATED
Creates a standard backpropagation neural network, which is not fully connected.
Expand All @@ -233,6 +372,9 @@ namespace FANN {
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>
Expand All @@ -254,13 +396,16 @@ namespace FANN {
return status;
}

/* Method: create_sparse_array
/* 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.
See also:
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>
Expand All @@ -273,7 +418,7 @@ namespace FANN {
return (ann != NULL);
}

/* Method: create_shortcut
/* Method: create_shortcut - DEPRECATED
Creates a standard backpropagation neural network, which is fully connected and which
also has shortcut connections.
Expand All @@ -284,6 +429,9 @@ namespace FANN {
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>
Expand All @@ -304,14 +452,17 @@ namespace FANN {
return status;
}

/* Method: create_shortcut_array
/* 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.
See also:
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>
Expand Down Expand Up @@ -415,11 +566,14 @@ namespace FANN {
}
}

/* Method: create_from_file
/* 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>
Expand Down
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