public training_data(training_data data) : this(fannfloatPINVOKE.new_training_data__SWIG_1(training_data.getCPtr(data)), true)
{
if (fannfloatPINVOKE.SWIGPendingException.Pending)
{
throw fannfloatPINVOKE.SWIGPendingException.Retrieve();
}
}
/*********************************************************************/
/* Method: CreateTrainFromCallback
* Creates the training data from a user supplied function.
* As the training data are numerable (data 1, data 2...), the user must write
* a function that receives the number of the training data set (input,output)
* and returns the set.
*
* Parameters:
* dataCount - The number of training data
* inputCount - The number of inputs per training data
* outputCount - The number of ouputs per training data
* callback - The user suplied delegate
*
* Parameters for the user delegate:
* number - The number of the training data set
* inputCount - The number of inputs per training data
* outputCount - The number of ouputs per training data
* input - The set of inputs
* output - The set of desired outputs
*
* See also:
* <ReadTrainFromFile>, <NeuralNet::TrainOnData>,
* <fann_create_train_from_callback at http://libfann.github.io/fann/docs/files/fann_train-h.html#fann_create_train_from_callback>
*
* This function appears in FANN >= 2.1.0
*/
public void CreateTrainFromCallback(uint dataCount, uint inputCount, uint outputCount, DataCreateCallback callback)
{
InternalData = new FannWrapperFloat.training_data();
Callback = callback;
RawCallback = new data_create_callback(InternalCallback);
fannfloatPINVOKE.training_data_create_train_from_callback(training_data.getCPtr(this.InternalData), dataCount, inputCount, outputCount, Marshal.GetFunctionPointerForDelegate(RawCallback));
}
public void merge_train_data(training_data data)
{
fannfloatPINVOKE.training_data_merge_train_data(swigCPtr, training_data.getCPtr(data));
if (fannfloatPINVOKE.SWIGPendingException.Pending)
{
throw fannfloatPINVOKE.SWIGPendingException.Retrieve();
}
}
/* Constructor: TrainingData
Reads a file that stores training data.
See also:
<ReadTrainFromFile>, <SetTrainData> or <CreateTrainFromCallback>
*/
public TrainingData(string filename)
{
InternalData = new FannWrapperFloat.training_data();
if (!ReadTrainFromFile(filename))
{
throw new ArgumentException("Cannot read data from \"{0}\"", filename);
}
}
/* Constructor: TrainingData
* Reads a file that stores training data.
*
* See also:
* <ReadTrainFromFile>, <SetTrainData> or <CreateTrainFromCallback>
*/
public TrainingData(string filename)
{
InternalData = new FannWrapperFloat.training_data();
if (!ReadTrainFromFile(filename))
{
throw new ArgumentException("Cannot read data from \"{0}\"", filename);
}
}
public void descale_train(training_data data)
{
fannfloatPINVOKE.neural_net_descale_train(swigCPtr, training_data.getCPtr(data));
if (fannfloatPINVOKE.SWIGPendingException.Pending)
{
throw fannfloatPINVOKE.SWIGPendingException.Retrieve();
}
}
public void cascadetrain_on_data(training_data data, uint max_neurons, uint neurons_between_reports, float desired_error)
{
fannfloatPINVOKE.neural_net_cascadetrain_on_data(swigCPtr, training_data.getCPtr(data), max_neurons, neurons_between_reports, desired_error);
if (fannfloatPINVOKE.SWIGPendingException.Pending)
{
throw fannfloatPINVOKE.SWIGPendingException.Retrieve();
}
}
public bool set_scaling_params(training_data data, float new_input_min, float new_input_max, float new_output_min, float new_output_max)
{
bool ret = fannfloatPINVOKE.neural_net_set_scaling_params(swigCPtr, training_data.getCPtr(data), new_input_min, new_input_max, new_output_min, new_output_max);
if (fannfloatPINVOKE.SWIGPendingException.Pending)
{
throw fannfloatPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
public float test_data(training_data data)
{
float ret = fannfloatPINVOKE.neural_net_test_data(swigCPtr, training_data.getCPtr(data));
if (fannfloatPINVOKE.SWIGPendingException.Pending)
{
throw fannfloatPINVOKE.SWIGPendingException.Retrieve();
}
return(ret);
}
public training_data(training_data data)
: this(fannfloatPINVOKE.new_training_data__SWIG_1(training_data.getCPtr(data)), true)
{
if (fannfloatPINVOKE.SWIGPendingException.Pending) throw fannfloatPINVOKE.SWIGPendingException.Retrieve();
}
internal static global::System.Runtime.InteropServices.HandleRef getCPtr(training_data obj)
{
return (obj == null) ? new global::System.Runtime.InteropServices.HandleRef(null, global::System.IntPtr.Zero) : obj.swigCPtr;
}
public void merge_train_data(training_data data)
{
fannfloatPINVOKE.training_data_merge_train_data(swigCPtr, training_data.getCPtr(data));
if (fannfloatPINVOKE.SWIGPendingException.Pending) throw fannfloatPINVOKE.SWIGPendingException.Retrieve();
}
internal TrainingData(training_data other)
{
InternalData = other;
}
/*********************************************************************/
/* Method: CreateTrainFromCallback
Creates the training data from a user supplied function.
As the training data are numerable (data 1, data 2...), the user must write
a function that receives the number of the training data set (input,output)
and returns the set.
Parameters:
dataCount - The number of training data
inputCount - The number of inputs per training data
outputCount - The number of ouputs per training data
callback - The user suplied delegate
Parameters for the user delegate:
number - The number of the training data set
inputCount - The number of inputs per training data
outputCount - The number of ouputs per training data
input - The set of inputs
output - The set of desired outputs
See also:
<ReadTrainFromFile>, <NeuralNet::TrainOnData>,
<fann_create_train_from_callback at http://libfann.github.io/fann/docs/files/fann_train-h.html#fann_create_train_from_callback>
This function appears in FANN >= 2.1.0
*/
public void CreateTrainFromCallback(uint dataCount, uint inputCount, uint outputCount, DataCreateCallback callback)
{
InternalData = new FannWrapperFloat.training_data();
Callback = callback;
RawCallback = new data_create_callback(InternalCallback);
fannfloatPINVOKE.training_data_create_train_from_callback(training_data.getCPtr(this.InternalData), dataCount, inputCount, outputCount, Marshal.GetFunctionPointerForDelegate(RawCallback));
}
/* Constructor: TrainingData
Copy constructor constructs a copy of the training data.
Corresponds to the C API <fann_duplicate_train_data at http://libfann.github.io/fann/docs/files/fann_train-h.html#fann_duplicate_train_data> function.
*/
public TrainingData(TrainingData data)
{
InternalData = new FannWrapperFloat.training_data(data.InternalData);
}
/* Constructor: TrainingData
Default constructor creates an empty training data.
Use <ReadTrainFromFile>, <SetTrainData> or <CreateTrainFromCallback> to initialize.
*/
public TrainingData()
{
InternalData = new FannWrapperFloat.training_data();
}
internal static global::System.Runtime.InteropServices.HandleRef getCPtr(training_data obj)
{
return((obj == null) ? new global::System.Runtime.InteropServices.HandleRef(null, global::System.IntPtr.Zero) : obj.swigCPtr);
}
/* Constructor: TrainingData
*
* Copy constructor constructs a copy of the training data.
* Corresponds to the C API <fann_duplicate_train_data at http://libfann.github.io/fann/docs/files/fann_train-h.html#fann_duplicate_train_data> function.
*/
public TrainingData(TrainingData data)
{
InternalData = new FannWrapperFloat.training_data(data.InternalData);
}
/* Constructor: TrainingData
*
* Default constructor creates an empty training data.
* Use <ReadTrainFromFile>, <SetTrainData> or <CreateTrainFromCallback> to initialize.
*/
public TrainingData()
{
InternalData = new FannWrapperFloat.training_data();
}
