/* Method: MergeTrainData
Merges the data into the data contained in the <TrainingData>.
This function appears in FANN >= 1.1.0.
*/
public void MergeTrainData(TrainingData data)
{
InternalData.merge_train_data(data.InternalData);
}
/* 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 FannWrapperFixed.training_data(data.InternalData);
}
/* 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 FannWrapperFixed.training_data(data.InternalData);
}
/* Method: MergeTrainData
*
* Merges the data into the data contained in the <TrainingData>.
*
* This function appears in FANN >= 1.1.0.
*/
public void MergeTrainData(TrainingData data)
{
InternalData.merge_train_data(data.InternalData);
}
/* Method: TestData
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>, <MSE>, <BitFail>, <fann_test_data at http://libfann.github.io/fann/docs/files/fann_train-h.html#fann_test_data>
This function appears in FANN >= 1.2.0.
*/
public float TestData(TrainingData data)
{
return net.test_data(data.InternalData);
}
/* Method: InitWeights
Initialize the weights using Widrow + Nguyen's algorithm.
This function behaves similarly to <fann_randomize_weights at http://libfann.github.io/fann/docs/files/fann-h.html#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:
<RandomizeWeights>, <TrainingData::ReadTrainFromFile>,
<fann_init_weights at http://libfann.github.io/fann/docs/files/fann-h.html#fann_init_weights>
This function appears in FANN >= 1.1.0.
*/
public void InitWeights(TrainingData data)
{
net.init_weights(data.InternalData);
}
