/* Method: ScaleInputTrainData
*
* Scales the inputs in the training data to the specified range.
*
* A simplified scaling method, which is mostly useful in examples where it's known that all the
* data will be in one range and it should be transformed to another range.
*
* It is not recommended to use this on subsets of data as the complete input range might not be
* available in that subset.
*
* For more powerful scaling, please consider <NeuralNet::ScaleTrain>
*
* See also:
* <ScaleOutputTrainData>, <ScaleTrainData>, <fann_scale_input_train_data at http://libfann.github.io/fann/docs/files/fann_train-h.html#fann_scale_input_train_data>
*
* This function appears in FANN >= 2.0.0.
*/
public void ScaleInputTrainData(double new_min, double new_max)
{
InternalData.scale_input_train_data(new_min, new_max);
}
/* Method: ScaleInputTrainData
*
* Scales the inputs in the training data to the specified range.
*
* A simplified scaling method, which is mostly useful in examples where it's known that all the
* data will be in one range and it should be transformed to another range.
*
* It is not recommended to use this on subsets of data as the complete input range might not be
* available in that subset.
*
* For more powerful scaling, please consider <NeuralNet::ScaleTrain>
*
* See also:
* <ScaleOutputTrainData>, <ScaleTrainData>, <fann_scale_input_train_data at http://libfann.github.io/fann/docs/files/fann_train-h.html#fann_scale_input_train_data>
*
* This function appears in FANN >= 2.0.0.
*/
public void ScaleInputTrainData(float new_min, float new_max)
{
InternalData.scale_input_train_data(new_min, new_max);
}