Ejemplo n.º 1
0
 /* 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);
 }
Ejemplo n.º 2
0
 /* 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);
 }