/**
* Calculate the error for this neural network. The error is calculated using
* root-mean-square(RMS).
*
* @param data The training set.
* @return The error percentage.
*/
public double calculateError(final MLDataSet data) {
final ErrorCalculation errorCalculation = new ErrorCalculation();
final double[] actual = new double[this.outputCount];
final MLDataPair pair = BasicMLDataPair.createPair(data.getInputSize(), data.getIdealSize());
for (int i = 0; i < data.getRecordCount(); i++) {
data.getRecord(i, pair);
compute(pair.getInputArray(), actual);
errorCalculation.updateError(actual, pair.getIdealArray(), pair.getSignificance());
}
return errorCalculation.calculate();
}
public static ObjectPair<double[][], double[][]> trainingToArray(MLDataSet training) {
int length = (int) training.getRecordCount();
double[][] a = new double[length][training.getInputSize()];
double[][] b = new double[length][training.getIdealSize()];
int index = 0;
for (MLDataPair pair : training) {
EngineArray.arrayCopy(pair.getInputArray(), a[index]);
EngineArray.arrayCopy(pair.getIdealArray(), b[index]);
index++;
}
return new ObjectPair<double[][], double[][]>(a, b);
}