/**
* 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();
}
private MLDataSet[] splitDataSet(MLDataSet data) {
MLDataSet[] subsets = new MLDataSet[numSplit];
for (int i = 0; i < subsets.length; i++) {
subsets[i] = new BasicMLDataSet();
}
for (int i = 0; i < data.getRecordCount(); i++) {
MLDataPair pair = BasicMLDataPair.createPair(INPUT_COUNT, OUTPUT_COUNT);
data.getRecord(i, pair);
subsets[i % numSplit].add(pair);
}
return subsets;
}
