public static double evaluate(ErrorCalculationMode m, double[] actual, double[] ideal) {
ErrorCalculation.setMode(m);
ErrorCalculation error = new ErrorCalculation();
for (int i = 0; i < actual.length; i++) {
error.updateError(actual[i], ideal[i]);
}
return error.calculate();
}
public static double calculateRegressionError(MLRegression method, MLDataSet data) {
final ErrorCalculation errorCalculation = new ErrorCalculation();
if (method instanceof MLContext) ((MLContext) method).clearContext();
for (final MLDataPair pair : data) {
final MLData actual = method.compute(pair.getInput());
errorCalculation.updateError(
actual.getData(), pair.getIdeal().getData(), pair.getSignificance());
}
return errorCalculation.calculate();
}
/**
* 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();
}