/**
* Calculate the error for the given method and dataset.
*
* @param method The method to use.
* @param data The data to use.
* @return The error.
*/
public double calculateError(MLMethod method, MLDataSet data) {
if (this.dataset.getNormHelper().getOutputColumns().size() == 1) {
ColumnDefinition cd = this.dataset.getNormHelper().getOutputColumns().get(0);
if (cd.getDataType() == ColumnType.nominal) {
return EncogUtility.calculateClassificationError((MLClassification) method, data);
}
}
return EncogUtility.calculateRegressionError((MLRegression) method, data);
}
/**
* Calculate the error for this neural network.
*
* @param data The training set.
* @return The error percentage.
*/
@Override
public double calculateError(final MLDataSet data) {
return EncogUtility.calculateRegressionError(this, data);
}