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();
}
/**
* Evaluate the network and display (to the console) the output for every value in the training
* set. Displays ideal and actual.
*
* @param network The network to evaluate.
* @param training The training set to evaluate.
*/
public static void evaluate(final MLRegression network, final MLDataSet training) {
for (final MLDataPair pair : training) {
final MLData output = network.compute(pair.getInput());
System.out.println(
"Input="
+ EncogUtility.formatNeuralData(pair.getInput())
+ ", Actual="
+ EncogUtility.formatNeuralData(output)
+ ", Ideal="
+ EncogUtility.formatNeuralData(pair.getIdeal()));
}
}