/// <summary>
/// Run the example.
/// </summary>
public void Process()
{
IErrorCalculation calcEss = new ErrorCalculationSSE();
IErrorCalculation calcMse = new ErrorCalculationMSE();
IErrorCalculation calcRms = new ErrorCalculationRMS();
DataHolder smallErrors = Generate(Seed, Rows, Cols, Low, High, 0.1);
DataHolder mediumErrors = Generate(Seed, Rows, Cols, Low, High, 0.5);
DataHolder largeErrors = Generate(Seed, Rows, Cols, Low, High, 1.0);
DataHolder hugeErrors = Generate(Seed, Rows, Cols, Low, High, 10.0);
double smallEss = smallErrors.CalculateError(calcEss);
double smallMse = smallErrors.CalculateError(calcMse);
double smallRms = smallErrors.CalculateError(calcRms);
double mediumEss = mediumErrors.CalculateError(calcEss);
double mediumMse = mediumErrors.CalculateError(calcMse);
double mediumRms = mediumErrors.CalculateError(calcRms);
double largeEss = largeErrors.CalculateError(calcEss);
double largeMse = largeErrors.CalculateError(calcMse);
double largeRms = largeErrors.CalculateError(calcRms);
double hugeEss = hugeErrors.CalculateError(calcEss);
double hugeMse = hugeErrors.CalculateError(calcMse);
double hugeRms = hugeErrors.CalculateError(calcRms);
Console.WriteLine("Type\tESS\tMSE\tRMS");
Console.WriteLine("Small\t" + (int)smallEss + "\t" + smallMse.ToString("0.0000") + "\t" + smallRms.ToString("0.0000"));
Console.WriteLine("Medium\t" + (int)mediumEss + "\t" + mediumMse.ToString("0.0000") + "\t" + mediumRms.ToString("0.0000"));
Console.WriteLine("Large\t" + (int)largeEss + "\t" + largeMse.ToString("0.0000") + "\t" + largeRms.ToString("0.0000"));
Console.WriteLine("Huge\t" + (int)hugeEss + "\t" + hugeMse.ToString("0.0000") + "\t" + hugeRms.ToString("0.0000"));
}
public void PerformTraining(ILearningMethod method, object model, IEnumerable validationData)
{
IRegressionAlgorithm regression = model as IRegressionAlgorithm;
if (regression != null)
{
IErrorCalculation errorCalc;
switch (ErrorCalculation)
{
case ErrorCaclulationType.MeanSquare:
errorCalc = new ErrorCalculationMSE();
break;
case ErrorCaclulationType.RootMeanSquare:
errorCalc = new ErrorCalculationRMS();
break;
case ErrorCaclulationType.SumOfSquares:
errorCalc = new ErrorCalculationSSE();
break;
default:
throw new ArgumentOutOfRangeException();
}
new SimpleLearn().PerformIterationsEarlyStop(method, regression, (IList <BasicData>)validationData,
Tolerate, errorCalc);
}
else
{
IClassificationAlgorithm classification = (IClassificationAlgorithm)model;
new SimpleLearn().PerformIterationsClassifyEarlyStop(method, classification,
(IList <BasicData>)validationData, Tolerate);
}
}
public void TestErrorCalc()
{
IErrorCalculation calc = new ErrorCalculationRMS();
double result = ErrorTestingUtil.CalculateError(
calc,
ErrorTestingUtil.Actual,
ErrorTestingUtil.Ideal);
Assert.AreEqual(12.3134, result, 0.001);
}
