public void DeNormalizeDataSetOutputs(DataSet[] dataset)
{
int outputsCount = dataset[0].Outputs.Count;
foreach (var item in dataset)
{
for (int i = 0; i < outputsCount; i++)
{
item.Outputs[i] = DataProcessing.Denormalize(item.Outputs[i], maxOutputs[i], minOutputs[i]);
}
}
}
public override String ProcessTestSetResults(Matrix <double> testSetInput, Matrix <double> testSetOutput, Matrix <double> testSetPredictedOutcome)
{
double accError = 0;
int failures = 0;
String txt = "Testing Set Results\r\n\r\n";
for (int i = 0; i < testSetPredictedOutcome.RowCount; i++)
{
var realResult = testSetOutput[i, 0];
var estimatedResult = testSetPredictedOutcome[i, 0];
if (UseNormalizedOutput)
{
estimatedResult = DataProcessing.Denormalize(estimatedResult, maxOutputs[0], minOutputs[0]);
realResult = DataProcessing.Denormalize(realResult, maxOutputs[0], minOutputs[0]);
}
double error = realResult - estimatedResult;
string result = String.Empty;
if (Math.Abs(error) > defaultErrorThreshold)
{
failures++;
result = "[ERROR]";
}
if (testSetInput != null)
{
result += "Input[X: " + testSetInput[i, 0] + ", Y:" + testSetInput[i, 1] + testSetInput[i, 2] + "] > ";
}
result += "Real Output:" + realResult + " Estimation: " + estimatedResult + "[Error:" + Math.Round(error, 5) + "]\r\n";
txt += result;
accError += error;
}
txt += "\r\n\r\nAverage Estimation Error: " + (accError / testSetPredictedOutcome.RowCount).ToString();
txt += "\r\nTotal Errors: " + failures + " out of " + testSetPredictedOutcome.RowCount + " samples [" + Math.Round((double)(100 * failures / testSetPredictedOutcome.RowCount), 2) + "%]";
return(txt);
}
