private static BiometricResult Evaluate(ClassifierInstance classifier, List <ISample> testingSamples, double minInterval, double interval, BiometricResult result)
{
if (interval < minInterval)
{
// the threshold found for the smallest interval represents the best guess at the EER
return(result);
}
else
{
// recurse until the smallest allowable interval is hit
ErrorRatePair delta1 = CalculateErrorRate(classifier, testingSamples, (result.GetThreshold() - (interval / 2.0)));
ErrorRatePair delta2 = CalculateErrorRate(classifier, testingSamples, (result.GetThreshold() + (interval / 2.0)));
Console.WriteLine("delta1 : " + delta1.GetFAR() + " " + delta1.GetFRR() + " \n " + delta2.GetFAR() + " " + delta2.GetFRR());
Console.WriteLine("threshold: " + result.GetThreshold());
Console.WriteLine("errorDelta1: " + delta1.GetErrorDelta() + " errorDelta2: " + delta2.GetErrorDelta());
if (delta1.GetErrorDelta() < delta2.GetErrorDelta())
{
Console.WriteLine("Choose delta1");
return(Evaluate(classifier, testingSamples, minInterval, (interval / 2.0), new BiometricResult((result.GetThreshold() - (interval / 2.0)), new List <ErrorRatePair> {
delta1
})));
}
else
{
Console.WriteLine("Choose delta2");
return(Evaluate(classifier, testingSamples, minInterval, (interval / 2.0), new BiometricResult((result.GetThreshold() + (interval / 2.0)), new List <ErrorRatePair> {
delta2
})));
}
}
}
private static Tuple <ErrorRatePair, List <ErrorRatePair> > CalculateErrorRate <T>(SampleSet <T> sampleSet, ClassifierFactory <T> classifier, int trainingSize, double threshold, int xValidationStart, int xValidationLength) where T : ISample
{
// test classifier instances with testing samples and threshold asynchronously to get cross validated result
List <ErrorRatePair> errorRates = Enumerable.Range(xValidationStart, xValidationLength).AsParallel()
.Select(x => CalculateErrorRate(classifier.GetInstance(x), SampleSetHelpers.GetSampleSetTestingSamples(sampleSet, trainingSize, x), threshold))
.ToList();
ErrorRatePair totalError = errorRates[0];
for (int i = 1; i < errorRates.Count; i++)
{
totalError = totalError + errorRates[i];
}
return(new Tuple <ErrorRatePair, List <ErrorRatePair> >(totalError, errorRates));
}