public SelfEndpointingSingleGestureRecognizer
(string label, Classifier classifier, ILoggingProvider <T> Provider)
: base(label, classifier.Dataset as DataSet <T>, Provider, false)
{
Classifier = classifier;
SmoothedData = new SmoothedList <T>(3); // Half-life of the "weight" of a data point: three data points later (pretty fast - this is a light smoothing.)
PeakFinder = new PeakFinder <T>(SmoothedData,
(seq) =>
{
var Seq = new Sequence <T>()
{
SourcePath = seq.ToArray()
};
var analyzedSeq = Current.Analyze(Seq).Result;
// If we've stated that we only care about one gesture class, and it's not that, then we're obviously not there yet.
//if (Target != null && Seq.RecognizedAsIndex != Target.index) return double.NaN;
if (Target != null && Seq.RecognizedAsName != Target.className)
{
return(double.NaN);
}
else
{
return(analyzedSeq.RecognitionScore);
}
}, thresholdScore: 1.0, minLength: Classifier.Dataset?.MinSequenceLength ?? 5);
InterimInterval = TimeSpan.FromMilliseconds(100);
}
public override double AssessShot(SmoothedList <float> distancesDodged, List <TimeSpan> timestamps, IncomingRangedAttack incoming)
{
var effectiveDodge = distancesDodged.Last() / DistanceForOneSigma / (1.0 + 0.01 * incoming.DodgeCompensationBonus);
var bellCurveDieRoll = Accord.Statistics.Distributions.Univariate.NormalDistribution.Random();
var resultScore = incoming.BaseZScore - effectiveDodge - bellCurveDieRoll;
Log.Debug("Evasion|AssessShot", $"Resolved an evasion attempt with a {((resultScore > 0) ? "hit" : "miss")} ({resultScore:f2}) based on an EffectiveDodge of {effectiveDodge:f2} and a random dodge of {bellCurveDieRoll:f2}.");
return(resultScore);
}
public override double AssessShot(SmoothedList <float> relevantFactors, List <TimeSpan> timestamps, IncomingRangedAttack incoming)
{
throw new NotImplementedException();
}
