public bool Equals(SlashConfig other)
{
if (other == null)
{
return(false);
}
return(Data.Equals(other.Data) &&
Output.Equals(other.Output) &&
Index.Equals(other.Index) &&
HilbertClassifier.Equals(other.HilbertClassifier) &&
DensityClassifier.Equals(other.DensityClassifier) &&
Math.Abs(AcceptableBCubed - other.AcceptableBCubed) <= 0.0001
);
}
/// <summary>
/// Recluster the data, starting with an initial classification and further combining points into fewer clusters.
/// </summary>
void Recluster()
{
LoadData();
var classifier = new HilbertClassifier(InitialClassification, Configuration.Index.BitsPerDimension)
{
IndexConfig = Configuration.Index.Budget,
MaxNeighborsToCompare = Configuration.HilbertClassifier.MaxNeighborsToCompare,
OutlierDistanceMultiplier = Configuration.HilbertClassifier.OutlierDistanceMultiplier,
OutlierSize = Configuration.Index.Budget.OutlierSize,
UseExactClusterDistance = Configuration.HilbertClassifier.UseExactClusterDistance
};
//TODO: Follow this with the DensityClassifier.
Timer.Start("Classify by distance");
FinalClassification = classifier.Classify();
Timer.Stop("Classify by distance");
MergeSquareDistance = classifier.MergeSquareDistance;
ReclassifyByDensity();
Logger.Info(Summary());
SaveData();
}
public override int GetHashCode()
{
return(AcceptableBCubed.GetHashCode() + Data.GetHashCode() + DensityClassifier.GetHashCode()
+ HilbertClassifier.GetHashCode() + Index.GetHashCode() + Output.GetHashCode());
}
