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
);
}
public override int GetHashCode()
{
return(AcceptableBCubed.GetHashCode() + Data.GetHashCode() + DensityClassifier.GetHashCode()
+ HilbertClassifier.GetHashCode() + Index.GetHashCode() + Output.GetHashCode());
}
/// <summary>
/// Apply Density-based reclassification to the FinalClassification.
/// This may cause some clusters to be split into smaller clusters.
/// It will not cause any existing clusters to be merged.
/// </summary>
void ReclassifyByDensity()
{
// 0. Decide if we will be doing this or not, based on the configuration.
if (!Configuration.DensityClassifier.SkipDensityClassification)
{
Timer.Start("Reclassify by density");
var numberOfClustersSplit = 0;
// 1. Loop through all clusters in FinalClassification
// We will be modifying FinalClassification while iterating over it,
// so we need to copy the list of labels up front.
var classLabels = FinalClassification.ClassLabels().ToList();
foreach (var clusterId in classLabels)
{
// 2. Decide if the cluster needs reclustering.
if (NeedsReclustering(clusterId))
{
// 3. Obtain the members of the cluster and index them by the Hilbert curve
var pointsToClassify = FinalClassification.PointsInClass(clusterId);
var lookupPointById = new Dictionary <int, UnsignedPoint>();
foreach (var p in pointsToClassify)
{
lookupPointById[p.UniqueId] = p;
}
int labelCounter = 1;
var subClassification = new Classification <UnsignedPoint, string>(pointsToClassify, p => (labelCounter++).ToString());
var hIndex = new HilbertIndex(subClassification, Configuration.Index.BitsPerDimension);
// 4. Create a DensityClassifier, properly configured.
var unmergeableSize = (int)(pointsToClassify.Count * Configuration.DensityClassifier.UnmergeableSizeFraction);
var densityClassifier = new DensityClassifier(hIndex, MergeSquareDistance, unmergeableSize)
{
NeighborhoodRadiusMultiplier = Configuration.DensityClassifier.NeighborhoodRadiusMultiplier,
OutlierSize = Configuration.DensityClassifier.OutlierSize,
MergeableShrinkage = Configuration.DensityClassifier.MergeableShrinkage
};
// 5. Reclassify.
// This classification is in terms of HilbertPoints, so afterwards we will need to map them to
// their non-HilbertPoint, original UnsignedPoints.
var densityClassification = densityClassifier.Classify();
// 6. If the number of clusters made from the points is more than one...
if (densityClassification.NumPartitions > 1)
{
numberOfClustersSplit++;
// 7. ... loop through all HilbertPoints from cluster and find corresponding UnsignedPoints.
foreach (var hPoint in densityClassification.Points())
{
var uPoint = lookupPointById[hPoint.UniqueId];
// Form the new class label by appending the previous label and the density-based label.
var previousClassLabel = FinalClassification.GetClassLabel(uPoint);
var densityClassLabel = densityClassification.GetClassLabel(hPoint);
var newClassLabel = $"{previousClassLabel}-{densityClassLabel}";
// 8. Pull point from its current cluster and add it to a new cluster
FinalClassification.Remove(uPoint);
FinalClassification.Add(uPoint, newClassLabel);
}
}
}
}
Timer.Stop("Reclassify by density");
Logger.Info($"Clusters split due to density-based reclassification: {numberOfClustersSplit}");
}
}
