/// <inheritdoc />
public double Evaluate(ClusterSet <TInstance> clusterSet)
{
// undefined if only one cluster
if (clusterSet.Count < 2)
{
return(double.NaN);
}
// gets clusters' centroids
var centroids = clusterSet.Select(t => this._centroidFunc(t)).ToList();
var n = 0;
var sum = 0d;
var minCentDist = double.MaxValue;
for (var i = 0; i < clusterSet.Count; i++)
{
n += clusterSet[i].Count;
for (var j = i + 1; j < clusterSet.Count; j++)
{
// updates weighted pairwise distances
var betweenClusterDist = this.DissimilarityMetric.Calculate(centroids[i], centroids[j]);
minCentDist = Math.Min(minCentDist, betweenClusterDist);
sum += clusterSet[i].Sum(
inst1 => clusterSet[j]
.Sum(inst2 => this.DissimilarityMetric.Calculate(inst1, inst2) * betweenClusterDist));
}
}
return(2 * sum * minCentDist / (n * (n - 1)));
}
/// <inheritdoc />
public double Evaluate(ClusterSet <TInstance> clusterSet)
{
// undefined if only one cluster
if (clusterSet.Count < 2)
{
return(double.NaN);
}
// gets clusters' centroids
var centroids = clusterSet.Select(t => this._centroidFunc(t)).ToList();
var n = 0;
var sum = 0d;
for (var i = 0; i < clusterSet.Count; i++)
{
n += clusterSet[i].Count;
// updates sum of squared distances to centroids
foreach (var instance in clusterSet[i])
{
var dist = this.DissimilarityMetric.Calculate(instance, centroids[i]);
sum += dist * dist;
}
}
return(Math.Sqrt(sum / n));
}
/// <inheritdoc />
public double Evaluate(ClusterSet <TInstance> clusterSet)
{
// undefined if only one cluster
if (clusterSet.Count < 2)
{
return(double.NaN);
}
// gets clusters' centroids
var centroids = clusterSet.Select(t => this._centroidFunc(t)).ToList();
var n = 0;
var sum = 0d;
var minCentDist = double.MaxValue;
for (var i = 0; i < clusterSet.Count; i++)
{
n += clusterSet[i].Count;
// updates sum of distances to centroids
foreach (var instance in clusterSet[i])
{
var dist = this.DissimilarityMetric.Calculate(instance, centroids[i]);
sum += dist * dist;
}
// updates min between-cluster distance
for (var j = i + 1; j < clusterSet.Count; j++)
{
var betweenClusterDist = this.DissimilarityMetric.Calculate(centroids[i], centroids[j]);
minCentDist = Math.Min(minCentDist, betweenClusterDist * betweenClusterDist);
}
}
return(-sum / (n * minCentDist));
}