/// <summary>
/// Gets the centroids for each cluster.
/// </summary>
/// <returns>The centroids and their class labels.</returns>
public List <ClusterCentroid> GetCentroids()
{
return(Clusters
.ClassLabels()
.Select(label => new ClusterCentroid {
ClusterLabel = label,
Centroid = UnsignedPoint.Centroid(Clusters.PointsInClass(label)),
Count = Clusters.PointsInClass(label).Count
}).ToList());
}
/// <summary>
/// Searches for the point in the first cluster that is closest to a corresponding point in the second cluster
/// and returns an approximate result.
///
/// This finds the centroid C1 of the first cluster, then the point P2 in the second cluster closest to centroid C1, then the
/// point P1 in the first cluster closest to P2.
///
/// NOTE: If the two clusters overlap or are shaped irregularly, this is likely to return a poor result.
/// If the clusters are spherical, the results are likely to be very good.
/// </summary>
/// <param name="color1">Indicates the first cluster to be searched.</param>
/// <param name="color2">Indicates the second cluster to be searched.</param>
/// <returns>An approximate result, inclusing one point from each cluster and the square of the distance between them.</returns>
public ClosestPair FindPairByCentroids(TLabel color1, TLabel color2)
{
var points1 = Clusters.PointsInClass(color1);
var points2 = Clusters.PointsInClass(color2);
var c1 = UnsignedPoint.Centroid(points1);
var p2 = points2
.OrderBy(p => c1.Measure(p))
.First()
;
var closest = points1.Select(p1 => new ClosestPair(color1, p1, color2, p2, p1.Measure(p2))).OrderBy(cp => cp.SquareDistance).First();
return(closest.Swap(color1));
}
public ClusterRadius(IList <UnsignedPoint> points)
{
Centroid = UnsignedPoint.Centroid(points);
var radiusSum = 0.0;
MaximumRadius = 0;
foreach (var point in points)
{
var distance = Centroid.Distance(point);
MaximumRadius = Math.Max(MaximumRadius, distance);
radiusSum += distance;
}
if (points.Count > 0)
{
MeanRadius = radiusSum / points.Count;
}
}
