/// <summary>
/// Creates a new CentroidPoint and fills its values with a deep copy of the values of an existing CentroidPoint.
/// The ID is not copied from the existing CentroidPoint.
/// </summary>
/// <returns>The newly created CentroidPoint.</returns>
public static CentroidPoint CentroidOfValues(CentroidPoint toCopy)
{
CentroidPoint res = new CentroidPoint(toCopy.Dim);
res.CopyValuesFrom(toCopy);
return(res);
}
/// <summary>
/// Initialize empty clusters with centroid values based on their distances from each other.
/// </summary>
/// <param name="clusterCount">Number of clusters to initialize.</param>
protected void InitEmptyKmeansClusters(uint clusterCount)
{
_clusters = new Dictionary <uint, Cluster>();
CentroidPoint firstcentroid = new CentroidPoint(PointDim);
firstcentroid.FillWithRandomValues(); //first center is random
_clusters.Add(firstcentroid.ID, new Cluster(firstcentroid));
for (uint i = 1; i < clusterCount; i++)
{
CentroidPoint centroid = new CentroidPoint(PointDim);
Point farthest = new Point(PointDim);
float maxDist = 0.0f;
foreach (ImagePoint point in _points)
{
float dist = 0.0f;
foreach (var cluster in _clusters)
{
dist += Point.SquaredEuclDist(cluster.Value.Centroid, point);
}
if (dist > maxDist)
{
maxDist = dist;
farthest = point;
}
}
centroid.CopyValuesFrom(farthest);
_clusters.Add(centroid.ID, new Cluster(centroid));
}
}
/// <summary>
/// Initialize empty clusters with centroid values randomly selected from the pixels of the loaded input image.
/// </summary>
/// <param name="clusterCount">Number of clusters to initialize.</param>
protected void InitEmptyClustersFromRandomImagePoints(uint clusterCount)
{
_clusters = new Dictionary <uint, Cluster>();
List <Point> randomPoints = PickRandomImagePoints(_random, (int)clusterCount);
for (uint i = 0; i < clusterCount; i++)
{
CentroidPoint centroid = new CentroidPoint(PointDim);
centroid.CopyValuesFrom(randomPoints[(int)i]);
_clusters.Add(centroid.ID, new Cluster(centroid));
}
}