private void ClusteringWorkerDoWork(object sender, DoWorkEventArgs e)
{
// checks data points
if (this._dataPoints == null || this._dataPoints.Count == 0)
{
return;
}
// selects linkage criterion
ILinkageCriterion <DataPoint> linkage;
var selectedIndex = e.Argument;
switch (selectedIndex)
{
case 1:
linkage = new CompleteLinkage <DataPoint>(this._dissimilarityMetric);
break;
case 2:
linkage = new SingleLinkage <DataPoint>(this._dissimilarityMetric);
break;
case 3:
linkage = new MinimumEnergyLinkage <DataPoint>(this._dissimilarityMetric);
break;
case 4:
linkage = new CentroidLinkage <DataPoint>(this._dissimilarityMetric, DataPoint.GetMedoid);
break;
case 5:
linkage = new WardsMinimumVarianceLinkage <DataPoint>(
this._dissimilarityMetric, DataPoint.GetMedoid);
break;
default:
linkage = new AverageLinkage <DataPoint>(this._dissimilarityMetric);
break;
}
// clusters data-points
var clusteringAlg = new AgglomerativeClusteringAlgorithm <DataPoint>(linkage);
this._clusteringResult = clusteringAlg.GetClustering(this._dataPoints);
}
public static void Main(string[] args)
{
Plot generatedDataPlot = new Plot();
Spawner spawner = new Spawner(STD_DEV);
List <PointF> allPoints = new List <PointF>();
for (int i = 0; i < CLUSTER_COUNT; ++i)
{
spawner.ResetCenter(MIN_CENTER_DISTANCE, MAX_CENTER_DISTANCE);
PointF[] points = spawner.Spawn(POINT_COUNT);
allPoints.AddRange(points);
Color color = generatedDataPlot.GetNextColor();
generatedDataPlot.AddScatterPoints(points, color, label: $"Points {i + 1}");
generatedDataPlot.AddPoint(spawner.Center.X, spawner.Center.Y, color, 25);
}
generatedDataPlot.Legend();
PlotForm generatedDataPlotForm = new PlotForm(generatedDataPlot, "source_data");
generatedDataPlotForm.ShowDialog();
Plot grayDataPlot = new Plot();
grayDataPlot.AddScatterPoints(allPoints.ToArray(), label: "Gray points");
grayDataPlot.Legend();
PlotForm grayDataPlotForm = new PlotForm(grayDataPlot, "gray_data");
grayDataPlotForm.ShowDialog();
KMeansClusterizer clusterizer = new KMeansClusterizer();
List <Dictionary <PointF, List <PointF> > > clusterizingHistory = clusterizer.Clusterize(allPoints, CLUSTER_COUNT);
PlotForm resultPlotForm = new PlotForm(CreateClusterizingPlot(clusterizingHistory.Last()), "crusterized");
resultPlotForm.ShowDialog();
PlotForm historyForm = new PlotForm(clusterizingHistory.Select(c => CreateClusterizingPlot(c)).ToList(), "history_");
historyForm.ShowDialog();
CentroidLinkage <DataPoint> linkage = new CentroidLinkage <DataPoint>(
new DissimilarityMetric(),
cluster => new DataPoint(
cluster.Average(p => p.X),
cluster.Average(p => p.Y)
)
);
AgglomerativeClusteringAlgorithm <DataPoint> algorithm = new AgglomerativeClusteringAlgorithm <DataPoint>(linkage);
HashSet <DataPoint> dataPoints = allPoints.Select(p => new DataPoint(p)).ToHashSet();
ClusteringResult <DataPoint> clusteringResult = algorithm.GetClustering(dataPoints);
ClusterSet <DataPoint> result = clusteringResult[clusteringResult.Count - 3];
Plot aglomeraPlot = new Plot();
foreach (Cluster <DataPoint> resultCluster in result)
{
Color color = aglomeraPlot.GetNextColor();
aglomeraPlot.AddScatterPoints(
resultCluster.Select(p => (double)p.X).ToArray(),
resultCluster.Select(p => (double)p.Y).ToArray(),
color
);
aglomeraPlot.AddPoint(
resultCluster.Select(p => p.X).Average(),
resultCluster.Select(p => p.Y).Average(),
color, 25
);
}
PlotForm aglomeraForm = new PlotForm(aglomeraPlot, "aglomera");
aglomeraForm.ShowDialog();
clusteringResult.SaveD3DendrogramFile(Environment.CurrentDirectory + "/dendro.json");
Console.ReadLine();
}
