예제 #1
0
 private async void rbtnPlotList_Click(object sender, RibbonControlEventArgs e)
 {
     var frm = new PlotForm();
     await Task.Run(() => frm.ShowDialog());
 }
예제 #2
0
        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();
        }