finalized
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54
src/Helper.cs
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54
src/Helper.cs
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@ -0,0 +1,54 @@
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using System.Diagnostics;
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using System.Drawing;
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using System.Globalization;
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using ScottPlot;
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namespace kMeans;
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public static class Helper
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{
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public static Plot CreatePlot(IEnumerable<Point> points, IEnumerable<Point> centroids)
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{
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var plot = new Plot();
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plot.Legend(true, Alignment.UpperRight);
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var colors = new Dictionary<int, Color>();
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var colorGroups = points.GroupBy(x => x.ClusterId);
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foreach (var clusterGroup in colorGroups)
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{
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var color = plot.GetNextColor();
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colors.Add(clusterGroup.Key, color);
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var xs = clusterGroup.Select(p => p.X).ToArray();
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var ys = clusterGroup.Select(p => p.Y).ToArray();
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plot.AddScatterPoints(xs, ys, color);
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}
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const MarkerShape marker = MarkerShape.cross;
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const float size = 10f;
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foreach (var (x, y, clusterId) in centroids)
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{
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var color = colors.TryGetValue(clusterId, out var c) ? c : plot.GetNextColor();
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plot.AddScatterPoints(new[] { x }, new[] { y }, color, size, marker, $"ClusterId: {clusterId}");
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}
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return plot;
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}
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public static void ExportAndPreviewPlot(Plot plot, string path)
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{
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plot.SaveFig(path);
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using var p = Process.Start(new ProcessStartInfo(path) { UseShellExecute = true });
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p?.WaitForExit();
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}
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public static IEnumerable<Point> ParseCsv(string path)
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{
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var lines = File.ReadLines(path);
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foreach (var line in lines)
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{
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var current = line.Split(',');
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if (!double.TryParse(current[0], NumberStyles.Any, CultureInfo.InvariantCulture, out var x)) continue;
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if (!double.TryParse(current[1], NumberStyles.Any, CultureInfo.InvariantCulture, out var y)) continue;
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yield return new Point(x, y, -1);
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}
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}
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}
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8
src/Point.cs
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8
src/Point.cs
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@ -0,0 +1,8 @@
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namespace kMeans;
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public record Point(double X, double Y, int ClusterId)
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{
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public int ClusterId { get; set; } = ClusterId;
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public double X { get; set; } = X;
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public double Y { get; set; } = Y;
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}
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120
src/Program.cs
120
src/Program.cs
@ -1,7 +1,4 @@
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using ScottPlot;
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using System.Diagnostics;
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using System.Drawing;
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using System.Globalization;
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using static kMeans.Helper;
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namespace kMeans;
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@ -13,78 +10,43 @@ public static class Program
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public static void Main()
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{
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Console.WriteLine("k-Means-Algorithmus");
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Console.WriteLine("k-Means-Algorithm");
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var points = ParseCsv(Coordinates).ToList();
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var centroids = InitializeRandomlyCentroids(points, K).ToList();
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AssignPointsToCentroids(points, centroids);
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UpdateCentroids(points, centroids);
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var plot = CreatePlot(points, centroids);
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ExportAndPreviewPlot(plot);
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}
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private static bool UpdateCentroids(List<Point> points, List<Point> centroids)
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{
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// calculate mean of all points of one cluster.
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foreach (var centroid in centroids)
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var centroids = InitializeRandomlyCentroids(points, K).ToList();
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var update = true;
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while (update)
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{
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var clusterPoints = points.Where(x => x.ClusterId == centroid.ClusterId);
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double newMeanX = 0;
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double newMeanY = 0;
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foreach (var cluster in clusterPoints)
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{
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centroid.X = 3;
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}
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AssignPointsToCentroids(points, centroids);
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update = UpdateCentroids(points, centroids);
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}
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return false;
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var plot = CreatePlot(points, centroids);
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ExportAndPreviewPlot(plot, PlotOut);
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}
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private static void AssignPointsToCentroids(List<Point> points, List<Point> centroids)
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{
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foreach (var point in points)
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{
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var id = 0;
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var distance = double.MaxValue;
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foreach (var centroid in centroids)
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{
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// calculate euclid distance and assign id.
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var currentDistance = Distance(centroid, point);
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if (currentDistance > distance) continue;
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distance = currentDistance;
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id = centroid.ClusterId;
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}
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point.ClusterId = id;
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}
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}
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private static Plot CreatePlot(IEnumerable<Point> points, IEnumerable<Point> centroids)
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private static double Distance(Point centroid, Point point)
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{
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var sw = Stopwatch.StartNew();
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var plot = new Plot();
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var colors = new Dictionary<int, Color>();
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var colorGroups = points.GroupBy(x => x.ClusterId);
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foreach (var clusterGroup in colorGroups)
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{
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var color = plot.GetNextColor();
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colors.Add(clusterGroup.Key, color);
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var xs = clusterGroup.Select(p => p.X).ToArray();
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var ys = clusterGroup.Select(p => p.Y).ToArray();
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plot.AddScatterPoints(xs, ys, color);
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}
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const MarkerShape marker = MarkerShape.cross;
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const float size = 10f;
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foreach (var centroid in centroids)
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{
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var color = colors.TryGetValue(centroid.ClusterId, out var c) ? c : plot.GetNextColor();
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plot.AddScatterPoints(new[] { centroid.X }, new[] { centroid.Y }, color, size, marker);
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}
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sw.Stop();
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Console.WriteLine($"[{nameof(CreatePlot)}] Elapsed: {sw.ElapsedMilliseconds}ms");
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return plot;
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}
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private static void ExportAndPreviewPlot(Plot plot)
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{
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var sw = Stopwatch.StartNew();
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plot.SaveFig(PlotOut);
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Process.Start(new ProcessStartInfo(PlotOut) { UseShellExecute = true });
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sw.Stop();
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Console.WriteLine($"[{nameof(ExportAndPreviewPlot)}] Elapsed: {sw.ElapsedMilliseconds}ms");
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return Math.Sqrt(Math.Pow(centroid.X - point.X, 2) + Math.Pow(centroid.Y - point.Y, 2));
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}
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private static IEnumerable<Point> InitializeRandomlyCentroids(IReadOnlyCollection<Point> points, int k)
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@ -99,35 +61,35 @@ public static class Program
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{
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var x = (minX + maxX) * rnd.NextDouble();
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var y = (minY + maxY) * rnd.NextDouble();
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yield return new Point(x, y, true, i);
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var point = new Point(x, y, i);
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Console.WriteLine(point);
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yield return point;
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}
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}
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private static IEnumerable<Point> ParseCsv(string path)
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private static bool UpdateCentroids(IReadOnlyCollection<Point> points, List<Point> centroids)
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{
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var lines = File.ReadLines(path);
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foreach (var line in lines)
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// calculate mean of all points of one cluster.
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var updated = false;
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foreach (var centroid in centroids)
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{
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var current = line.Split(',');
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if (!double.TryParse(current[0], NumberStyles.Any, CultureInfo.InvariantCulture, out var x)) continue;
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if (!double.TryParse(current[1], NumberStyles.Any, CultureInfo.InvariantCulture, out var y)) continue;
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yield return new Point(x, y, false, -1);
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updated |= UpdateClusterCentroid(centroid, points);
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}
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}
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}
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public class Point
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{
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public Point(double x, double y, bool focusPoint, int clusterId)
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return updated;
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}
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private static bool UpdateClusterCentroid(Point centroid, IEnumerable<Point> points)
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{
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X = x;
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Y = y;
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FocusPoint = focusPoint;
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ClusterId = clusterId;
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}
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var pointsOfCluster = points.Where(p => p.ClusterId == centroid.ClusterId).ToArray();
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var sumX = pointsOfCluster.Sum(p => p.X);
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var meanX = sumX / pointsOfCluster.Length;
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var sumY = pointsOfCluster.Sum(p => p.Y);
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var meanY = sumY / pointsOfCluster.Length;
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public int ClusterId { get; set; }
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public bool FocusPoint { get; set; }
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public double X { get; set; }
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public double Y { get; set; }
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if (!(Math.Abs(centroid.X - meanX) > 1e-5) || !(Math.Abs(centroid.Y - meanY) > 1e-5)) return false;
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centroid.X = meanX;
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centroid.Y = meanY;
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return true;
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}
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}
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