public ClustersModel GetKmeanClusters(VrpData data, int clustersCount)
{
var centers = new Point2DReal[clustersCount];
var perm = Permutations.GetRandomPermutation(data.N - 1);
for (int i = 0; i < clustersCount; i++)
{
centers[i] = data.Customers[perm[i] + 1].Point;
}
var colors = new int[data.N];
var count = new int[clustersCount];
var changed = true;
while (changed)
{
changed = false;
count = new int[clustersCount];
for (int i = 1; i < data.N; i++)
{
var bestCenter = -1;
var bestDist = 0D;
for (int j = 0; j < clustersCount; j++)
{
if (bestCenter == -1 || centers[j].Dist(data.Customers[i].Point) < bestDist)
{
bestCenter = j;
bestDist = centers[j].Dist(data.Customers[i].Point);
}
}
if (colors[i] != bestCenter)
{
colors[i] = bestCenter;
changed = true;
}
}
for (int j = 0; j < clustersCount; j++)
{
var center = new Point2DReal(0, 0);
for (int i = 1; i < data.N; i++)
{
if (colors[i] == j)
{
center = center + data.Customers[i].Point;
count[j]++;
}
}
centers[j] = center * (1D / count[j]);
}
}
var result = new ClustersModel();
result.Color = colors;
result.Count = count;
result.Centers = centers;
return result;
}
private void DrawLine(Pen pen, Point2DReal a, Point2DReal b, Graphics g)
{
g.DrawLine(pen, (float) a.X, (float) a.Y, (float) b.X, (float) b.Y);
}
public static void Main(string[] args)
{
Thread.CurrentThread.CurrentCulture = CultureInfo.InvariantCulture;
//var lines = args[0].Split('\n');
string text;
using (var reader = File.OpenText(args[0]))
{
text = reader.ReadToEnd();
}
var lines = text.Split('\n');
var n = int.Parse(lines[0]);
var points = new Point2DReal[n];
for (int i = 0; i < n; i++)
{
var line = lines[i + 1].Split();
var x = double.Parse(line[0]);
var y = double.Parse(line[1]);
points[i] = new Point2DReal(x, y);
}
/*var matrix = new double[n,n];
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
matrix[i, j] = points[i].Dist(points[j]);
}
}
double avgDist = 0;
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
avgDist += matrix[i, j];
}
}
avgDist /= (n * n);
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
matrix[i, j] /= avgDist;
}
}*/
IMeasure measure;
if (n <= 6000)
{
measure = new MatrixMeasureFactory().CreateMatrixMeasure(points);
}
else
{
measure = new PointsMeasure(points);
}
var path = new Opt3().GetPath(n, measure);
var ans = points[path[0]].Dist(points[path[n - 1]]);
for (int i = 0; i < n - 1; i++)
{
ans += points[path[i]].Dist(points[path[i + 1]]);
}
Console.WriteLine("{0} 0", ans);
Console.WriteLine(string.Join(" ", path));
}
