public void test2()
{
WriteLine("Generating the normal samples spacial points. ");
double[] mu1 = new double[] { 200, 0 }, mu2 = new double[] { -200, 0 };
double sd = 50;
int EachClusterSize = 100;
// SpacialPoint ExpectedCenter1 = new SpacialPoint(mu1);
// SpacialPoint ExpectedCenter2 = new SpacialPoint(mu2);
WriteLine($"2 hypothetical centers: [[{mu1[0]}, {mu1[1]}], [{mu2[0]},{mu2[1]}],");
int Counter = 10;
WriteLine("Average Normalized Deviations from centroid to centers");
while (--Counter != 0)
{
Point[] samples1 = SpatialPoint.NormalRandomPoints(mu1, sd, EachClusterSize);
Point[] samples2 = SpatialPoint.NormalRandomPoints(mu2, sd, EachClusterSize);
Point[] merged = new Point[samples1.Length + samples2.Length];
Array.Copy(samples1, 0, merged, 0, samples1.Length);
Array.Copy(samples2, 0, merged, samples1.Length, samples2.Length);
KMinSpanningTree Kmst = new KMinSpanningTree(merged);
WriteLine("------------------------Centroids:----------------------------------");
Point[][] SetOfClusters = MakeNestedArrayFomPointCollection(Kmst.KSpanningTree);
SaveTheClustersToJson($"tests_output/test2_clusters{Counter}.json", SetOfClusters);
}
}
public void Test1()
{
Point[] Points = new Point[]
{
new SpatialPoint(new int[] { 1, 0 }),
new SpatialPoint(new int[] { 2, 0 }),
new SpatialPoint(new int[] { 3, 0 }),
new SpatialPoint(new int[] { 3, 10 }),
new SpatialPoint(new int[] { 4, 0 })
};
KMinSpanningTree Kmst = new KMinSpanningTree(Points);
Kmst.KMinKruskal();
IImmutableSet <Edge> ChosenEdges = Kmst.ChosenEdges;
WriteLine($"The clustering threshold: {Kmst.ClusterSizeThreshold}");
Assert.IsTrue(ChosenEdges.Count != 0);
foreach (Edge e in Kmst.ChosenEdges)
{
WriteLine($"{e}");
}
foreach (PointCollection pc in Kmst.KSpanningTree)
{
WriteLine(pc);
}
}
