// Called by LandMap.GetZones (), returns number of subregions
public int ClusterLocationsAccordKMedoidsVoronoi(MapPoint[,] points, TerrainVerticesDatabase vertDatabase)
{
// K-means cluster algorithm to separate locations in the regions
int regionId = 0;
for (int isleId = 0; isleId < regions.Count; isleId++)
{
MapRegion region = regions[isleId];
double[][] tileLocations = new double[region.turf.Count][];
for (int i = 0; i < tileLocations.Length; i++)
{
tileLocations[i] = new double[3];
TerrainVertData vertData = vertDatabase.GetVertDataFromRegionTile(region.turf[i], isleId);
if (vertData != null)
{
tileLocations[i][0] = region.turf[i].x;
tileLocations[i][1] = vertData.inlandPosition;
tileLocations[i][2] = region.turf[i].y;
}
else
{
LoggerTool.Post("Null from VertDB for " + region.turf[i].ToString());
}
}
int k = InitializeNumOfK(tileLocations.Length);
k = Mathf.Max(1, k);
Debug.Log(k + " centroid(s)");
VoronoiIteration kmedoidsVoronoi = new VoronoiIteration(k);
kmedoidsVoronoi.MaxIterations = 100;
KMedoidsClusterCollection <double> clusters = kmedoidsVoronoi.Learn(tileLocations);
int[] labels = clusters.Decide(tileLocations);
Debug.Log("Number of labels = " + labels.Length);
for (int i = 0; i < labels.Length; i++)
{
points[(int)tileLocations[i][0], (int)tileLocations[i][2]].areaValue = regionId + labels[i];
}
regionId += k;
}
return(regionId);
}
public void doc_learn()
{
#region doc_learn
Accord.Math.Random.Generator.Seed = 0;
// Declare some observations
int[][] observations = new int[][]
{
new[] { 2, 6 }, // a
new[] { 3, 4 }, // a
new[] { 3, 8 }, // a
new[] { 4, 7 }, // a
new[] { 6, 2 }, // b
new[] { 6, 4 }, // b
new[] { 7, 3 }, // b
new[] { 7, 4 }, // b
new[] { 8, 5 }, // b
new[] { 7, 6 } // b
};
// Create a new 2-Medoids algorithm.
var kmedoidsVi = new VoronoiIteration <int>(2, new Manhattan());
kmedoidsVi.MaxIterations = 100;
// Set initial medoids
kmedoidsVi.Clusters.Centroids[0] = observations[1];
kmedoidsVi.Clusters.Centroids[1] = observations[7];
// Compute and retrieve the data centroids
var clusters = kmedoidsVi.Learn(observations);
// Use the centroids to parition all the data
int[] labels = clusters.Decide(observations);
#endregion
Assert.AreEqual(labels[0], labels[1]);
Assert.AreEqual(labels[0], labels[2]);
Assert.AreEqual(labels[0], labels[3]);
Assert.AreEqual(labels[4], labels[5]);
Assert.AreEqual(labels[4], labels[6]);
Assert.AreEqual(labels[4], labels[7]);
Assert.AreEqual(labels[4], labels[8]);
Assert.AreEqual(labels[4], labels[9]);
}
public int ClusterLocationsAccordKMedoidsVoronoi(MapPoint[,] points)
{
// K-medoids cluster algorithm to separate locations in the regions
int regionId = 0;
for (int isleId = 0; isleId < regions.Count; isleId++)
{
MapRegion region = regions[isleId];
double[][] tileLocations = new double[region.turf.Count][];
for (int i = 0; i < tileLocations.Length; i++)
{
tileLocations[i] = new double[2];
tileLocations[i][0] = region.turf[i].x;
tileLocations[i][1] = region.turf[i].y;
}
int k = InitializeNumOfK(region.turf.Count);
Debug.Log(k + " centroid(s)");
VoronoiIteration kmedoidsVoronoi = new VoronoiIteration(k);
kmedoidsVoronoi.MaxIterations = 100;
KMedoidsClusterCollection <double> clusters = kmedoidsVoronoi.Learn(tileLocations);
int[] labels = clusters.Decide(tileLocations);
Debug.Log("Number of labels = " + labels.Length);
for (int i = 0; i < labels.Length; i++)
{
points[(int)tileLocations[i][0], (int)tileLocations[i][1]].areaValue = regionId + labels[i];
}
regionId += k;
}
return(regionId);
}
