//public static Int64 attributes_num;
static void Main(string[] args)
{
MyCustomDatasetItem[] featureData = { };
List <MyCustomDatasetItem> testPoints = new List <MyCustomDatasetItem>();
//Dictionary<string, List<Tuple<string, double>>> testPoints = new Dictionary<string, List<Tuple<string, double>>>();
StreamReader lines = new StreamReader(@"B:\TrendQuery\output.txt");//Source\tTarget\tWeight
string line;
Int64 row = 22;
List <Int64> columns = new List <Int64>();
List <double> data = new List <double>();
//attributes_num = 0;
//set points
//List<string> nodes = new List<string>();
while ((line = lines.ReadLine()) != null)
{
string[] tmp = line.Split('\t');
int indx = contains_MDS(testPoints, tmp[0]);
if (indx == -1)
{
testPoints.Add(new MyCustomDatasetItem(tmp[0]));
}
else
{
testPoints[indx].Columns.Add(tmp[1], double.Parse(tmp[2]));
}
indx = contains_MDS(testPoints, tmp[1]);
if (indx == -1)
{
testPoints.Add(new MyCustomDatasetItem(tmp[1]));
}
else
{
testPoints[indx].Columns.Add(tmp[0], double.Parse(tmp[2]));
}
}
featureData = testPoints.ToArray();
HashSet <MyCustomDatasetItem[]> clusters;
var dbs = new DbscanAlgorithm <MyCustomDatasetItem>(((x, y) => Distances.dist(x, y)));//Math.Sqrt(((x.X - y.X) * (x.X - y.X)) + ((x.Y - y.Y) * (x.Y - y.Y))));
dbs.ComputeClusterDbscan(allPoints: featureData, epsilon: 200, minPts: 10, clusters: out clusters);
List <string> cluster_points = new List <string>();
int k = 1;
foreach (var cluster in clusters)
{
for (int i = 0; i < cluster.Length; i++)
{
cluster_points.Add(cluster[i].Row.ToString() + "\t" + k.ToString());
}
k++;
}
File.WriteAllLines(@"B:\TrendQuery\DBSClusters.txt", cluster_points.ToArray <string>());
}
static void Main(string[] args)
{
MyCustomDatasetItem[] featureData = { };
List <MyCustomDatasetItem> testPoints = new List <MyCustomDatasetItem>();
for (int i = 0; i < 1000; i++)
{
//points around (1,1) with most 1 distance
testPoints.Add(new MyCustomDatasetItem(1, 1 + ((float)i / 1000)));
testPoints.Add(new MyCustomDatasetItem(1, 1 - ((float)i / 1000)));
testPoints.Add(new MyCustomDatasetItem(1 - ((float)i / 1000), 1));
testPoints.Add(new MyCustomDatasetItem(1 + ((float)i / 1000), 1));
//points around (5,5) with most 1 distance
testPoints.Add(new MyCustomDatasetItem(5, 5 + ((float)i / 1000)));
testPoints.Add(new MyCustomDatasetItem(5, 5 - ((float)i / 1000)));
testPoints.Add(new MyCustomDatasetItem(5 - ((float)i / 1000), 5));
testPoints.Add(new MyCustomDatasetItem(5 + ((float)i / 1000), 5));
}
featureData = testPoints.ToArray();
HashSet <MyCustomDatasetItem[]> clusters;
var dbs = new DbscanAlgorithm <MyCustomDatasetItem>((x, y) => Math.Sqrt(((x.X - y.X) * (x.X - y.X)) + ((x.Y - y.Y) * (x.Y - y.Y))));
dbs.ComputeClusterDbscan(allPoints: featureData, epsilon: .01, minPts: 10, clusters: out clusters);
}
// Called by LandMap.GetZones (), returns number of subregions
public int ClusterLocationsDBSCAN(float epsilon, int minPts, MapPoint[,] points, TerrainVerticesDatabase vertDatabase)
{
// DBSCAN cluster algorithm to separate locations in the regions
int regionId = 0;
for (int isleId = 0; isleId < regions.Count; isleId++)
{
MapRegion region = regions[isleId];
List <TilePoint> tileLocations = new List <TilePoint> ();
for (int i = 0; i < region.turf.Count; i++)
{
TerrainVertData vertData = vertDatabase.GetVertDataFromRegionTile(region.turf[i], isleId);
if (vertData != null)
{
tileLocations.Add(new TilePoint(region.turf[i].x, vertData.inlandPosition, region.turf[i].y));
}
else
{
LoggerTool.Post("Null from VertDB for " + region.turf[i].ToString());
}
}
HashSet <TilePoint[]> clusters;
var dbscan = new DbscanImplementation.DbscanAlgorithm <TilePoint> ((t1, t2) => TilePoint.Dist(t1, t2));
dbscan.ComputeClusterDbscan(allPoints: tileLocations.ToArray(), epsilon: epsilon, minPts: minPts, clusters: out clusters);
int k = clusters.Count;
Debug.Log("Number of clusters = " + k);
int clstCount = 0;
foreach (var tiles in clusters)
{
for (int i = 0; i < tiles.Length; i++)
{
points[(int)tiles[i].X, (int)tiles[i].Z].areaValue = regionId + clstCount;
}
clstCount++;
}
regionId += k;
}
return(regionId);
}
