public IEnumerable <Cluster <T> > Clusterize(IEnumerable <Point <T> > points, float groupDistance)
{
var result = new List <Cluster <T> >();
var processingPoints = points.ToList();
var processedPoints = new HashSet <Point <T> >();
foreach (var processingPoint in processingPoints)
{
if (processedPoints.Contains(processingPoint))
{
continue;
}
var newGroup = new Cluster <T>();
newGroup.AddPoint(processingPoint);
foreach (var point in processingPoints)
{
if (point == processingPoint || processedPoints.Contains(point))
{
continue;
}
if (Vector2.SqrMagnitude(point.Position - processingPoint.Position) < groupDistance * groupDistance)
{
newGroup.AddPoint(point);
processedPoints.Add(processingPoint);
processedPoints.Add(point);
}
}
if (newGroup.Points.Count > 1)
{
result.Add(newGroup);
}
}
return(result);
}
public Partition GetPartition()
{
LightWeightGraph lwg = (_reassignNodes) ? GetAttackedGraphWithReassignment() : GetAttackedGraph();
//Get our cluster Assignment
List <List <int> > componentList = lwg.GetComponents();
//Setup our Clusters
List <Cluster> clusterList = new List <Cluster>();
for (int i = 0; i < componentList.Count; i++)
{
Cluster c = new Cluster(i);
foreach (var n in componentList[i])
{
c.AddPoint(new ClusteredItem(lwg[n].Label));
}
clusterList.Add(c);
}
String meta = "VAT: \nRemoved Count:" + NumNodesRemoved + "\n"
+ String.Join(",", _nodeRemovalOrder.GetRange(0, NumNodesRemoved));
return(new Partition(clusterList, g, meta));
}
private void AddToCluster(KMPoint point)
{
double minDistance = 0;
Cluster current = null;
if (point == null)
{
throw new NullReferenceException("point is null");
}
foreach (var cluster in _clusters)
{
double distance = point.GetDistanceTo(cluster.Center);
if (current == null || minDistance > distance)
{
minDistance = distance;
current = cluster;
}
}
if (current != null)
{
current.AddPoint(point);
}
}
private static HashSet <Cluster> Init(List <Point> list, int k)
{
HashSet <Cluster> clusters = new HashSet <Cluster>();
HashSet <int> chosenPoint = new HashSet <int>();
Random rand = new Random();
int newPoint = 0;
for (int i = 0; i < k; i++)
{
do
{
newPoint = rand.Next(0, list.Count);
} while (chosenPoint.Contains(newPoint));
chosenPoint.Add(newPoint);
var newCluster = new Cluster();
newCluster.AddPoint(list[newPoint]);
newCluster.UpdateCentroid();
clusters.Add(newCluster);
}
int n = 0;
foreach (var point in list)
{
if (point == clusters.FirstOrDefault().Points.FirstOrDefault())
{
n = 0;
}
FindMinDist(point, clusters).AddPoint(point);
n++;
}
return(clusters);
}
/// <summary>
/// 将ASCDTSubGraph的结果保存到clusters中
/// </summary>
private void CreateClusters()
{
if (m_bLocal == false)
{
for (int i = 0; i < m_globalSubGraphs.Count; i++)
{
Cluster cluster = new Cluster();
cluster.SetClusterIndex(i + 1);
ASCDTSubGraph subGraph = m_globalSubGraphs[i];
List <ITinNode> tinNodes = subGraph.GetNodeList();
for (int j = 0; j < tinNodes.Count; j++)
{
IPoint newPoint = new PointClass();
tinNodes[j].QueryAsPoint(newPoint);
IZAware pZAware = newPoint as IZAware;
pZAware.ZAware = false;
cluster.AddPoint(newPoint);
}
cluster.CreateConvexHull(m_dataInfo.GetSpatialReference());
m_Clusters.AddCluster(cluster);
}
}
else
{
for (int i = 0; i < m_localSubGraphs.Count; i++)
{
Cluster cluster = new Cluster();
cluster.SetClusterIndex(i + 1);
ASCDTSubGraph subGraph = m_localSubGraphs[i];
List <ITinNode> tinNodes = subGraph.GetNodeList();
for (int j = 0; j < tinNodes.Count; j++)
{
IPoint newPoint = new PointClass();
tinNodes[j].QueryAsPoint(newPoint);
IZAware pZAware = newPoint as IZAware;
pZAware.ZAware = false;
cluster.AddPoint(newPoint);
}
cluster.CreateConvexHull(m_dataInfo.GetSpatialReference());
m_Clusters.AddCluster(cluster);
}
}
}
/// <summary>
/// 通过核心点对簇进行扩展
/// </summary>
/// <param name="cluster"></param>
/// <param name="pntIndex"></param>
/// <returns></returns>
private Cluster ExpandCluster(Cluster cluster, int pntIndex)
{
cluster.AddPoint(m_DBSCANPnts[pntIndex].GetPoint());
List <DBSCANPoint> neighbors = m_DBSCANPnts[pntIndex].GetNeighborPoints();
for (int i = 0; i < neighbors.Count; i++)
{
int neighborIndex = GetDBSCANPointIndexByOID(neighbors[i].GetOID());
if (m_DBSCANPnts[neighborIndex].IsVisited() == true)
{
continue;
}
m_DBSCANPnts[neighborIndex].SetAsVisited();
//迭代
//边界点跟其邻域内的某个核心点放在同一个簇中
// if (m_DBSCANPnts[neighborIndex].GetPointType() != 0)
if (m_DBSCANPnts[neighborIndex].GetPointType() == 1)
{
ExpandCluster(cluster, neighborIndex);
}
}
return(cluster);
}
static void PrintSegmentMap(Map map, int blueThreshold)
{
BinaryMatrix discovered = new BinaryMatrix(map.Width, map.Height);
BinaryMatrix obstacles = map.ScanObstacles(blueThreshold);
List <Cluster> clusters = new List <Cluster>();
for (int x = 0; x < map.Width; x++)
{
for (int y = 0; y < map.Height; y++)
{
if (!discovered[x, y] && obstacles[x, y])
{
discovered[x, y] = true;
Cluster cluster = new Cluster();
Queue <XY> queue = new Queue <XY>();
queue.Enqueue(new XY(x, y));
while (queue.Count > 0)
{
XY point = queue.Dequeue();
cluster.AddPoint(point);
foreach (XY neighbor in point.GetNeighbors())
{
if (
neighbor.IsInsideBox(map.Box) &&
!discovered[neighbor] &&
obstacles[neighbor]
)
{
discovered[neighbor] = true;
queue.Enqueue(neighbor);
}
}
}
clusters.Add(cluster);
}
}
}
Console.WriteLine("Discovered {0} clusters", clusters.Count);
Bitmap clustersMap = new Bitmap(map.Width, map.Height);
for (int x = 0; x < map.Width; x++)
{
for (int y = 0; y < map.Height; y++)
{
clustersMap.SetPixel(x, y, Color.Black);
}
}
foreach (Cluster cluster in clusters)
{
for (int x = cluster.Left; x <= cluster.Right; x++)
{
for (int y = cluster.Top; y <= cluster.Bottom; y++)
{
clustersMap.SetPixel(x, y, Color.Red);
}
}
}
clustersMap.Save(FILE_BASE + "background-segments.jpg");
}