/// <summary>
/// GetGPartition is different from GetPartition in 2 ways:
/// 1. It does not require a connected graph.
/// 2. If there are too many clusters, it combines them such that the desired number of clusters is returned
/// </summary>
/// <returns>A partitioning of the graph</returns>
public Partition GetGPartition()
{
DistanceMatrix mat = null;
if (_data.Type == AbstractDataset.DataType.DistanceMatrix)
{
mat = (DistanceMatrix)_data;
}
else if (_data.Type == AbstractDataset.DataType.PointSet)
{
mat = ((PointSet)_data).GetDistanceMatrix();
}
//get the actual partition (if graph not necessarily connected)
Partition partition = Partition.GetPartition((LightWeightGraph)_data);
//Dictionary to hold VAT
var vatMap = new Dictionary <int, VAT>();
//Dictionary to hold subset array
var subsetMap = new Dictionary <int, int[]>();
while (partition.Clusters.Count < _minK)
//while (clusterList.Count < _minK)
{
Console.WriteLine("Count = " + partition.Clusters.Count);
Console.WriteLine("mink = " + _minK);
//Calculate the VAT for all values
foreach (var c in partition.Clusters.Where(c => !vatMap.ContainsKey(c.ClusterId)))
{
//We must calculate a graph for this subset of data
List <int> clusterSubset = c.Points.Select(p => p.Id).ToList();
//Now calculate Vat
LightWeightGraph lwg;
if (_data.Type == AbstractDataset.DataType.Graph)
{
bool[] exclusion = new bool[_data.Count];
for (int i = 0; i < _data.Count; i++)
{
exclusion[i] = true;
}
foreach (var p in c.Points)
{
exclusion[p.Id] = false;
}
lwg = new LightWeightGraph((LightWeightGraph)_data, exclusion);
}
else //Distance matrix or Pointset
{
Debug.Assert(mat != null, "mat != null");
var subMatrix = mat.GetReducedDataSet(clusterSubset);
//Generate our graph
lwg = _graphGen.GenerateGraph(subMatrix.Mat);
}
subsetMap.Add(c.ClusterId, clusterSubset.ToArray());
lwg.IsWeighted = _weighted;
VAT v = new VAT(lwg, _reassignNodes, _alpha, _beta);
_vatNodeRemovalOrder = v.NodeRemovalOrder;
_vatNumNodesRemoved = v.NumNodesRemoved;
if (_hillClimb)
{
v.HillClimb();
}
////VATClust v = new VATClust(subMatrix.Mat, _weighted, _useKnn, _kNNOffset, _alpha, _beta);
vatMap.Add(c.ClusterId, v);
Console.WriteLine("Calculated Vat for cluster " + c.ClusterId);
}
meta.AppendLine("All calculated VATs:");
//Now find the minimum vat value
int minVatCluster = 0;
double minVatValue = double.MaxValue;
foreach (var c in vatMap)
{
meta.Append(String.Format("{0} ", c.Value.MinVat));
if (c.Value.MinVat < minVatValue)
{
minVatCluster = c.Key;
minVatValue = c.Value.MinVat;
}
}
meta.AppendLine();
//now merge the partition into the cluster
var minVAT = vatMap[minVatCluster];
var subPartition = minVAT.GetPartition();
var nodeIndexMap = subsetMap[minVatCluster];
meta.AppendFormat("Vat: MinVat={0}\r\n", minVAT.MinVat);
meta.AppendFormat("Removed Count:{0}\r\n", minVAT.NumNodesRemoved);
meta.AppendLine(String.Join(",",
minVAT.NodeRemovalOrder.GetRange(0, minVAT.NumNodesRemoved).Select(c => nodeIndexMap[c])));
partition.MergeSubPartition(subPartition, nodeIndexMap, minVatCluster);
vatMap.Remove(minVatCluster);
subsetMap.Remove(minVatCluster);
Console.WriteLine("Found min cluster");
Console.WriteLine(meta);
}
partition.MetaData = meta.ToString();
// The idea is now that we have partitions, combine them so that partition.Clusters.Count == minK
if (partition.Clusters.Count > _minK)
{
combineClusters(partition, _minK);
}
return(partition);
}
