public static Heap.SubmodularHeap ReadSubmodularHeap(string filename)
{
System.IO.StreamReader srHeap = new System.IO.StreamReader(filename);
List <Graph.ClusteringEdge> data = new List <Graph.ClusteringEdge>();
while (srHeap.Peek() > 0)
{
string content = srHeap.ReadLine();
string[] items = content.Split(new char[] { ' ' });
Graph.ClusteringEdge ce = new Graph.ClusteringEdge(Convert.ToInt32(items[0]), Convert.ToInt32(items[1]), Convert.ToDouble(items[2]), Convert.ToInt32(items[3]), Convert.ToDouble(items[4]));
data.Add(ce);
}
srHeap.Close();
Heap.SubmodularHeap subHeap = new Heap.SubmodularHeap(data);
return(subHeap);
}
public static List <Graph.CompositeNode> Clustering(Graph.HyperGraph inGraph, double lambda, double gamma, double mu, int numClusters, string outputDirectory, out double entropy, out double purity, out double balance, out double diversity, out double objective)
{
#region initialize
//when begin, each cluster contains a single node
List <Graph.CompositeNode> clusters = new List <Graph.CompositeNode>();
foreach (var nodei in inGraph.Nodes)
{
foreach (var node in nodei)
{
List <int> tmp = new List <int>();
List <int> tmp2 = new List <int>();
List <int> tmp3 = new List <int>();
tmp.Add(node.NodeID);
tmp2.Add(node.Label);
tmp3.Add(node.Modality);
clusters.Add(new Graph.CompositeNode(tmp, tmp2, tmp3));
}
}
//for debugging
//CoupledDL.LazyGreedy.OutputClusterResult(clusters, inGraph, @"D:\Result\Temporary\Cluster\");
//calculate the self loop weight and the total of weight of each vertex
double[] loop = Function.CalculateLoopWeight(inGraph);
double totalWeight = Function.CalculateTotalWeight(loop);
Function.NormalizeWeight(ref loop, ref inGraph, totalWeight, 1);
//calculate the initial objective gain and decide the trade-off parameter
double[] hGain = new double[inGraph.NumEdges];
double[] pGain = new double[inGraph.NumEdges];
double[] lGain = new double[inGraph.NumEdges];
double[] mGain = new double[inGraph.NumEdges];
double maxHGain = 0, maxPGain = 0, maxLGain = 0, maxMGain = 0;
for (int i = 0; i < inGraph.NumEdges; i++)
{
Console.WriteLine(i + "th edge's gain is initialized.");
hGain[i] = Function.CalculateHGain(inGraph.Edges[i].Weight, loop[inGraph.Edges[i].StartID] - inGraph.Edges[i].Weight, loop[inGraph.Edges[i].EndID] - inGraph.Edges[i].Weight);
int clusterIndex1 = Function.FindIndex(inGraph, inGraph.Edges[i].StartID);
int clusterIndex2 = Function.FindIndex(inGraph, inGraph.Edges[i].EndID);
if (clusterIndex1 != clusterIndex2)
{
pGain[i] = Function.CalculatePGain(clusters, inGraph.NumLabelNodes, clusterIndex1, clusterIndex2);
lGain[i] = Function.CalculateLGain(clusters, inGraph.NumLabelNodes, inGraph.NumUnLabelNodes, clusterIndex1, clusterIndex2);
mGain[i] = Function.CalculateMGain(clusters, inGraph.NumEachModality, clusterIndex1, clusterIndex2);
}
if (hGain[i] > maxHGain)
{
maxHGain = hGain[i];
}
if (pGain[i] > maxPGain)
{
maxPGain = pGain[i];
}
if (lGain[i] > maxLGain)
{
maxLGain = lGain[i];
}
if (mGain[i] > maxMGain)
{
maxMGain = mGain[i];
}
}
double adjustedLambda = lambda * maxHGain / maxPGain;
double adjustedGamma = gamma * maxHGain / maxLGain;
double adjustedMu = mu * maxHGain / maxMGain;
List <Graph.ClusteringEdge> edges = new List <Graph.ClusteringEdge>();
for (int i = 0; i < inGraph.NumEdges; i++)
{
double gain = hGain[i] + adjustedLambda * pGain[i] + adjustedGamma * lGain[i];
edges.Add(new Graph.ClusteringEdge(inGraph.Edges[i].StartID, inGraph.Edges[i].EndID, inGraph.Edges[i].Weight, inGraph.Edges[i].Type, gain));
}
hGain = null; pGain = null; lGain = null; mGain = null;
#endregion
#region build the heap
Heap.SubmodularHeap heap = new Heap.SubmodularHeap(edges);
heap.CheckMaxHeap(); // for debugging
#endregion
#region greedily add the edge and perform the clustering
Graph.ClusteringEdge bestEdge;
int countCluster = inGraph.NumNodes;
List <double> addedEdges = new List <double>();
while (countCluster > numClusters)
{
if (heap.IsEmpty())
{
Console.WriteLine("Heap is empty!");
objective = Function.CalculateObjective(clusters, inGraph, addedEdges, loop, adjustedLambda, adjustedGamma, adjustedMu, out entropy, out purity, out balance, out diversity);
loop = null;
return(clusters);
}
Console.WriteLine(countCluster + "->" + numClusters + "clusters remained");
//find the best edge to add
bestEdge = heap.ExtractMax();
addedEdges.Add(bestEdge.Weight);
//merge the clusters which the best edge connects
int clusterID1 = Function.FindIndex(inGraph, bestEdge.StartID);
int clusterID2 = Function.FindIndex(inGraph, bestEdge.EndID);
if (clusterID1 != clusterID2)
{
int remove = Function.MergeTwoClusters(ref clusters, clusterID1, clusterID2);
int stay = clusterID1 == remove ? clusterID2 : clusterID1;
foreach (var c in clusters[stay].SingleNodes)
{
inGraph.FindNode(c).ClusterID = stay;
}
countCluster--;
for (int c = remove; c < countCluster; c++)
{
foreach (var n in clusters[c].SingleNodes)
{
inGraph.FindNode(n).ClusterID--;
}
}
loop[bestEdge.StartID] -= bestEdge.Weight;
loop[bestEdge.EndID] -= bestEdge.Weight;
}
heap.UpdateSubmodularHeap(clusters, inGraph, loop, inGraph.NumLabelNodes, inGraph.NumUnLabelNodes, adjustedLambda, adjustedGamma, adjustedMu);
}
objective = Function.CalculateObjective(clusters, inGraph, addedEdges, loop, adjustedLambda, adjustedGamma, adjustedMu, out entropy, out purity, out balance, out diversity);
//string clusterFilename = Path.Combine(outputDirectory, "cluster.txt");
//string heapFilename = Path.Combine(outputDirectory, "heap.txt");
//string paraFilename = Path.Combine(outputDirectory, "parameter.txt");
//Function.WriteClusters(clusters, clusterFilename);
//Function.WriteParameters(loop, totalWeight, adjustedLambda, adjustedGamma, paraFilename);
//heap.PrintHeap(heapFilename);
loop = null;
return(clusters);
#endregion
}