public void Calculate()
{
double iclusteringCoefficient = 0;
int iEdgeCountForFullness = 0, iNeighbourCount = 0;
for (int i = 0; i < container.Size; ++i)
{
iNeighbourCount = container.GetVertexDegree(i);
if (iNeighbourCount != 0)
{
iEdgeCountForFullness = (iNeighbourCount == 1) ? 1 : iNeighbourCount * (iNeighbourCount - 1) / 2;
iclusteringCoefficient = (edgesBetweenNeighbours[i]) / iEdgeCountForFullness;
CoefficientsPerVertex.Add(i, Math.Round(iclusteringCoefficient, 3));
}
else
{
CoefficientsPerVertex.Add(i, 0);
}
}
for (int i = 0; i < container.Size; ++i)
{
double result = CoefficientsPerVertex[(uint)i];
if (CoefficientsDistribution.Keys.Contains(result))
{
CoefficientsDistribution[CoefficientsPerVertex[i]]++;
}
else
{
CoefficientsDistribution[CoefficientsPerVertex[i]] = 1;
}
}
}
private Double[] CalculateProbabilities()
{
Double[] result = new Double[container.Size];
Double graphDegree = (Double)container.GetVertexDegreeSum();
if (graphDegree != 0)
{
for (Int32 i = 0; i < result.Length; ++i)
{
result[i] = (Double)container.GetVertexDegree(i) / graphDegree;
}
}
else
{
for (Int32 i = 0; i < result.Length; ++i)
{
result[i] = 1.0 / result.Length;
}
}
return(result);
}
private int get_vertex_to_connect(int current_vertex, List <double> fitnesses_, DistributedRandom distributed_random_)
{
double sum = 0;
for (int i = 0; i < current_vertex; ++i)
{
sum += (container.GetVertexDegree(i) * fitnesses_[i]);
}
double probabilty = distributed_random_.get_probability() * sum;
sum = 0;
for (int i = 0; i < current_vertex; ++i)
{
sum += (container.GetVertexDegree(i) * fitnesses_[i]);
if (probabilty <= sum)
{
return(i);
}
}
return(-1);
}
