private void Seperation(Centroid centroid)
{
// filter out the cluster which has customers with identical id as that of the centroid which we want to calculate the distance with.
List <Centroid> tempCentroidList = (KmeansCentroids.Where(c => c.Id != centroid.Id)).ToList();
for (int i = 0; i < tempCentroidList.Count; i++)
{
for (int customer = 0; customer < centroid.cluster.Count; customer++)
{
double sumdistance = 0; // accumulator
for (int other_customer = 0; other_customer < tempCentroidList[i].cluster.Count; other_customer++)
{
if (tempCentroidList[i].cluster[other_customer].CustomerId < centroid.cluster[customer].CustomerId)
{ // if the value cant't be found in the matrix because of the a> b & b > a implementation, flip the customer and the other customer to find th distance value.
sumdistance += Distance_Matrix[tempCentroidList[i].cluster[other_customer].CustomerId] [centroid.cluster[customer].CustomerId];
}
else
{
sumdistance += Distance_Matrix[centroid.cluster[customer].CustomerId] [tempCentroidList[i].cluster[other_customer].CustomerId];
}
}
// add the accumulated results to the list of neighbours of the customer
centroid.cluster[customer].AddNeighbhour(new Tuple <int, double>(tempCentroidList[i].Id, sumdistance));
}
}
}
//function that returns the average of the sillouette
private void SilhouetteCalculation(List <Centroid> kmeansCentroids)
{
var listCohesianSeperation = new List <Tuple <double, double> >();
//foreach centroid calculate the cohesion and seperation and appendthe results to a list
kmeansCentroids.ForEach(cluster => {
Cohension(cluster);
Seperation(cluster);
});
// calculate the sillhouette for each customer
kmeansCentroids.ForEach(x => x.cluster.ForEach(cust => cust.calculateSilhouette()));
// the sillhouettes of every customer are sbeing summed and divided by the amount of customers to obtain the average Silhouette.
var sumSilouettte = KmeansCentroids.Select(cluster => cluster.cluster.Select(cust => cust.customerSillhouette).Sum()).Sum();
AGVSillouette = sumSilouettte / Data.Count();
}