public double ClusterDistanceAverage(Cluster oper, IDissimilarity diss)
{
double sum = 0;
int cant = 0;
foreach (Element element in oper.Elements)
{
foreach (Element operElement in this.Elements)
{
sum += diss.CalculateDissimilarity(element, operElement);
cant++;
}
}
return((double)sum / (double)cant);
}
/// <summary>
/// Dissimilarity
/// </summary>
public static double Dissimilarity(this IDissimilarity rules, IEnumerable <int> left, IEnumerable <int> right)
{
if (rules is null)
{
throw new ArgumentNullException(nameof(rules));
}
else if (left is null)
{
throw new ArgumentNullException(nameof(left));
}
else if (right is null)
{
throw new ArgumentNullException(nameof(right));
}
return(rules.Dissimilarity(left.Select(x => x != 0), right.Select(x => x != 0)));
}
public double ClusterDistanceComplete(Cluster oper, IDissimilarity diss)
{
double max = double.MinValue;
for (int i = 0; i < Elements.Count; i++)
{
for (int j = 0; j < oper.Elements.Count; j++)
{
double dissValue = diss.CalculateDissimilarity(Elements[i], oper.Elements[j]);
if (dissValue > max)
{
max = dissValue;
}
}
}
return(max);
}
public double ClusterDistanceSingle(Cluster oper, IDissimilarity diss)
{
double min = double.MaxValue;
for (int i = 0; i < Elements.Count; i++)
{
for (int j = 0; j < oper.Elements.Count; j++)
{
double dissValue = diss.CalculateDissimilarity(Elements[i], oper.Elements[j]);
if (dissValue < min)
{
min = dissValue;
}
}
}
return(min);
}
public List <Tupla <Cluster> > PerfectMating(Partition partition, IDissimilarity diss)
{
double[,] distances = new double[Clusters.Count, partition.Clusters.Count];
for (int i = 0; i < Clusters.Count; i++)
{
for (int j = 0; j < partition.Clusters.Count; j++)
{
distances[i, j] = Clusters.Values.ToList <Cluster>()[i].ClusterDistanceAverage(partition.Clusters.Values.ToList <Cluster>()[j], diss);
}
}
List <Tupla <int> > bestsolution = new List <Tupla <int> >();
// SearchAlgoritmNonInformedBPP(0, Clusters.Count, new bool[partition.Clusters.Count], new List<Tupla<Cluster>>(), bestsolution,this,partition);
SearchAlgoritmInformedSimulatedAnnealing(100, 0.5, 10, 5, ref bestsolution, distances);
List <Tupla <Cluster> > result = new List <Tupla <Cluster> >();
foreach (var tupla in bestsolution)
{
result.Add(new Tupla <Cluster>(Clusters.Values.ToList <Cluster>()[tupla.I], partition.Clusters.Values.ToList <Cluster>()[tupla.J]));
}
return(result);
}
public AgglomerativeLinks(Set set, IDissimilarity diss, CalculateClusterDistance CalculateClusterDistance)
: base(set, diss)
{
this.CalculateClusterDistance = CalculateClusterDistance;
}
