public override Structuring BuildStructuring()
{
try
{
if (Set == null)
{
throw new NullReferenceException();
}
int _current = 1;
int _max = IterationsCount * (Set.ElementsCount * 4);
if (IContainerProgressBar != null)
{
IContainerProgressBar.ResetProgressBar(1, _max, true);
IContainerProgressBar.UpdateProgressBar(1, "Running LAC algorithm...", true);
}
//////BORRAR////////////////////////////
//Verificar que todos los atributos sean de tipo Numerico
foreach (var attr in Set.Attributes.Values)
{
if (attr.AttributeType != AttributeType.Numeric)
{
throw new Exception("The LAC algorithm run only with numerics Sets.");
}
}
if (ClustersCount > Set.ElementsCount)
{
ClustersCount = Set.ElementsCount;
}
if (ClustersCount == 1)
{
Dictionary <string, Cluster> dic_clus = new Dictionary <string, Cluster>();
string name = "C-0";
List <Element> temp = new List <Element>();
for (int i = 0; i < Set.ElementsCount; i++)
{
temp.Add(Set[i]);
}
dic_clus.Add(name, new Cluster(name)
{
Elements = temp
});
if (IContainerProgressBar != null)
{
IContainerProgressBar.FinishProgressBar();
}
Structuring = new Partition()
{
Clusters = dic_clus, Proximity = Proximity
};
return(Structuring);
}
else
{
List <Cluster> clusters = new List <Cluster>();
List <List <double> > W = new List <List <double> >();
//Seleccionar los centroides aleatorios
Random r = new Random(Seed);
int index = 0;
#region K centroids y W
for (int i = Set.ElementsCount - 1, j = 0; i >= 0; i--, j++)
{
index = r.Next(0, i + 1);
List <Element> l = new List <Element>();
l.Add(Set[index]);
clusters.Add(new Cluster("C-" + j, l)
{
Centroid = Set[index]
});
List <double> wi = new List <double>();
for (int m = 0; m < Set.Attributes.ValuesCount; m++)
{
wi.Add(1.0 / Math.Sqrt(Set.Attributes.ValuesCount));
}
W.Add(wi);
//Element temp = Set[index];
//Set[index] = Set[i];
//Set[i] = temp;
Set.Swap(index, i);
if (clusters.Count == ClustersCount)
{
break;
}
}
#endregion
bool converged = false;
int numIterations = 0;
int[] clusterAssignments = new int[Set.ElementsCount];
while (!converged && numIterations < IterationsCount)
{
numIterations++;
converged = true;
//PASO 3::
//Asignar cada elemento a su cluster correspondiente(lo unico que se utiliza son los centroides)
//No tienen porque estar llenos los clusters
AssignElements(clusterAssignments, clusters, W, ref _current);
foreach (var item in clusters)
{
item.Elements.Clear();
}
//Lleno cada cluster segun la asignacion
for (int j = 0; j < clusterAssignments.Length; j++)
{
while (clusters.Count <= clusterAssignments[j])
{
clusters.Add(new Cluster("C-" + j));
}
clusters[clusterAssignments[j]].AddElement(Set[j]);
if (IContainerProgressBar != null)
{
IContainerProgressBar.UpdateProgressBar(_current++, "Running LAC algorithm...", false);
}
}
//PASO 4::
//Computar los nuevos pesos
for (int j = 0; j < W.Count; j++)
{
for (int i = 0; i < W[j].Count; i++)
{
double Xji = CalculateXji(i, clusters[j]);
double wji = CalculateWji(Xji, Set.Attributes.ValuesCount, clusters[j]);
W[j][i] = wji;
}
}
//PASO 5::
//Asignar nuevamente cada elemento a su cluster correspondiente
converged = AssignElements(clusterAssignments, clusters, W, ref _current);
foreach (var item in clusters)
{
item.Elements.Clear();
}
for (int j = 0; j < clusterAssignments.Length; j++)
{
while (clusters.Count <= clusterAssignments[j])
{
clusters.Add(new Cluster("C-" + j));
}
clusters[clusterAssignments[j]].AddElement(Set[j]);
if (IContainerProgressBar != null)
{
IContainerProgressBar.UpdateProgressBar(_current++, "Running LAC algorithm...", false);
}
}
//PASO 6::
//Computar los nuevos centroides
for (int i = 0; i < clusters.Count; i++)
{
clusters[i].UpdateCentroid();
}
}
//Crear Dictionary<string,Cluster> para poder construir la particion
Dictionary <string, Cluster> dic_clusters = new Dictionary <string, Cluster>();
for (int i = 0; i < clusters.Count; i++)
{
List <double> _ws = new List <double>();
clusters[i].Name = "C-" + i;
clusters[i].Weights = _ws;
dic_clusters.Add(clusters[i].Name, clusters[i]);
for (int j = 0; j < Set.Attributes.ValuesCount; j++)
{
_ws.Add((double)W[i][j]);
}
}
if (IContainerProgressBar != null)
{
IContainerProgressBar.FinishProgressBar();
}
Structuring = new WeightedPartition()
{
Clusters = dic_clusters, Proximity = Proximity
};
return(Structuring);
}
}
catch (Exception _ex)
{
if (IContainerProgressBar != null)
{
IContainerProgressBar.ShowError("Error occurred in LAC algorithm.\n" + _ex.Message);
}
return(null);
}
}
public override Structuring BuildStructuring()
{
try
{
if (Set == null)
throw new NullReferenceException();
int _current = 1;
int _max = IterationsCount * (Set.ElementsCount * 4);
if (IContainerProgressBar != null)
{
IContainerProgressBar.ResetProgressBar(1, _max, true);
IContainerProgressBar.UpdateProgressBar(1, "Running LAC algorithm...", true);
}
//////BORRAR////////////////////////////
//Verificar que todos los atributos sean de tipo Numerico
foreach (var attr in Set.Attributes.Values)
if (attr.AttributeType != AttributeType.Numeric)
throw new Exception("The LAC algorithm run only with numerics Sets.");
if (ClustersCount > Set.ElementsCount)
ClustersCount = Set.ElementsCount;
if (ClustersCount == 1)
{
Dictionary<string, Cluster> dic_clus = new Dictionary<string, Cluster>();
string name = "C-0";
List<Element> temp = new List<Element>();
for (int i = 0; i < Set.ElementsCount; i++)
temp.Add(Set[i]);
dic_clus.Add(name, new Cluster(name) { Elements = temp });
if (IContainerProgressBar != null)
IContainerProgressBar.FinishProgressBar();
Structuring = new Partition() { Clusters = dic_clus, Proximity = Proximity };
return Structuring;
}
else
{
List<Cluster> clusters = new List<Cluster>();
List<List<double>> W = new List<List<double>>();
//Seleccionar los centroides aleatorios
Random r = new Random(Seed);
int index = 0;
#region K centroids y W
for (int i = Set.ElementsCount - 1, j = 0; i >= 0; i--, j++)
{
index = r.Next(0, i + 1);
List<Element> l = new List<Element>();
l.Add(Set[index]);
clusters.Add(new Cluster("C-" + j, l) { Centroid = Set[index] });
List<double> wi = new List<double>();
for (int m = 0; m < Set.Attributes.ValuesCount; m++)
{
wi.Add(1.0 / Math.Sqrt(Set.Attributes.ValuesCount));
}
W.Add(wi);
//Element temp = Set[index];
//Set[index] = Set[i];
//Set[i] = temp;
Set.Swap(index, i);
if (clusters.Count == ClustersCount)
break;
}
#endregion
bool converged = false;
int numIterations = 0;
int[] clusterAssignments = new int[Set.ElementsCount];
while (!converged && numIterations < IterationsCount)
{
numIterations++;
converged = true;
//PASO 3::
//Asignar cada elemento a su cluster correspondiente(lo unico que se utiliza son los centroides)
//No tienen porque estar llenos los clusters
AssignElements(clusterAssignments, clusters, W, ref _current);
foreach (var item in clusters)
item.Elements.Clear();
//Lleno cada cluster segun la asignacion
for (int j = 0; j < clusterAssignments.Length; j++)
{
while (clusters.Count <= clusterAssignments[j])
clusters.Add(new Cluster("C-" + j));
clusters[clusterAssignments[j]].AddElement(Set[j]);
if (IContainerProgressBar != null)
IContainerProgressBar.UpdateProgressBar(_current++, "Running LAC algorithm...", false);
}
//PASO 4::
//Computar los nuevos pesos
for (int j = 0; j < W.Count; j++)
{
for (int i = 0; i < W[j].Count; i++)
{
double Xji = CalculateXji(i, clusters[j]);
double wji = CalculateWji(Xji, Set.Attributes.ValuesCount, clusters[j]);
W[j][i] = wji;
}
}
//PASO 5::
//Asignar nuevamente cada elemento a su cluster correspondiente
converged = AssignElements(clusterAssignments, clusters, W, ref _current);
foreach (var item in clusters)
item.Elements.Clear();
for (int j = 0; j < clusterAssignments.Length; j++)
{
while (clusters.Count <= clusterAssignments[j])
clusters.Add(new Cluster("C-" + j));
clusters[clusterAssignments[j]].AddElement(Set[j]);
if (IContainerProgressBar != null)
IContainerProgressBar.UpdateProgressBar(_current++, "Running LAC algorithm...", false);
}
//PASO 6::
//Computar los nuevos centroides
for (int i = 0; i < clusters.Count; i++)
clusters[i].UpdateCentroid();
}
//Crear Dictionary<string,Cluster> para poder construir la particion
Dictionary<string, Cluster> dic_clusters = new Dictionary<string, Cluster>();
for (int i = 0; i < clusters.Count; i++)
{
List<double> _ws = new List<double>();
clusters[i].Name = "C-" + i;
clusters[i].Weights = _ws;
dic_clusters.Add(clusters[i].Name, clusters[i]);
for (int j = 0; j < Set.Attributes.ValuesCount; j++)
_ws.Add((double)W[i][j]);
}
if (IContainerProgressBar != null)
IContainerProgressBar.FinishProgressBar();
Structuring = new WeightedPartition() { Clusters = dic_clusters, Proximity = Proximity };
return Structuring;
}
}
catch (Exception _ex)
{
if (IContainerProgressBar != null)
IContainerProgressBar.ShowError("Error occurred in LAC algorithm.\n" + _ex.Message);
return null;
}
}
