/// <summary>
/// Perform a single training iteration.
/// </summary>
///
public void Iteration()
{
// loop over all clusters
foreach (KMeansCluster element in _clusters)
{
for (int k = 0; k < element.Size(); k++)
{
IMLData data = element.Get(k);
double distance = CalculateEuclideanDistance(
element.Centroid, data);
KMeansCluster tempCluster = null;
bool match = false;
foreach (KMeansCluster cluster in _clusters)
{
double d = CalculateEuclideanDistance(cluster.Centroid,
element.Get(k));
if (distance > d)
{
distance = d;
tempCluster = cluster;
match = true;
}
}
if (match)
{
tempCluster.Add(element.Get(k));
element.Remove(element.Get(k));
foreach (KMeansCluster element2 in _clusters)
{
element2.Centroid.CalcCentroid();
}
CalcWcss();
}
}
}
}
/// <summary>
/// Construct the K-Means object.
/// </summary>
///
/// <param name="k">The number of clusters to use.</param>
/// <param name="theSet">The dataset to cluster.</param>
public KMeansClustering(int k, IMLDataSet theSet)
{
_clusters = new KMeansCluster[k];
for (int i = 0; i < k; i++)
{
_clusters[i] = new KMeansCluster();
}
_set = theSet;
SetInitialCentroids();
// break up the data over the clusters
int clusterNumber = 0;
foreach (IMLDataPair pair in _set)
{
_clusters[clusterNumber].Add(pair.Input);
clusterNumber++;
if (clusterNumber >= _clusters.Length)
{
clusterNumber = 0;
}
}
CalcWcss();
foreach (KMeansCluster element in _clusters)
{
element.Centroid.CalcCentroid();
}
CalcWcss();
}
/// <summary>
/// Construct the K-Means object.
/// </summary>
///
/// <param name="k">The number of clusters to use.</param>
/// <param name="theSet">The dataset to cluster.</param>
public KMeansClustering(int k, IMLDataSet theSet)
{
_clusters = new KMeansCluster[k];
for (int i = 0; i < k; i++)
{
_clusters[i] = new KMeansCluster();
}
_set = theSet;
SetInitialCentroids();
// break up the data over the clusters
int clusterNumber = 0;
foreach (IMLDataPair pair in _set)
{
_clusters[clusterNumber].Add(pair.Input);
clusterNumber++;
if (clusterNumber >= _clusters.Length)
{
clusterNumber = 0;
}
}
CalcWcss();
foreach (KMeansCluster element in _clusters)
{
element.Centroid.CalcCentroid();
}
CalcWcss();
}
private static double x92c8f50710486fbe(double x3bd62873fafa6252, KMeansCluster x4bbc2c453c470189)
{
return (x3bd62873fafa6252 + x4bbc2c453c470189.SumSqr);
}
