private static bool UpdateClustering(double[][] data, int[] clustering, double[][] means)
{
int numClusters = means.Length;
bool changed = false;
int[] newClustering = new int[clustering.Length];
Array.Copy(clustering, newClustering, clustering.Length);
double[] distances = new double[numClusters];
for (int i = 0; i < data.Length; ++i)
{
for (int k = 0; k < numClusters; ++k)
{
distances[k] = HelpersCompute.Distance(data[i], means[k]);
}
int newClusterID = HelpersCompute.MinIndex(distances);
if (newClusterID != newClustering[i])
{
changed = true;
newClustering[i] = newClusterID;
}
}
if (changed == false)
{
return(false);
}
int[] clusterCounts = new int[numClusters];
for (int i = 0; i < data.Length; ++i)
{
int cluster = newClustering[i];
++clusterCounts[cluster];
}
for (int k = 0; k < numClusters; ++k)
{
if (clusterCounts[k] == 0)
{
return(false);
}
}
Array.Copy(newClustering, clustering, newClustering.Length);
return(true);
}
public static int[] ClusterMethod(double[][] rawData, int numClusters)
{
double[][] data = HelpersCompute.Normalize(rawData);
//double[][] data = rawData;
bool changed = true;
bool success = true;
int[] clustering = HelpersCompute.InitClustering(data.Length, numClusters, 0);
double[][] means = HelpersCompute.Allocate(numClusters, data[0].Length);
int maxCount = data.Length * 100;
int ct = 0;
while (changed == true && success == true && ct < maxCount)
{
++ct;
success = HelpersCompute.UpdateMeans(data, clustering, means);
changed = UpdateClustering(data, clustering, means);
}
return(clustering);
}
