private ClusteringResult CreateSingleCluster(IUnlabeledExampleCollection <SparseVector <double> > dataset)
{
ClusteringResult clustering = new ClusteringResult();
Cluster root = new Cluster();
for (int i = 0; i < dataset.Count; i++)
{
root.Items.Add(i);
}
clustering.AddRoot(root);
CentroidData centroid = new CentroidData();
centroid.Items.AddRange(root.Items);
centroid.Update(dataset);
centroid.UpdateCentroidLen();
mCentroids = new ArrayList <CentroidData>();
mCentroids.Add(centroid);
return(clustering);
}
public ClusteringResult Cluster(int numOutdated, IUnlabeledExampleCollection <SparseVector <double> > batch)
{
Utils.ThrowException(batch == null ? new ArgumentNullException("batch") : null);
Utils.ThrowException(numOutdated < 0 ? new ArgumentOutOfRangeException("numOutdated") : null);
if (mDataset == null)
{
// initialize
mLogger.Trace("Cluster", "Initializing ...");
Utils.ThrowException(numOutdated > 0 ? new ArgumentOutOfRangeException("numOutdated") : null);
//Utils.ThrowException(batch.Count == 0 ? new ArgumentValueException("batch") : null);
if (batch.Count == 0)
{
return(new ClusteringResult());
}
kMeans(batch, Math.Min(mK, batch.Count));
mDataset = new UnlabeledDataset <SparseVector <double> >(batch);
foreach (CentroidData centroid in mCentroids)
{
centroid.Tag = mTopicId++;
}
//OutputState();
}
else
{
// update clusters
Utils.ThrowException(numOutdated > mDataset.Count ? new ArgumentOutOfRangeException("numOutdated") : null);
if (numOutdated == 0 && batch.Count == 0)
{
return(GetClusteringResult());
}
mLogger.Trace("Cluster", "Updating clusters ...");
// assign new instances
double dummy;
Assign(mCentroids, ModelUtils.GetTransposedMatrix(batch), batch.Count, /*offs=*/ mDataset.Count, out dummy);
mDataset.AddRange(batch);
// remove outdated instances
foreach (CentroidData centroid in mCentroids)
{
foreach (int item in centroid.CurrentItems)
{
if (item >= numOutdated)
{
centroid.Items.Add(item);
}
}
centroid.Update(mDataset);
centroid.UpdateCentroidLen();
}
mDataset.RemoveRange(0, numOutdated);
ArrayList <CentroidData> centroidsNew = new ArrayList <CentroidData>(mCentroids.Count);
foreach (CentroidData centroid in mCentroids)
{
if (centroid.CurrentItems.Count > 0)
{
centroidsNew.Add(centroid);
Set <int> tmp = new Set <int>();
foreach (int idx in centroid.CurrentItems)
{
tmp.Add(idx - numOutdated);
}
centroid.CurrentItems.Inner.SetItems(tmp);
}
}
if (centroidsNew.Count == 0) // reset
{
mCentroids = null;
mDataset = null;
return(new ClusteringResult());
}
mCentroids = centroidsNew;
// execute main loop
kMeansMainLoop(mDataset, mCentroids);
//OutputState();
}
// adjust k
double minQual; // *** not used at the moment
int minQualIdx;
double qual = GetClustQual(out minQual, out minQualIdx);
if (qual < mQualThresh)
{
while (qual < mQualThresh) // split cluster at minQualIdx
{
mLogger.Trace("Cluster", "Increasing k to {0} ...", mCentroids.Count + 1);
mCentroids.Add(mCentroids[minQualIdx].Clone());
mCentroids.Last.Tag = mTopicId++;
kMeansMainLoop(mDataset, mCentroids);
if (mCentroids.Last.CurrentItems.Count > mCentroids[minQualIdx].CurrentItems.Count)
{
// swap topic identifiers
object tmp = mCentroids.Last.Tag;
mCentroids.Last.Tag = mCentroids[minQualIdx].Tag;
mCentroids[minQualIdx].Tag = tmp;
}
qual = GetClustQual(out minQual, out minQualIdx);
//OutputState();
}
}
else if (numOutdated > 0)
{
while (qual > mQualThresh && mCentroids.Count > 1) // join clusters
{
mLogger.Trace("Cluster", "Decreasing k to {0} ...", mCentroids.Count - 1);
ArrayList <CentroidData> centroidsCopy = mCentroids.DeepClone();
if (mCentroids.Count == 2) // create single cluster
{
object topicId = mCentroids[0].CurrentItems.Count > mCentroids[1].CurrentItems.Count ? mCentroids[0].Tag : mCentroids[1].Tag;
mCentroids = new ArrayList <CentroidData>();
mCentroids.Add(new CentroidData());
for (int i = 0; i < mDataset.Count; i++)
{
mCentroids.Last.Items.Add(i);
}
mCentroids.Last.Tag = topicId;
mCentroids.Last.Update(mDataset);
mCentroids.Last.UpdateCentroidLen();
}
else
{
int idx1, idx2;
GetMostSimilarClusters(out idx1, out idx2);
CentroidData c1 = mCentroids[idx1];
CentroidData c2 = mCentroids[idx2];
object topicId = c1.CurrentItems.Count > c2.CurrentItems.Count ? c1.Tag : c2.Tag;
mCentroids.RemoveAt(idx2);
c1.Items.AddRange(c1.CurrentItems);
c1.Items.AddRange(c2.CurrentItems);
c1.Tag = topicId;
c1.Update(mDataset);
c1.UpdateCentroidLen();
kMeansMainLoop(mDataset, mCentroids);
}
qual = GetClustQual();
if (qual >= mQualThresh)
{
mLogger.Trace("Cluster", "Accepted solution at k = {0}.", mCentroids.Count);
}
else
{
mCentroids = centroidsCopy;
}
//OutputState();
}
}
OutputState();
return(GetClusteringResult());
}