public ClusteringResult Cluster(IUnlabeledExampleCollection <SparseVector <double> > dataset)
{
Utils.ThrowException(dataset == null ? new ArgumentNullException("dataset") : null);
Utils.ThrowException(dataset.Count < NumLeaves ? new ArgumentValueException("dataset") : null);
ClusteringResult clusters = mKMeansClustering.Cluster(dataset);
UnlabeledDataset <SparseVector <double> > centroids = new UnlabeledDataset <SparseVector <double> >();
foreach (Cluster cluster in clusters.Roots)
{
SparseVector <double> centroid = ModelUtils.ComputeCentroid(cluster.Items, dataset, CentroidType.NrmL2);
centroids.Add(centroid);
centroid = Trim(centroid, 1000, 0.8);
cluster.ClusterInfo = 1; // cluster level
}
SparseMatrix <double> simMtx = ModelUtils.GetDotProductSimilarity(centroids, /*thresh=*/ 0, /*fullMatrix=*/ false);
SparseMatrix <double> clustMtxTr = ModelUtils.GetTransposedMatrix(centroids);
int iter = 1;
while (clusters.Roots.Count > 1)
{
Console.WriteLine("Iteration {0} ...", iter++);
int idx1, idx2;
FindMaxSim(simMtx, out idx1, out idx2);
Update(simMtx, clustMtxTr, clusters.Roots.Count, idx1, idx2, clusters.Roots.Inner, dataset, /*damping=*/ 0.9);
Console.WriteLine(simMtx.ToString("E0.00"));
Console.WriteLine();
}
return(clusters);
}
private UnlabeledDataset <SparseVector <double> > GetDatasetSubset(IEnumerable <int> items, IUnlabeledExampleCollection <SparseVector <double> > dataset)
{
UnlabeledDataset <SparseVector <double> > datasetSubset = new UnlabeledDataset <SparseVector <double> >();
foreach (int item in items)
{
datasetSubset.Add(dataset[item]);
}
return(datasetSubset);
}
public static IUnlabeledExampleCollection <ExT> ConvertToUnlabeledDataset <LblT, ExT>(ILabeledExampleCollection <LblT, ExT> dataset)
{
UnlabeledDataset <ExT> unlabeledDataset = new UnlabeledDataset <ExT>();
foreach (LabeledExample <LblT, ExT> labeledExample in dataset)
{
unlabeledDataset.Add(labeledExample.Example);
}
return(unlabeledDataset);
}
public static UnlabeledDataset <ExT> ConvertToUnlabeledDataset <LblT, ExT>(ILabeledExampleCollection <LblT, ExT> dataset)
{
Utils.ThrowException(dataset == null ? new ArgumentNullException("dataset") : null);
UnlabeledDataset <ExT> unlabeledDataset = new UnlabeledDataset <ExT>();
foreach (LabeledExample <LblT, ExT> labeledExample in dataset)
{
unlabeledDataset.Add(labeledExample.Example);
}
return(unlabeledDataset);
}
public void Load(BinarySerializer reader)
{
Utils.ThrowException(reader == null ? new ArgumentNullException("reader") : null);
// the following statements throw serialization-related exceptions
mRnd = (Random)reader.ReadDotNetObject();
mEps = reader.ReadDouble();
mTrials = reader.ReadInt();
mK = reader.ReadInt();
mCentroids = reader.ReadObject <ArrayList <CentroidData> >();
mDataset = reader.ReadObject <UnlabeledDataset <SparseVector <double> > >();
mQualThresh = reader.ReadDouble();
mTopicId = reader.ReadLong();
}
public IUnlabeledDataset ConvertDataset(Type newExType, bool move)
{
Utils.ThrowException(newExType == null ? new ArgumentNullException("newExType") : null);
Utils.ThrowException(move && typeof(ExT).IsValueType ? new ArgumentValueException("newExType") : null);
IUnlabeledDataset newDataset = null;
ArrayList <object> tmp = new ArrayList <object>(mItems.Count);
for (int i = 0; i < mItems.Count; i++)
{
tmp.Add(ModelUtils.ConvertExample(mItems[i], newExType)); // throws ArgumentValueException
if (move)
{
mItems[i] = default(ExT);
} // *** this is guaranteed to be null by the second assertion
}
if (move)
{
mItems.Clear();
}
if (newExType == typeof(SparseVector <double>))
{
newDataset = new UnlabeledDataset <SparseVector <double> >(tmp);
}
else if (newExType == typeof(SparseVector <double> .ReadOnly))
{
newDataset = new UnlabeledDataset <SparseVector <double> .ReadOnly>(tmp);
}
else if (newExType == typeof(BinaryVector))
{
newDataset = new UnlabeledDataset <BinaryVector>(tmp);
}
else if (newExType == typeof(BinaryVector.ReadOnly))
{
newDataset = new UnlabeledDataset <BinaryVector.ReadOnly>(tmp);
}
else
{
throw new ArgumentNotSupportedException("newExType");
}
return(newDataset);
}
public ClusteringResult Cluster(IUnlabeledExampleCollection <SparseVector <double> > dataset)
{
Utils.ThrowException(dataset == null ? new ArgumentNullException("dataset") : null);
Utils.ThrowException(dataset.Count < 2 ? new ArgumentValueException("dataset") : null);
ClusteringResult clusteringResult = new ClusteringResult();
Queue <Cluster> queue = new Queue <Cluster>();
// create root
Cluster root = new Cluster();
for (int i = 0; i < dataset.Count; i++)
{
Utils.ThrowException(dataset[i].Count == 0 ? new ArgumentValueException("dataset") : null);
root.Items.Add(i);
}
clusteringResult.AddRoot(root);
// add root to queue
queue.Enqueue(root);
while (queue.Count > 0)
{
// get next cluster
Cluster cluster = queue.Dequeue();
// compute cluster quality
UnlabeledDataset <SparseVector <double> > localDataset = GetDatasetSubset(cluster.Items, dataset);
SparseVector <double> centroid;
double quality = GetClusterQuality(localDataset, out centroid);
cluster.ClusterInfo = new Pair <SparseVector <double>, double>(centroid, quality);
if (quality < mMinQuality)
{
// split cluster, add children to queue
ClusteringResult localResult = mKMeansClustering.Cluster(localDataset);
for (int i = 0; i < 2; i++)
{
cluster.AddChild(localResult.Roots[i]);
localResult.Roots[i].Parent = cluster;
queue.Enqueue(localResult.Roots[i]);
}
}
}
return(clusteringResult);
}
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());
}
