public ClusteringResult Cluster(IUnlabeledExampleCollection <SparseVector <double> > dataset)
{
Utils.ThrowException(dataset == null ? new ArgumentNullException("dataset") : null);
Utils.ThrowException(dataset.Count < mK ? new ArgumentValueException("dataset") : null);
mDataset = new UnlabeledDataset <SparseVector <double> >(dataset);
ClusteringResult clustering = null;
double globalBestClustQual = 0;
for (int trial = 1; trial <= mTrials; trial++)
{
mLogger.Info("Cluster", "Clustering trial {0} of {1} ...", trial, mTrials);
ArrayList <CentroidData> centroids = new ArrayList <CentroidData>(mK);
ArrayList <int> bestSeeds = null;
for (int i = 0; i < mK; i++)
{
centroids.Add(new CentroidData());
}
// select seed items
double minSim = double.MaxValue;
ArrayList <int> tmp = new ArrayList <int>(mDataset.Count);
for (int i = 0; i < mDataset.Count; i++)
{
tmp.Add(i);
}
for (int k = 0; k < 3; k++)
{
ArrayList <SparseVector <double> > seeds = new ArrayList <SparseVector <double> >(mK);
tmp.Shuffle(mRnd);
for (int i = 0; i < mK; i++)
{
seeds.Add(mDataset[tmp[i]]);
}
// assess quality of seed items
double simAvg = 0;
foreach (SparseVector <double> seed1 in seeds)
{
foreach (SparseVector <double> seed2 in seeds)
{
if (seed1 != seed2)
{
simAvg += DotProductSimilarity.Instance.GetSimilarity(seed1, seed2);
}
}
}
simAvg /= (double)(mK * mK - mK);
//Console.WriteLine(simAvg);
if (simAvg < minSim)
{
minSim = simAvg;
bestSeeds = new ArrayList <int>(mK);
for (int i = 0; i < mK; i++)
{
bestSeeds.Add(tmp[i]);
}
}
}
ArrayList <KeyDat <double, int> > medoids = new ArrayList <KeyDat <double, int> >(mK);
for (int i = 0; i < mK; i++)
{
centroids[i].Items.Add(bestSeeds[i]);
centroids[i].Update(mDataset);
centroids[i].UpdateCentroidLen();
medoids.Add(new KeyDat <double, int>(-1, bestSeeds[i]));
}
double[,] dotProd = new double[mDataset.Count, mK];
SparseMatrix <double> dsMat = ModelUtils.GetTransposedMatrix(mDataset);
// main loop
int iter = 0;
double bestClustQual = 0;
double clustQual;
while (true)
{
iter++;
mLogger.Info("Cluster", "Iteration {0} ...", iter);
clustQual = 0;
// assign items to clusters
//StopWatch stopWatch = new StopWatch();
int j = 0;
foreach (CentroidData cen in centroids)
{
SparseVector <double> cenVec = cen.GetSparseVector();
double[] dotProdSimVec = ModelUtils.GetDotProductSimilarity(dsMat, mDataset.Count, cenVec);
for (int i = 0; i < dotProdSimVec.Length; i++)
{
if (dotProdSimVec[i] > 0)
{
dotProd[i, j] = dotProdSimVec[i];
}
}
j++;
}
for (int dsInstIdx = 0; dsInstIdx < mDataset.Count; dsInstIdx++)
{
double maxSim = double.MinValue;
ArrayList <int> candidates = new ArrayList <int>();
for (int cenIdx = 0; cenIdx < mK; cenIdx++)
{
double sim = dotProd[dsInstIdx, cenIdx];
if (sim > maxSim)
{
maxSim = sim;
candidates.Clear();
candidates.Add(cenIdx);
}
else if (sim == maxSim)
{
candidates.Add(cenIdx);
}
}
if (candidates.Count > 1)
{
candidates.Shuffle(mRnd);
}
if (candidates.Count > 0) // *** is this always true?
{
centroids[candidates[0]].Items.Add(dsInstIdx);
clustQual += maxSim;
if (medoids[candidates[0]].Key < maxSim)
{
medoids[candidates[0]] = new KeyDat <double, int>(maxSim, dsInstIdx);
}
}
}
//Console.WriteLine(stopWatch.TotalMilliseconds);
clustQual /= (double)mDataset.Count;
mLogger.Info("Cluster", "Quality: {0:0.0000}", clustQual);
// compute new centroids
for (int i = 0; i < mK; i++)
{
centroids[i].Update(mDataset);
centroids[i].UpdateCentroidLen();
}
// check if done
if (iter > 1 && clustQual - bestClustQual <= mEps)
{
break;
}
bestClustQual = clustQual;
for (int i = 0; i < medoids.Count; i++)
{
medoids[i] = new KeyDat <double, int>(-1, medoids[i].Dat);
}
}
if (trial == 1 || clustQual > globalBestClustQual)
{
globalBestClustQual = clustQual;
mCentroids = centroids;
mMedoids = medoids;
// save the result
clustering = new ClusteringResult();
for (int i = 0; i < mK; i++)
{
clustering.AddRoot(new Cluster());
clustering.Roots.Last.Items.AddRange(centroids[i].Items);
}
}
}
return(clustering);
}
//private double GetQual()
//{
// double clustQual = 0;
// foreach (Centroid centroid in mCentroids)
// {
// foreach (int itemIdx in centroid.CurrentItems)
// {
// clustQual += centroid.GetDotProduct(mDataset[itemIdx]);
// }
// }
// clustQual /= (double)mDataset.Count;
// return clustQual;
//}
// TODO: exceptions
public ClusteringResult Update(int dequeueN, IEnumerable <SparseVector <double> > addList, ref int iter)
{
StopWatch stopWatch = new StopWatch();
// update centroid data (1)
foreach (CentroidData centroid in mCentroids)
{
foreach (int item in centroid.CurrentItems)
{
if (item >= dequeueN)
{
centroid.Items.Add(item);
}
}
centroid.Update(mDataset);
centroid.UpdateCentroidLen();
}
//Console.WriteLine(">>> {0} >>> update centroid data (1)", stopWatch.TotalMilliseconds);
stopWatch.Reset();
// update dataset
mDataset.RemoveRange(0, dequeueN);
int ofs = mDataset.Count;
mDataset.AddRange(addList);
//Console.WriteLine(">>> {0} >>> update dataset", stopWatch.TotalMilliseconds);
stopWatch.Reset();
// update centroid data (2)
foreach (CentroidData centroid in mCentroids)
{
Set <int> itemsOfs = new Set <int>();
foreach (int item in centroid.CurrentItems)
{
itemsOfs.Add(item - dequeueN);
}
centroid.CurrentItems.Inner.SetItems(itemsOfs);
centroid.Items.SetItems(itemsOfs);
}
//Console.WriteLine(">>> {0} >>> update centroid data (2)", stopWatch.TotalMilliseconds);
stopWatch.Reset();
// assign new instances
double bestClustQual = 0;
{
mLogger.Info("Update", "Initializing ...");
int i = 0;
foreach (SparseVector <double> example in addList)
{
double maxSim = double.MinValue;
ArrayList <int> candidates = new ArrayList <int>();
for (int j = 0; j < mK; j++)
{
double sim = mCentroids[j].GetDotProduct(example);
if (sim > maxSim)
{
maxSim = sim;
candidates.Clear();
candidates.Add(j);
}
else if (sim == maxSim)
{
candidates.Add(j);
}
}
if (candidates.Count > 1)
{
candidates.Shuffle(mRnd);
}
if (candidates.Count > 0) // *** is this always true?
{
mCentroids[candidates[0]].Items.Add(ofs + i);
}
i++;
}
// update centroids
foreach (CentroidData centroid in mCentroids)
{
centroid.Update(mDataset);
centroid.UpdateCentroidLen();
}
//Console.WriteLine(GetQual());
foreach (CentroidData centroid in mCentroids)
{
foreach (int itemIdx in centroid.CurrentItems)
{
bestClustQual += centroid.GetDotProduct(mDataset[itemIdx]);
}
}
bestClustQual /= (double)mDataset.Count;
mLogger.Info("Update", "Quality: {0:0.0000}", bestClustQual);
}
//Console.WriteLine(">>> {0} >>> assign new instances", stopWatch.TotalMilliseconds);
stopWatch.Reset();
// main k-means loop
iter = 0;
while (true)
{
iter++;
mLogger.Info("Update", "Iteration {0} ...", iter);
// assign items to clusters
for (int i = 0; i < mDataset.Count; i++)
{
SparseVector <double> example = mDataset[i];
double maxSim = double.MinValue;
ArrayList <int> candidates = new ArrayList <int>();
for (int j = 0; j < mK; j++)
{
double sim = mCentroids[j].GetDotProduct(example);
if (sim > maxSim)
{
maxSim = sim;
candidates.Clear();
candidates.Add(j);
}
else if (sim == maxSim)
{
candidates.Add(j);
}
}
if (candidates.Count > 1)
{
candidates.Shuffle(mRnd);
}
if (candidates.Count > 0) // *** is this always true?
{
mCentroids[candidates[0]].Items.Add(i);
}
}
//
// *** OPTIMIZE THIS with GetDotProductSimilarity (see this.Cluster) !!! ***
//
//Console.WriteLine(">>> {0} >>> loop: assign items to clusters", stopWatch.TotalMilliseconds);
stopWatch.Reset();
double clustQual = 0;
// update centroids
foreach (CentroidData centroid in mCentroids)
{
centroid.Update(mDataset);
centroid.UpdateCentroidLen();
}
//Console.WriteLine(GetQual());
foreach (CentroidData centroid in mCentroids)
{
foreach (int itemIdx in centroid.CurrentItems)
{
clustQual += centroid.GetDotProduct(mDataset[itemIdx]);
}
}
clustQual /= (double)mDataset.Count;
//Console.WriteLine(">>> {0} >>> loop: update centroids", stopWatch.TotalMilliseconds);
stopWatch.Reset();
mLogger.Info("Update", "Quality: {0:0.0000} Diff: {1:0.0000}", clustQual, clustQual - bestClustQual);
// check if done
if (clustQual - bestClustQual <= mEps)
{
break;
}
bestClustQual = clustQual;
}
// save the result
ClusteringResult clustering = new ClusteringResult();
for (int i = 0; i < mK; i++)
{
clustering.AddRoot(new Cluster());
clustering.Roots.Last.Items.AddRange(mCentroids[i].Items);
}
return(clustering);
}
