//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);
}
