// TODO: exceptions
public Vector2D[] Update(int numDequeue, IEnumerable <SparseVector <double> > newInst, bool test, LayoutSettings settings, ref PtInfo[] ptInfo, int _count)
{
// clustering
mLogger.Info("Update", "Clustering ...");
/*prof*/ StopWatch sw = new StopWatch();
mKMeans.Eps = mKMeansEps;
int iter = 0;
mKMeans.Update(numDequeue, newInst, ref iter);
/*prof*/ sw.Save("cl.txt", _count, iter.ToString());
// determine reference instances
/*prof*/ sw.Reset();
UnlabeledDataset <SparseVector <double> > dsRefInst = new UnlabeledDataset <SparseVector <double> >();
UnlabeledDataset <SparseVector <double> > dsNewInst = new UnlabeledDataset <SparseVector <double> >(newInst);
foreach (SparseVector <double> centroid in mKMeans.GetCentroids())
{
dsRefInst.Add(centroid); // dataset of reference instances
dsNewInst.Add(centroid); // dataset of new instances
}
// position reference instances
mLogger.Info("Update", "Positioning reference instances ...");
SparseMatrix <double> simMtx = ModelUtils.GetDotProductSimilarity(dsRefInst, mSimThresh, /*fullMatrix=*/ false);
StressMajorizationLayout sm = new StressMajorizationLayout(dsRefInst.Count, new DistFunc(simMtx));
sm.Random = mRandom;
sm.MaxSteps = int.MaxValue;
sm.MinDiff = 1E-3;
mRefPos = sm.ComputeLayout(/*settings=*/ null, mRefPos /*make this a property!!!*/);
/*prof*/ sw.Save("sm.txt", _count);
// k-NN
/*prof*/ sw.Reset();
DateTime t = DateTime.Now;
mLogger.Info("Update", "Computing similarities ...");
// update list of neighborhoods
mPatches.RemoveRange(mDataset.Count - mKClust, mKClust);
mPatches.RemoveRange(0, numDequeue);
// remove instances from [dataset and] neighborhoods
foreach (Patch patch in mPatches)
{
if (patch.Min != null && (patch.Min.Idx < numDequeue || patch.Max.Idx >= mDataset.Count - mKClust))
{
int oldCount = patch.List.Count;
ArrayList <KeyDat <double, Patch> > tmp = new ArrayList <KeyDat <double, Patch> >();
foreach (KeyDat <double, Patch> item in patch.List)
{
if (item.Dat.Idx >= numDequeue && item.Dat.Idx < mDataset.Count - mKClust)
{
tmp.Add(item);
}
//else
//{
// Console.WriteLine("Remove {0}", item.Dat.Idx - numDequeue);
//}
}
patch.List = tmp;
patch.ProcessList();
patch.NeedUpdate = patch.List.Count < mKNn && oldCount >= mKNn;
}
}
// update dataset
mDataset.RemoveRange(mDataset.Count - mKClust, mKClust);
mDataset.RemoveRange(0, numDequeue);
// add new instances to dataset
int preAddCount = mDataset.Count;
mDataset.AddRange(dsNewInst);
// precompute transposed matrices
SparseMatrix <double> trNewInst = ModelUtils.GetTransposedMatrix(dsNewInst);
SparseMatrix <double> trDataset = ModelUtils.GetTransposedMatrix(mDataset);
// add new instances to neighborhoods
for (int i = 0; i < dsNewInst.Count; i++)
{
mPatches.Add(new Patch(-1));
mPatches.Last.NeedUpdate = true;
}
for (int i = 0; i < mPatches.Count; i++)
{
mPatches[i].Idx = i;
}
for (int i = 0; i < mPatches.Count; i++)
{
Patch patch = mPatches[i];
SparseVector <double> vec = mDataset[i];
if (vec != null)
{
if (patch.NeedUpdate) // full update required
{
//if (i == 1347) { Console.WriteLine("full update"); }
SparseVector <double> simVec = ModelUtils.GetDotProductSimilarity(trDataset, mDataset.Count, vec, mSimThresh);
ArrayList <KeyDat <double, int> > tmp = new ArrayList <KeyDat <double, int> >();
foreach (IdxDat <double> item in simVec)
{
if (item.Idx != i)
{
tmp.Add(new KeyDat <double, int>(item.Dat, item.Idx));
}
}
tmp.Sort(new Comparer2());
int count = Math.Min(tmp.Count, mKNnExt);
patch.List.Clear();
for (int j = 0; j < count; j++)
{
patch.List.Add(new KeyDat <double, Patch>(tmp[j].Key, mPatches[tmp[j].Dat]));
}
patch.ProcessList();
patch.NeedUpdate = false;
}
else // only new instances need to be considered
{
//if (i == 1347) { Console.WriteLine("partial update"); }
SparseVector <double> simVec = ModelUtils.GetDotProductSimilarity(trNewInst, dsNewInst.Count, vec, mSimThresh);
// check if further processing is needed
bool needMerge = false;
if (test)
{
foreach (IdxDat <double> item in simVec)
{
if (item.Dat >= patch.MinSim)
{
needMerge = true;
//Console.WriteLine("{0} {1}", item.Dat, patch.MinSim);
break;
}
}
}
else
{
foreach (IdxDat <double> item in simVec)
{
if (item.Dat > patch.MinSim)
{
needMerge = true;
//Console.WriteLine("{0} {1}", item.Dat, patch.MinSim);
break;
}
}
}
if (needMerge || patch.List.Count < mKNn)
{
//if (i == 1347) { Console.WriteLine("merge"); }
int oldCount = patch.List.Count;
ArrayList <KeyDat <double, Patch> > tmp = new ArrayList <KeyDat <double, Patch> >();
foreach (IdxDat <double> item in simVec)
{
tmp.Add(new KeyDat <double, Patch>(item.Dat, mPatches[item.Idx + preAddCount]));
}
// merge the two lists
// TODO: speed this up
patch.List.AddRange(tmp);
patch.List.Sort(new Comparer());
// trim list to size
if (oldCount >= mKNn)
{
patch.List.RemoveRange(oldCount, patch.List.Count - oldCount);
}
patch.ProcessList();
}
}
}
}
/*prof*/ sw.Save("knn.txt", _count);
// *** Test ***
sw.Reset();
ModelUtils.GetDotProductSimilarity(mDataset, mSimThresh, /*fullMatrix=*/ true);
sw.Save("selfSim.txt", _count, mDataset.Count.ToString());
if (test)
{
simMtx = ModelUtils.GetDotProductSimilarity(mDataset, mSimThresh, /*fullMatrix=*/ true);
ArrayList <Patch> patches = new ArrayList <Patch>();
for (int i = 0; i < mDataset.Count; i++)
{
patches.Add(new Patch(i));
}
foreach (IdxDat <SparseVector <double> > simMtxRow in simMtx)
{
if (simMtxRow.Dat.Count <= 1)
{
mLogger.Warn("Update", "Instance #{0} has no neighborhood.", simMtxRow.Idx);
}
ArrayList <KeyDat <double, int> > knn = new ArrayList <KeyDat <double, int> >(simMtxRow.Dat.Count);
foreach (IdxDat <double> item in simMtxRow.Dat)
{
if (item.Idx != simMtxRow.Idx)
{
knn.Add(new KeyDat <double, int>(item.Dat, item.Idx));
}
}
knn.Sort(new Comparer2());
int count = Math.Min(knn.Count, mKNnExt);
for (int i = 0; i < count; i++)
{
patches[simMtxRow.Idx].List.Add(new KeyDat <double, Patch>(knn[i].Key, patches[knn[i].Dat]));
}
patches[simMtxRow.Idx].ProcessList();
}
// compare
if (patches.Count != mPatches.Count)
{
throw new Exception("Count mismatch.");
}
for (int i = 0; i < mPatches.Count; i++)
{
if (patches[i].List.Count < mKNn && patches[i].List.Count != mPatches[i].List.Count)
{
Console.WriteLine(mPatches[i].List.Count);
Console.WriteLine(patches[i].List.Count);
Output(mPatches[i].List);
Output(patches[i].List);
Console.WriteLine(i);
throw new Exception("List count mismatch.");
}
int count = Math.Min(mPatches[i].List.Count, mKNn);
for (int j = 0; j < count; j++)
{
//Console.WriteLine("{4} {0}-{1} {2}-{3}", mPatches[i].List[j].Key, mPatches[i].List[j].Dat.Idx, patches[i].List[j].Key, patches[i].List[j].Dat.Idx, i);
if (mPatches[i].List[j].Key != patches[i].List[j].Key || mPatches[i].List[j].Dat.Idx != patches[i].List[j].Dat.Idx)
{
Console.WriteLine("i:{4} fast:{0}-{1} slow:{2}-{3}", mPatches[i].List[j].Key, mPatches[i].List[j].Dat.Idx, patches[i].List[j].Key, patches[i].List[j].Dat.Idx, i);
int idxFast = mPatches[i].List[j].Dat.Idx;
int idxSlow = patches[i].List[j].Dat.Idx;
Console.WriteLine("slow @ fast idx: {0}", GetKey(patches[i].List, idxFast));
Console.WriteLine("fast @ slow idx: {0}", GetKey(mPatches[i].List, idxSlow));
throw new Exception("Patch item mismatch.");
}
}
}
}
// *** End of test ***
//Console.WriteLine("Number of patches: {0}", mPatches.Count);
//int waka = 0;
//foreach (Patch patch in mPatches)
//{
// waka += patch.List.Count;
//}
//Console.WriteLine("Avg list size: {0}", (double)waka / (double)mPatches.Count);
Console.WriteLine((DateTime.Now - t).TotalMilliseconds);
/*prof*/ sw.Reset();
mLogger.Info("Update", "Constructing system of linear equations ...");
LabeledDataset <double, SparseVector <double> > lsqrDs = new LabeledDataset <double, SparseVector <double> >();
Vector2D[] layout = new Vector2D[mDataset.Count - mKClust];
foreach (Patch patch in mPatches)
{
int count = Math.Min(patch.List.Count, mKNn);
SparseVector <double> eq = new SparseVector <double>();
double wgt = 1.0 / (double)count;
for (int i = 0; i < count; i++)
{
eq.InnerIdx.Add(patch.List[i].Dat.Idx);
eq.InnerDat.Add(-wgt);
}
eq.InnerIdx.Sort(); // *** sort only indices
eq[patch.Idx] = 1;
lsqrDs.Add(0, eq);
}
for (int i = mDataset.Count - mKClust, j = 0; i < mDataset.Count; i++, j++)
{
SparseVector <double> eq = new SparseVector <double>(new IdxDat <double>[] { new IdxDat <double>(i, 1) });
lsqrDs.Add(mRefPos[j].X, eq);
}
LSqrModel lsqr = new LSqrModel();
mSolX.RemoveRange(0, numDequeue);
double[] aux = new double[mKClust];
mSolX.CopyTo(mSolX.Count - mKClust, aux, 0, mKClust);
mSolX.RemoveRange(mSolX.Count - mKClust, mKClust);
foreach (SparseVector <double> newVec in newInst)
{
mSolX.Add(0);
}
mSolX.AddRange(aux);
lsqr.InitialSolution = mSolX.ToArray();
lsqr.Train(lsqrDs);
mSolX = lsqr.Solution.GetWritableCopy();
//for (int i = 0; i < lsqr.InitialSolution.Length; i++)
//{
// Console.WriteLine("{0}\t{1}", lsqr.InitialSolution[i], lsqr.Solution[i]);
//}
for (int i = 0; i < layout.Length; i++)
{
layout[i].X = lsqr.Solution[i];
}
for (int i = lsqrDs.Count - mKClust, j = 0; i < lsqrDs.Count; i++, j++)
{
lsqrDs[i].Label = mRefPos[j].Y;
}
mSolY.RemoveRange(0, numDequeue);
aux = new double[mKClust];
mSolY.CopyTo(mSolY.Count - mKClust, aux, 0, mKClust);
mSolY.RemoveRange(mSolY.Count - mKClust, mKClust);
foreach (SparseVector <double> newVec in newInst)
{
mSolY.Add(0);
}
mSolY.AddRange(aux);
lsqr.InitialSolution = mSolY.ToArray();
lsqr.Train(lsqrDs);
mSolY = lsqr.Solution.GetWritableCopy();
for (int i = 0; i < layout.Length; i++)
{
layout[i].Y = lsqr.Solution[i];
}
/*prof*/ sw.Save("lsqr.txt", _count);
// -----------------------------------------------------------------
// make ptInfo
// -----------------------------------------------------------------
ptInfo = new PtInfo[layout.Length];
int ii = 0;
foreach (Vector2D pt in layout)
{
ptInfo[ii] = new PtInfo();
ptInfo[ii].X = pt.X;
ptInfo[ii].Y = pt.Y;
ptInfo[ii].Vec = mDataset[ii];
ii++;
}
// -----------------------------------------------------------------
return(settings == null ? layout : settings.AdjustLayout(layout));
}
public Vector2D[] ComputeLayout(LayoutSettings settings)
{
UnlabeledDataset <SparseVector <double> > dataset = new UnlabeledDataset <SparseVector <double> >(mDataset);
// clustering
mLogger.Info("ComputeLayout", "Clustering ...");
KMeansFast kMeans = new KMeansFast(mKClust);
kMeans.Eps = mKMeansEps;
kMeans.Random = mRandom;
kMeans.Trials = 1;
ClusteringResult clustering = kMeans.Cluster(mDataset); // throws ArgumentValueException
// determine reference instances
UnlabeledDataset <SparseVector <double> > dsRefInst = new UnlabeledDataset <SparseVector <double> >();
foreach (Cluster cluster in clustering.Roots)
{
SparseVector <double> centroid
= cluster.Items.Count > 0 ? cluster.ComputeCentroid(mDataset, CentroidType.NrmL2) : new SparseVector <double>();
dsRefInst.Add(centroid); // dataset of reference instances
dataset.Add(centroid); // add centroids to the main dataset
}
// position reference instances
mLogger.Info("ComputeLayout", "Positioning reference instances ...");
SparseMatrix <double> simMtx = ModelUtils.GetDotProductSimilarity(dsRefInst, mSimThresh, /*fullMatrix=*/ false);
StressMajorizationLayout sm = new StressMajorizationLayout(dsRefInst.Count, new DistFunc(simMtx));
sm.Random = mRandom;
Vector2D[] centrPos = sm.ComputeLayout();
// k-NN
mLogger.Info("ComputeLayout", "Computing similarities ...");
simMtx = ModelUtils.GetDotProductSimilarity(dataset, mSimThresh, /*fullMatrix=*/ true);
mLogger.Info("ComputeLayout", "Constructing system of linear equations ...");
LabeledDataset <double, SparseVector <double> > lsqrDs = new LabeledDataset <double, SparseVector <double> >();
foreach (IdxDat <SparseVector <double> > simMtxRow in simMtx)
{
if (simMtxRow.Dat.Count <= 1)
{
mLogger.Warn("ComputeLayout", "Instance #{0} has no neighborhood.", simMtxRow.Idx);
}
ArrayList <KeyDat <double, int> > knn = new ArrayList <KeyDat <double, int> >(simMtxRow.Dat.Count);
foreach (IdxDat <double> item in simMtxRow.Dat)
{
if (item.Idx != simMtxRow.Idx)
{
knn.Add(new KeyDat <double, int>(item.Dat, item.Idx));
}
}
knn.Sort(DescSort <KeyDat <double, int> > .Instance);
int count = Math.Min(knn.Count, mKNN);
SparseVector <double> eq = new SparseVector <double>();
double wgt = 1.0 / (double)count;
for (int i = 0; i < count; i++)
{
eq.InnerIdx.Add(knn[i].Dat);
eq.InnerDat.Add(-wgt);
}
eq.InnerIdx.Sort(); // *** sort only indices
eq[simMtxRow.Idx] = 1;
lsqrDs.Add(0, eq);
}
Vector2D[] layout = new Vector2D[dataset.Count - mKClust];
for (int i = dataset.Count - mKClust, j = 0; i < dataset.Count; i++, j++)
{
SparseVector <double> eq = new SparseVector <double>(new IdxDat <double>[] { new IdxDat <double>(i, 1) });
lsqrDs.Add(centrPos[j].X, eq);
}
LSqrModel lsqr = new LSqrModel();
lsqr.Train(lsqrDs);
for (int i = 0; i < layout.Length; i++)
{
layout[i].X = lsqr.Solution[i];
}
for (int i = lsqrDs.Count - mKClust, j = 0; i < lsqrDs.Count; i++, j++)
{
lsqrDs[i].Label = centrPos[j].Y;
}
lsqr.Train(lsqrDs);
for (int i = 0; i < layout.Length; i++)
{
layout[i].Y = lsqr.Solution[i];
}
return(settings == null ? layout : settings.AdjustLayout(layout));
}
public Vector2D[] ComputeLayout(LayoutSettings settings)
{
// clustering
mLogger.Info("ComputeLayout", "Clustering ...");
mKMeans = new IncrementalKMeans(mKClust);
mKMeans.Eps = mKMeansEps;
mKMeans.Random = mRandom;
mKMeans.Trials = 3;
ClusteringResult clustering = mKMeans.Cluster(mDataset); // throws ArgumentValueException
// determine reference instances
UnlabeledDataset <SparseVector <double> > dsRefInst = new UnlabeledDataset <SparseVector <double> >();
foreach (SparseVector <double> centroid in mKMeans.GetCentroids())
{
dsRefInst.Add(centroid); // dataset of reference instances
mDataset.Add(centroid); // add centroids to the main dataset
}
// position reference instances
mLogger.Info("ComputeLayout", "Positioning reference instances ...");
SparseMatrix <double> simMtx = ModelUtils.GetDotProductSimilarity(dsRefInst, mSimThresh, /*fullMatrix=*/ false);
StressMajorizationLayout sm = new StressMajorizationLayout(dsRefInst.Count, new DistFunc(simMtx));
sm.Random = mRandom;
sm.MaxSteps = int.MaxValue;
sm.MinDiff = 0.00001;
mRefPos = sm.ComputeLayout();
// k-NN
mLogger.Info("ComputeLayout", "Computing similarities ...");
simMtx = ModelUtils.GetDotProductSimilarity(mDataset, mSimThresh, /*fullMatrix=*/ true);
mLogger.Info("ComputeLayout", "Constructing system of linear equations ...");
LabeledDataset <double, SparseVector <double> > lsqrDs = new LabeledDataset <double, SparseVector <double> >();
mPatches = new ArrayList <Patch>(mDataset.Count);
for (int i = 0; i < mDataset.Count; i++)
{
mPatches.Add(new Patch(i));
}
foreach (IdxDat <SparseVector <double> > simMtxRow in simMtx)
{
if (simMtxRow.Dat.Count <= 1)
{
mLogger.Warn("ComputeLayout", "Instance #{0} has no neighborhood.", simMtxRow.Idx);
}
ArrayList <KeyDat <double, int> > knn = new ArrayList <KeyDat <double, int> >(simMtxRow.Dat.Count);
foreach (IdxDat <double> item in simMtxRow.Dat)
{
if (item.Idx != simMtxRow.Idx)
{
knn.Add(new KeyDat <double, int>(item.Dat, item.Idx));
}
}
knn.Sort(DescSort <KeyDat <double, int> > .Instance);
int count = Math.Min(knn.Count, mKNnExt);
for (int i = 0; i < count; i++)
{
mPatches[simMtxRow.Idx].List.Add(new KeyDat <double, Patch>(knn[i].Key, mPatches[knn[i].Dat]));
}
mPatches[simMtxRow.Idx].ProcessList();
count = Math.Min(knn.Count, mKNn);
SparseVector <double> eq = new SparseVector <double>();
double wgt = 1.0 / (double)count;
for (int i = 0; i < count; i++)
{
eq.InnerIdx.Add(knn[i].Dat);
eq.InnerDat.Add(-wgt);
}
eq.InnerIdx.Sort(); // *** sort only indices
eq[simMtxRow.Idx] = 1;
lsqrDs.Add(0, eq);
}
Vector2D[] layout = new Vector2D[mDataset.Count - mKClust];
for (int i = mDataset.Count - mKClust, j = 0; i < mDataset.Count; i++, j++)
{
SparseVector <double> eq = new SparseVector <double>(new IdxDat <double>[] { new IdxDat <double>(i, 1) });
lsqrDs.Add(mRefPos[j].X, eq);
}
LSqrModel lsqr = new LSqrModel();
lsqr.Train(lsqrDs);
mSolX = lsqr.Solution.GetWritableCopy();
for (int i = 0; i < layout.Length; i++)
{
layout[i].X = lsqr.Solution[i];
}
for (int i = lsqrDs.Count - mKClust, j = 0; i < lsqrDs.Count; i++, j++)
{
lsqrDs[i].Label = mRefPos[j].Y;
}
lsqr.Train(lsqrDs);
mSolY = lsqr.Solution.GetWritableCopy();
for (int i = 0; i < layout.Length; i++)
{
layout[i].Y = lsqr.Solution[i];
}
return(settings == null ? layout : settings.AdjustLayout(layout));
}