// Cluster_Kmeans::Cluster()
int Cluster_Kmeans::Cluster() {
// First determine which frames are being clustered.
Iarray const& FramesToCluster = FrameDistances().FramesToCluster();
// Determine seeds
FindKmeansSeeds( FramesToCluster );
if (mode_ == RANDOM)
RN_.rn_set( kseed_ );
int pointCount = (int)FramesToCluster.size();
// This array will hold the indices of the points to process each iteration.
// If sequential this is just 0 -> pointCount. If random this will be
// reassigned each iteration.
Iarray PointIndices;
PointIndices.reserve( pointCount );
for (int processIdx = 0; processIdx != pointCount; processIdx++)
PointIndices.push_back( processIdx );
// Add the seed clusters
for (Iarray::const_iterator seedIdx = SeedIndices_.begin();
seedIdx != SeedIndices_.end(); ++seedIdx)
{
int seedFrame = FramesToCluster[ *seedIdx ];
// A centroid is created for new clusters.
AddCluster( ClusterDist::Cframes(1, seedFrame) );
// NOTE: No need to calc best rep frame, only 1 frame.
if (debug_ > 0)
mprintf("Put frame %i in cluster %i (seed index=%i).\n",
seedFrame, clusters_.back().Num(), *seedIdx);
}
// Assign points in 3 passes. If a point looked like it belonged to cluster A
// at first, but then we added many other points and altered our cluster
// shapes, its possible that we will want to reassign it to cluster B.
for (int iteration = 0; iteration != maxIt_; iteration++)
{
if (mode_ == RANDOM)
ShufflePoints( PointIndices );
// Add each point to an existing cluster, and recompute centroid
mprintf("\tRound %i: ", iteration);
ProgressBar progress( PointIndices.size() );
int Nchanged = 0;
int prog = 0;
for (Iarray::const_iterator pointIdx = PointIndices.begin();
pointIdx != PointIndices.end(); ++pointIdx, ++prog)
{
if (debug_ < 1) progress.Update( prog );
int oldClusterIdx = -1;
// if ( iteration != 0 || mode_ != SEQUENTIAL) // FIXME: Should this really happen for RANDOM
// {
int pointFrame = FramesToCluster[ *pointIdx ];
if (debug_ > 0)
mprintf("DEBUG: Processing frame %i (index %i)\n", pointFrame, *pointIdx);
bool pointWasYanked = true;
if (iteration > 0) {
// Yank this point out of its cluster, recompute the centroid
for (cluster_it C1 = clusters_.begin(); C1 != clusters_.end(); ++C1)
{
if (C1->HasFrame( pointFrame ))
{
// If this point is alone in its cluster its in the right place
if (C1->Nframes() == 1) {
pointWasYanked = false;
continue; // FIXME: should this be a break?
}
//oldBestRep = C1->BestRepFrame();
oldClusterIdx = C1->Num();
C1->RemoveFrameUpdateCentroid( Cdist_, pointFrame ); // TEST
// C1->RemoveFrameFromCluster( pointFrame );
//newBestRep = C1->FindBestRepFrame();
// C1->CalculateCentroid( Cdist_ );
if (debug_ > 0)
mprintf("Remove Frame %i from cluster %i\n", pointFrame, C1->Num());
//if (clusterToClusterCentroid_) {
// if (oldBestRep != NewBestRep)
// C1->AlignToBestRep( Cdist_ ); // FIXME: Only relevant for COORDS dist?
// C1->CalculateCentroid( Cdist_ ); // FIXME: Seems unnessecary to align prior
//}
}
}
} else {
// First iteration. If this point is already in a cluster it is a seed.
for (cluster_it C1 = clusters_.begin(); C1 != clusters_.end(); ++C1)
{
if (C1->HasFrame( pointFrame )) {
pointWasYanked = false;
if (debug_ > 0)
mprintf("Frame %i was already used to seed cluster %i\n",
pointFrame, C1->Num());
continue; // FIXME break?
}
}
}
if (pointWasYanked) {
// Find out what cluster this point is now closest to.
double closestDist = -1.0;
cluster_it closestCluster = clusters_.begin();
for (cluster_it C1 = clusters_.begin(); C1 != clusters_.end(); ++C1)
{
double dist = Cdist_->FrameCentroidDist(pointFrame, C1->Cent());
if (closestDist < 0.0 || dist < closestDist)
{
closestDist = dist;
closestCluster = C1;
}
}
//oldBestRep = closestCluster->BestRepFrame();
closestCluster->AddFrameUpdateCentroid( Cdist_, pointFrame ); // TEST
// closestCluster->AddFrameToCluster( pointFrame );
//newBestRep = closestCluster->FindBestFrameFrame();
// closestCluster->CalculateCentroid( Cdist_ );
if (closestCluster->Num() != oldClusterIdx)
{
Nchanged++;
if (debug_ > 0)
mprintf("Remove Frame %i from cluster %i, but add to cluster %i (dist= %f).\n",
pointFrame, oldClusterIdx, closestCluster->Num(), closestDist);
} else {
if (debug_ > 0)
mprintf("Frame %i staying in cluster %i\n", pointFrame, closestCluster->Num());
}
if (clusterToClusterCentroid_) {
//if (oldBestRep != NewBestRep) {
// C1->AlignToBestRep( Cdist_ ); // FIXME: Only relevant for COORDS dist?
// C1->CalculateCentroid( Cdist_ ); // FIXME: Seems unnessecary to align prior
//}
}
}
// }
} // END loop over points to cluster
if (Nchanged == 0) {
mprintf("\tK-means round %i: No change. Skipping the rest of the iterations.\n", iteration);
break;
} else
mprintf("\tK-means round %i: %i points changed cluster assignment.\n", iteration, Nchanged);
} // END k-means iterations
// Remove any empty clusters
// FIXME: Will there ever be empty clusters?
RemoveEmptyClusters();
// NOTE in PTRAJ here align all frames to best rep
return 0;
}
/** Ester, Kriegel, Sander, Xu; Proceedings of 2nd International Conference
* on Knowledge Discovery and Data Mining (KDD-96); pp 226-231.
*/
int Cluster_DBSCAN::Cluster() {
std::vector<int> NeighborPts;
std::vector<int> Npts2; // Will hold neighbors of a neighbor
std::vector<int> FramesToCluster;
ClusterDist::Cframes cluster_frames;
// First determine which frames are being clustered.
// FIXME: Just use sieved array?
for (int frame = 0; frame < (int)FrameDistances_.Nframes(); ++frame)
if (!FrameDistances_.IgnoringRow( frame ))
FramesToCluster.push_back( frame );
// Calculate Kdist function
if (!kdist_.Empty()) {
if (kdist_.Size() == 1)
ComputeKdist( kdist_.Front(), FramesToCluster );
else
ComputeKdistMap( kdist_, FramesToCluster );
return 0;
}
// Set up array to keep track of points that have been visited.
// Make it the size of FrameDistances so we can index into it. May
// waste memory during sieving but makes code easier.
std::vector<bool> Visited( FrameDistances_.Nframes(), false );
// Set up array to keep track of whether points are noise or in a cluster.
Status_.assign( FrameDistances_.Nframes(), UNASSIGNED);
mprintf("\tStarting DBSCAN Clustering:\n");
ProgressBar cluster_progress(FramesToCluster.size());
int iteration = 0;
for (std::vector<int>::iterator point = FramesToCluster.begin();
point != FramesToCluster.end(); ++point)
{
if (!Visited[*point]) {
// Mark this point as visited
Visited[*point] = true;
// Determine how many other points are near this point
RegionQuery( NeighborPts, FramesToCluster, *point );
if (debug_ > 0) {
mprintf("\tPoint %i\n", *point + 1);
mprintf("\t\t%u neighbors:", NeighborPts.size());
}
// If # of neighbors less than cutoff, noise; otherwise cluster
if ((int)NeighborPts.size() < minPoints_) {
if (debug_ > 0) mprintf(" NOISE\n");
Status_[*point] = NOISE;
} else {
// Expand cluster
cluster_frames.clear();
cluster_frames.push_back( *point );
// NOTE: Use index instead of iterator since NeighborPts may be
// modified inside this loop.
unsigned int endidx = NeighborPts.size();
for (unsigned int idx = 0; idx < endidx; ++idx) {
int neighbor_pt = NeighborPts[idx];
if (!Visited[neighbor_pt]) {
if (debug_ > 0) mprintf(" %i", neighbor_pt + 1);
// Mark this neighbor as visited
Visited[neighbor_pt] = true;
// Determine how many other points are near this neighbor
RegionQuery( Npts2, FramesToCluster, neighbor_pt );
if ((int)Npts2.size() >= minPoints_) {
// Add other points to current neighbor list
NeighborPts.insert( NeighborPts.end(), Npts2.begin(), Npts2.end() );
endidx = NeighborPts.size();
}
}
// If neighbor is not yet part of a cluster, add it to this one.
if (Status_[neighbor_pt] != INCLUSTER) {
cluster_frames.push_back( neighbor_pt );
Status_[neighbor_pt] = INCLUSTER;
}
}
// Remove duplicate frames
// TODO: Take care of this in Renumber?
std::sort(cluster_frames.begin(), cluster_frames.end());
ClusterDist::Cframes::iterator it = std::unique(cluster_frames.begin(),
cluster_frames.end());
cluster_frames.resize( std::distance(cluster_frames.begin(),it) );
// Add cluster to the list
AddCluster( cluster_frames );
if (debug_ > 0) {
mprintf("\n");
PrintClusters();
}
}
}
cluster_progress.Update(iteration++);
} // END loop over FramesToCluster
// Calculate the distances between each cluster based on centroids
CalcClusterDistances();
return 0;
}
