void grouping(std::vector<tLineSegment<point>> &L, double eps, size_t minLines,
distance_functor &_d, progress_visitor &pvis)
{
size_t clusterID=0;
std::queue<size_t> Q;
pvis.init(L.size());
for (size_t i=0; i < L.size(); i++)
{
if (i%100==0)
pvis(i,L.size(),"Phase 2: Clustering Segments");
if (L[i].cluster == UNCLASSIFIED)
{
std::vector<size_t> Ne = compute_NeIndizes<point>(L,i,eps,_d);
//cout << "Ne[" << i << "]="<< Ne.size() << endl;
if (Ne.size()+1 >= minLines)
{
L[i].cluster = clusterID;
for (auto it = Ne.begin(); it != Ne.end(); it++)
{
L[*it].cluster = clusterID;
Q.push(*it);
}
expandCluster(L,Q, eps,minLines,clusterID,_d);
clusterID++;
}else // not minLines
{
L[i].cluster = NOISE;
}
}
}
/*Step 3: check that clusters come from more than minLines
* different trajectories*/
for (int i=0; i < (int)clusterID; i++)
{
std::set<size_t> sources;
for (size_t j = 0; j < L.size(); j++)
if (L[j].cluster == i)
sources.insert (L[j].trajectory_index);
if (sources.size() < minLines)
{
for (size_t j = 0; j < L.size(); j++)
if (L[j].cluster == i)
L[j].cluster = REMOVED;
}
}
/*Step 3a: ClusterID compression into a range*/
/* Step 4: Representation*/
// left out, will do that later. This step creates a representative
// trajectory for a cluster.
};
void DbScan::scan()
{
unsigned clusterId = 1;
std::vector<Point>::iterator p = points_.begin();
while(p != points_.end())
{
// Point is thus far unclassified
if(p->label == -1)
expandCluster(p, clusterId);
++p;
}
}
vector<vector<int>> cluster(vector<Segment> &D, double epsilon, int minlns){
Segment L;
vector<int> clus(D.size());
fill(clus.begin(), clus.end(),-1);
vector<int> temp_neigh;
progress::begin();
int clusterId = 0;
for (int i = 0; i<D.size(); i++){
progress::step("clustering: ", i, 1, D.size());
if (clus[i]==-1){
temp_neigh = neighborhood(D, i, epsilon);
if(temp_neigh.size()>= minlns){
clus[i]=clusterId;
queue<int> Q;
for(auto j : temp_neigh){
clus[j]= clusterId;
Q.push(j);
}
expandCluster(D, clus, Q, clusterId, epsilon, minlns);
clusterId++;
}
else{
clus[i]=-2;
}
}
}
progress::done();
//collect and clean the clusters
vector<vector<int>> ret(clusterId);
vector<unordered_set<int>> participating(clusterId);
for(int i = 0; i<clus.size(); i++){
if (clus[i]>=0){
ret[clus[i]].push_back(i);
D[i].myclus = clus[i];
participating[clus[i]].insert(D[i].mytraj);
}
}
for(int i = 0; i<participating.size(); i++){
if (participating[i].size()<=minlns){
participating.erase(participating.begin() + i);
ret.erase(ret.begin() + i);
i--;
}
}
sort(ret.begin(), ret.end(), cs);
return ret;
}
