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 Post subject: dbscan code slow
PostPosted: Mon Feb 11, 2019 8:42 pm 
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Joined: Mon Feb 11, 2019 8:34 pm
Posts: 1
Hi,
i am trying to implement the dbscan code in c#(see below). The code takes 408 seconds for a 10000x20 matrix. it takes 20 seconds in matlab. can you point me to the part where i might be doing some thing wrong? matlab also uses kdtree.

public int[] ComputeDbscan(double[,] xtr, double epsilon, int minPts)
{

bool borderpoints = true;
int rows = xtr.GetLength(0);
int cols = xtr.GetLength(1);
bool[] visited = new bool[rows - 1 + 1];
int[] clusterId = new int[rows - 1 + 1];
int[] tags = new int[rows - 1 + 1];
for (int kk = 0; kk <= rows - 1; kk++)
tags[kk] = kk;

List<List<int>> clusterlist = new List<List<int>>();
double[] xtrRow = new double[cols - 1 + 1];
int Nweight;
int j;
int normtype = 2;//euclidean norm
bool selfmatch = true;
int ny = 0; //useless variable
List<int> N = new List<int>();
List<int> N2 = new List<int>();
alglib.kdtree kdt; //defien tree
alglib.kdtreerequestbuffer r1; //define buffer
alglib.kdtreebuildtagged(xtr, tags,rows,cols, ny,normtype, out kdt); //build tree with tags
alglib.kdtreecreaterequestbuffer(kdt, out r1); //create buffer for the tree
for (int kk = 0; kk <= cols - 1; kk++)
xtrRow[kk] = xtr[0, kk];
//Get the epsilon Factor*******************************
double[] nearest = new double[rows - 1 + 1];
if (epsilon == 0.0)
{
for (int i = 0; i <= rows - 1; i++)
{
for (int kk = 0; kk <= cols - 1; kk++)
xtrRow[kk] = xtr[i, kk];
alglib.kdtreequeryknn(kdt, xtrRow, minPts - 1);
double[] dists = new double[minPts-1];
alglib.kdtreequeryresultsdistances(kdt, ref dists);
nearest[i] = dists[minPts - 2];
}
epsilon = GetEPS(nearest); // this returns the best epsilon by simply looking for knee --not a bottleneck, negligible time
}

//***************run the dbscan loop****************************
for (int i = 0; i <= rows - 1; i++) {
if (visited[i] == true)
continue;
for (int kk = 0; kk <= cols - 1; kk++)
xtrRow[kk] = xtr[i, kk];
//Get the nearest collection of points and drop it to a list
alglib.kdtreequeryrnn(kdt, xtrRow, epsilon, selfmatch);
int[] Narray=new int[20];
alglib.kdtreequeryresultstagsi(kdt, out Narray);
//*********************************************************
Nweight = Narray.Length;
if (Nweight < minPts)
continue;
for (int kk = 0; kk <= Narray.Length - 1; kk++)
N.Add(Narray[kk]);
List<int> cluster = new List<int>();
cluster.Add(i);
visited[i] = true;
while (N.Count > 0)
{
j = N.Last();
N.RemoveAt(N.Count - 1);
if (visited[j] == true)
continue;
visited[j] = true;
for (int kk = 0; kk <= cols - 1; kk++)
xtrRow[kk] = xtr[j, kk];
//Get the nearest collection of points and drop it to a list
alglib.kdtreequeryrnn(kdt, xtrRow, epsilon, selfmatch);
int[] N2array = new int[20];
alglib.kdtreequeryresultstagsi(kdt, out N2array);
//*********************************************************
Nweight = N2array.Length;
if (Nweight >= minPts) {
for (int kk = 0; kk <= N2array.Length - 1; kk++)
N2.Add(N2array[kk]);
N = N.Union(N2).ToList();
}
if (Nweight >= minPts || borderpoints == true) {
cluster.Add(j);
}
}
clusterlist.Add(cluster);
N.Clear();
N2.Clear();
cluster = null;
}

for (int k = 0; k <= clusterlist.Count - 1; k++)
{
List<int> cluster = new List<int>();
cluster = clusterlist[k];
for (int m = 0; m <= cluster.Count - 1; m++)
clusterId[cluster[m]] = k + 1;
}
return clusterId;
}


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