/**
* @param Clustering
* The array of clusters
* @param posi
* The index of the merged cluster
* @param posj
* The index of the cluster that will be eliminated from the
* clustering
* @param distance
* The distance matrix that will be updated
*/
void UpdateDistances(List <SpeakerCluster> clustering, int posi, int posj, Array2DRowRealMatrix distance)
{
int clusterCount = clustering.Count;
for (int i = 0; i < clusterCount; i++)
{
distance.setEntry(i, posi, ComputeDistance(clustering[i], clustering[posi]));
distance.setEntry(posi, i, distance.getEntry(i, posi));
}
for (int i = posj; i < clusterCount - 1; i++)
{
for (int j = 0; j < clusterCount; j++)
{
distance.setEntry(i, j, distance.getEntry(i + 1, j));
}
}
for (int i = 0; i < clusterCount; i++)
{
for (int j = posj; j < clusterCount - 1; j++)
{
distance.setEntry(i, j, distance.getEntry(i, j + 1));
}
}
}
internal virtual void printMatrix(Array2DRowRealMatrix array2DRowRealMatrix)
{
for (int i = 0; i < array2DRowRealMatrix.getRowDimension(); i++)
{
for (int j = 0; j < array2DRowRealMatrix.getColumnDimension(); j++)
{
[email protected](new StringBuilder().append(array2DRowRealMatrix.getEntry(i, j)).append(" ").toString());
}
[email protected]();
}
}
void PrintMatrix(Array2DRowRealMatrix a)
{
for (int i = 0; i < a.getRowDimension(); i++)
{
for (int j = 0; j < a.getColumnDimension(); j++)
{
Console.Write(a.getEntry(i, j) + " ");
}
Console.WriteLine();
}
}
internal virtual void updateDistances(ArrayList arrayList, int num, int num2, Array2DRowRealMatrix array2DRowRealMatrix)
{
int num3 = arrayList.size();
for (int i = 0; i < num3; i++)
{
array2DRowRealMatrix.setEntry(i, num, this.computeDistance((SpeakerCluster)arrayList.get(i), (SpeakerCluster)arrayList.get(num)));
array2DRowRealMatrix.setEntry(num, i, array2DRowRealMatrix.getEntry(i, num));
}
for (int i = num2; i < num3 - 1; i++)
{
for (int j = 0; j < num3; j++)
{
array2DRowRealMatrix.setEntry(i, j, array2DRowRealMatrix.getEntry(i + 1, j));
}
}
for (int i = 0; i < num3; i++)
{
for (int j = num2; j < num3 - 1; j++)
{
array2DRowRealMatrix.setEntry(i, j, array2DRowRealMatrix.getEntry(i, j + 1));
}
}
}
internal virtual Array2DRowRealMatrix updateDistances(ArrayList arrayList)
{
int num = arrayList.size();
Array2DRowRealMatrix array2DRowRealMatrix = new Array2DRowRealMatrix(num, num);
for (int i = 0; i < num; i++)
{
for (int j = 0; j <= i; j++)
{
array2DRowRealMatrix.setEntry(i, j, this.computeDistance((SpeakerCluster)arrayList.get(i), (SpeakerCluster)arrayList.get(j)));
array2DRowRealMatrix.setEntry(j, i, array2DRowRealMatrix.getEntry(i, j));
}
}
return(array2DRowRealMatrix);
}
/**
* @param Clustering
* The array of clusters
*/
Array2DRowRealMatrix UpdateDistances(List <SpeakerCluster> clustering)
{
int clusterCount = clustering.Count;
Array2DRowRealMatrix distance = new Array2DRowRealMatrix(clusterCount, clusterCount);
for (int i = 0; i < clusterCount; i++)
{
for (int j = 0; j <= i; j++)
{
distance.setEntry(i, j, ComputeDistance(clustering[i], clustering[j]));
distance.setEntry(j, i, distance.getEntry(i, j));
}
}
return(distance);
}
/**
* @param features The feature vectors to be used for clustering
* @return A cluster for each speaker detected based on the feature vectors provided
*/
public List <SpeakerCluster> Cluster(List <float[]> features)
{
List <SpeakerCluster> ret = new List <SpeakerCluster>();
Array2DRowRealMatrix featuresMatrix = ArrayToRealMatrix(features, features.Count);
LinkedList <Integer> l = GetAllChangingPoints(featuresMatrix);
var it = l.GetEnumerator();
int curent;
it.MoveNext();
int previous = it.Current;
while (it.MoveNext())
{
curent = it.Current;
Segment s = new Segment(previous * Segment.FrameLength, (curent - previous)
* (Segment.FrameLength));
Array2DRowRealMatrix featuresSubset = (Array2DRowRealMatrix)featuresMatrix.getSubMatrix(
previous, curent - 1, 0, 12);
ret.Add(new SpeakerCluster(s, featuresSubset, GetBICValue(featuresSubset)));
previous = curent;
}
int clusterCount = ret.Count;
Array2DRowRealMatrix distance;
distance = new Array2DRowRealMatrix(clusterCount, clusterCount);
distance = UpdateDistances(ret);
while (true)
{
double distmin = 0;
int imin = -1, jmin = -1;
for (int i = 0; i < clusterCount; i++)
{
for (int j = 0; j < clusterCount; j++)
{
if (i != j)
{
distmin += distance.getEntry(i, j);
}
}
}
distmin /= (clusterCount * (clusterCount - 1) * 4);
for (int i = 0; i < clusterCount; i++)
{
for (int j = 0; j < clusterCount; j++)
{
if (distance.getEntry(i, j) < distmin && i != j)
{
distmin = distance.getEntry(i, j);
imin = i;
jmin = j;
}
}
}
if (imin == -1)
{
break;
}
ret[imin].MergeWith(ret[jmin]);
UpdateDistances(ret, imin, jmin, distance);
ret.Remove(jmin);
clusterCount--;
}
return(ret);
}