/// <summary>
/// Finds two vectors and returns the value.
/// </summary>
internal double Find(Peak vector1Peak, GroupInfo vector1Group, Peak vector2Peak, GroupInfo vector2Group)
{
IntensityMatrix.RowHeader h1 = new IntensityMatrix.RowHeader(vector1Peak, vector1Group);
IntensityMatrix.RowHeader h2 = new IntensityMatrix.RowHeader(vector2Peak, vector2Group);
int i = ValueMatrix.FindIndex(h1);
int j = ValueMatrix.FindIndex(h2);
return(Values[i, j]);
}
/// <summary>
///
/// </summary>
protected override IEnumerable <Cluster> Cluster(IntensityMatrix vmatrix, DistanceMatrix UNUSED, ArgsClusterer args, ConfigurationClusterer tag, ProgressReporter prog)
{
// Get parameters
// COUNT LIMIT
int countLimit = (int)tag.UntypedArgs.Parameters[0];
// DISTANCE LIMIT
double distanceLimit = (double)tag.UntypedArgs.Parameters[1];
// SEED PEAK
WeakReference <Peak> seedPeakRef = (WeakReference <Peak>)tag.UntypedArgs.Parameters[2];
Peak seedPeak = seedPeakRef.GetTargetOrThrow();
// SEED GROUP
GroupInfo groupInfo = (GroupInfo)tag.UntypedArgs.Parameters[3];
// DO-K-MEANS?
bool doKMeans = (bool)tag.UntypedArgs.Parameters[4];
// Create the seed cluster
Cluster seedCluster = new Cluster("1", tag);
List <Cluster> seedList = new List <Cluster> {
seedCluster
};
int seedIndex = vmatrix.FindIndex(new IntensityMatrix.RowHeader(seedPeak, args.SplitGroups ? groupInfo : null));
if (seedIndex == -1)
{
throw new InvalidOperationException($"The chosen peak {{{seedPeak}}} cannot be used a seed because it is not present in the value matrix. Please check that this peak has not been excluded by the filter condition {{{args.PeakFilter}}}.");
}
seedCluster.Exemplars.Add(vmatrix.Vectors[seedIndex]);
// Autogenerate the clusters
int? nCountLimit = (countLimit != Int32.MinValue) ? countLimit : (int?)null;
double?nDistanceLimit = (distanceLimit != Double.MinValue) ? countLimit : (double?)null;
List <Cluster> autoGenClusters = AutogenerateClusters(vmatrix, seedList, nDistanceLimit, nCountLimit, args.Distance, tag, prog);
// Do k-means (if requested)
if (doKMeans)
{
prog.Enter("k-means");
LegacyClustererHelper.PerformKMeansCentering(vmatrix, autoGenClusters, args.Distance, prog);
prog.Leave();
}
// Return full list
return(autoGenClusters);
}
