/// <summary>
/// d-k-means++
/// Ignores insignificant variables.
/// Returns new clusters (these won't be added to the core so make sure to do so)
/// </summary>
private static List <Cluster> AutogenerateClusters(IntensityMatrix vmatrix, List <Cluster> seed, double?stoppingDistance, int?stoppingCount, ConfigurationMetric metric, ConfigurationClusterer tag, ProgressReporter prog)
{
// Make a log of whatever limits have been set
if (!stoppingCount.HasValue && !stoppingDistance.HasValue)
{
throw new InvalidOperationException("No stopping condition set.");
}
// Assign all variables to nearest
List <Cluster> result = new List <Cluster>(seed);
// Get the actual limits
int iterations = 0;
int count = (stoppingCount - seed.Count) ?? Int32.MaxValue;
double distance = stoppingDistance ?? Double.MinValue;
// Get the most distant variable
prog.Enter("Initialising assignments");
LegacyClustererHelper.Assign(vmatrix, result, ECandidateMode.Exemplars, metric, prog);
Assignment mostDistant = GetMostDistantAssignment(result);
prog.Leave();
// Continue until our limits are breached
while ((count > 0) && (mostDistant.Score > distance))
{
// Check we haven't got unreasonable limits
iterations++;
prog.Enter("Centre generation (iteration " + iterations + ")");
if (iterations > 1000)
{
throw new InvalidOperationException("Too many iterations - exiting.");
}
// Create a new cluster with the most distant variable as its exemplar
var newCluster = new Cluster((result.Count + 1).ToString(), tag);
result.Add(newCluster);
newCluster.Exemplars.Add(mostDistant.Vector.Values); // todo: check to prevent multiple assignment?
// Make the assignments based on the closest exemplars
LegacyClustererHelper.Assign(vmatrix, result, ECandidateMode.Exemplars, metric, prog);
// Basic check
if (!newCluster.Assignments.Vectors.Contains(mostDistant.Vector))
{
throw new InvalidOperationException("Problem creating new cluster from vector - " + mostDistant.Vector.ToString() + " doesn't like being in its own cluster. Check this vector for discrepancies.");
}
// Get the next most distant variable
count = count - 1;
mostDistant = GetMostDistantAssignment(result);
prog.Leave();
}
// Return the number of iterations
return(result);
}
/// <summary>
///
/// </summary>
protected override IEnumerable <Cluster> Cluster(IntensityMatrix vmatrix, DistanceMatrix UNUSED, ArgsClusterer args, ConfigurationClusterer tag, ProgressReporter prog)
{
// GET OPTIONS
int k = (int)args.Parameters[0];
// CREATE RANDOM CENTRES
Random rnd = new Random();
var potentialCentres = new List <Vector>(vmatrix.Vectors);
List <Cluster> clusters = new List <Cluster>();
for (int n = 0; n < k; n++)
{
int random = rnd.Next(potentialCentres.Count);
Cluster p = new Cluster((clusters.Count + 1).ToString(), tag);
Vector vec = potentialCentres[random];
p.Exemplars.Add(vec.Values);
potentialCentres.RemoveAt(random);
clusters.Add(p);
}
// Assign to exemplars
prog.Enter("Initialising assignments");
LegacyClustererHelper.Assign(vmatrix, clusters, ECandidateMode.Exemplars, args.Distance, prog);
prog.Leave();
// Centre
LegacyClustererHelper.PerformKMeansCentering(vmatrix, clusters, args.Distance, prog);
return(clusters);
}
/// <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);
}
