/// <summary>
/// Fit the centers to the provided data.
/// </summary>
/// <param name="data">The data to fit</param>
/// <param name="weights">Weights for the data. If not provided, all points are equally weighted.</param>
public void Fit(List <LabelVector> data, float[] weights = null)
{
UpdateManager.WriteLine("Fitting centers using k-means clustering...");
_data = data;
_minDist = new float[data.Count];
_jobs = new List <Job>();
if (data.Count > JobSize)
{
int jobCount = _data.Count / JobSize;
if (_data.Count % JobSize != 0)
{
jobCount++;
}
for (int i = 0; i < _data.Count; i += JobSize)
{
_jobs.Add(new Job {
StartIndex = i, EndIndex = Math.Min(i + JobSize, _data.Count)
});
}
}
else
{
_jobs.Add(new Job {
StartIndex = 0, EndIndex = data.Count
});
}
UpdateManager.WriteLine("Data partitioned into {0} job{1}.", _jobs.Count, _jobs.Count == 1 ? "" : "s");
_centers = new List <LabelVector>();
UpdateManager.WriteLine("Initializing centers...");
UpdateManager.AddIndent();
if (data.Count > PARALLEL_THRESHOLD)
{
seedCentersParallel();
}
else
{
seedCenters();
}
UpdateManager.RemoveIndent();
for (short label = 0; label < _centers.Count; label++)
{
_centers[label].Label = label;
}
UpdateManager.WriteLine("Running Lloyd's algorithm...");
Stopwatch sw = new Stopwatch();
UpdateManager.Write("Building initial clusters...");
sw.Start();
while (runJobs(o => assignCenters(o)))
{
sw.Stop();
UpdateManager.WriteLine("Done [{0}ms]", sw.ElapsedMilliseconds);
for (int i = 0; i < _centers.Count; i++)
{
_centers[i] = new LabelVector(_data[0].Count);
}
sw.Reset();
UpdateManager.Write("Recomputing cluster centers...");
sw.Start();
for (int i = 0; i < data.Count; i++)
{
LabelVector x = data[i];
if (weights != null)
{
float featureValue = _centers[x.Label].FeatureValue;
_centers[x.Label] += weights[i] * x;
_centers[x.Label].FeatureValue += weights[i];
}
else
{
float featureValue = _centers[x.Label].FeatureValue;
_centers[x.Label] += x;
_centers[x.Label].FeatureValue += 1;
}
}
for (short i = 0; i < _centers.Count; i++)
{
_centers[i] /= _centers[i].FeatureValue;
_centers[i].Label = i;
}
sw.Stop();
UpdateManager.WriteLine("Done [{0}ms]", sw.ElapsedMilliseconds);
sw.Reset();
UpdateManager.Write("Rebuilding clusters...");
sw.Start();
}
sw.Stop();
UpdateManager.WriteLine("Done and done [{0}ms]", sw.ElapsedMilliseconds);
UpdateManager.WriteLine("Fitting complete.");
}
private void seedCentersParallel()
{
UpdateManager.WriteLine("Performing scalable K-Means++ initialization...");
LabelVector center = _data.SelectRandom().Clone() as LabelVector;
center.Label = 0;
_centers.Add(center);
runJobs(o => calculatePhi(o));
_phi = _data.Sum(o => o.FeatureValue);
int N = (int)Math.Log(_phi);
if (MaxIterations != 0)
{
N = MaxIterations;
}
Stopwatch sw = new Stopwatch();
for (int i = 0; i < N; i++)
{
sw.Reset();
UpdateManager.Write("Gathering center candidates ({0} of {1})...", i + 1, N);
sw.Start();
List <LabelVector> newCenters = runJobs(new Func <object, List <LabelVector> >(findCenterCandidates));
_centers.AddRange(newCenters);
for (short s = 0; s < _centers.Count; s++)
{
_centers[s].Label = s;
}
sw.Stop();
UpdateManager.WriteLine("Done [{0}ms]", sw.ElapsedMilliseconds);
sw.Reset();
UpdateManager.Write("Recalculating phi...");
sw.Start();
runJobs(o => calculatePhi(o));
_phi = _data.Sum(o => o.FeatureValue);
sw.Stop();
UpdateManager.WriteLine("Done [{0}ms]", sw.ElapsedMilliseconds);
}
sw.Reset();
UpdateManager.Write("Assigning centers...");
sw.Start();
for (short s = 0; s < _centers.Count; s++)
{
_centers[s].Label = s;
}
runJobs(o => assignCenters(o));
sw.Stop();
UpdateManager.WriteLine("Done [{0}ms]", sw.ElapsedMilliseconds);
float[] weights = new float[_centers.Count];
foreach (LabelVector x in _data)
{
weights[x.Label] += 1;
}
KMeans clusterCenters = new KMeans(_k);
UpdateManager.AddIndent();
clusterCenters.Fit(_centers, weights);
UpdateManager.RemoveIndent();
_centers = clusterCenters.Centers;
}