private static Func <int, int, double> DetermineInternalDistanceFunc <T>(HdbscanParameters <T> parameters,
IReadOnlyDictionary <int, double> sparseDistance,
int numPoints)
{
// Sparse matrix with caching.
if (sparseDistance != null)
{
return (a, b) =>
{
if (a < b)
{
return sparseDistance.ContainsKey(a * numPoints + b) ? sparseDistance[a * numPoints + b] : 1;
}
return sparseDistance.ContainsKey(b * numPoints + a) ? sparseDistance[b * numPoints + a] : 1;
}
}
;
// Normal matrix with caching.
if (parameters.CacheDistance)
{
return((a, b) => parameters.Distances[a][b]);
}
// No cache
return((a, b) =>
parameters.DistanceFunction.ComputeDistance(a, b, parameters.DataSet[a], parameters.DataSet[b]));
}
public static HdbscanResult Run <T>(HdbscanParameters <T> parameters)
{
var numPoints = parameters.DataSet?.Length ?? parameters.Distances.Length;
PrecomputeNormalMatrixDistancesIfApplicable(parameters, numPoints);
var sparseDistance = PrecomputeSparseMatrixDistancesIfApplicable(parameters, numPoints);
var internalDistanceFunc = DetermineInternalDistanceFunc(parameters, sparseDistance, numPoints);
// Compute core distances
var coreDistances = HdbscanAlgorithm.CalculateCoreDistances(
internalDistanceFunc,
numPoints,
parameters.MinPoints);
// Calculate minimum spanning tree
var mst = HdbscanAlgorithm.ConstructMst(
internalDistanceFunc,
numPoints,
coreDistances,
true);
mst.QuicksortByEdgeWeight();
var pointNoiseLevels = new double[numPoints];
var pointLastClusters = new int[numPoints];
var hierarchy = new List <int[]>();
// Compute hierarchy and cluster tree
var clusters = HdbscanAlgorithm.ComputeHierarchyAndClusterTree(
mst,
parameters.MinClusterSize,
parameters.Constraints,
hierarchy,
pointNoiseLevels,
pointLastClusters);
// Propagate clusters
var infiniteStability = HdbscanAlgorithm.PropagateTree(clusters);
// Compute final flat partitioning
var prominentClusters = HdbscanAlgorithm.FindProminentClusters(
clusters,
hierarchy,
numPoints);
// Compute outlier scores for each point
var scores = HdbscanAlgorithm.CalculateOutlierScores(
clusters,
pointNoiseLevels,
pointLastClusters,
coreDistances);
return(new HdbscanResult
{
Labels = prominentClusters,
OutliersScore = scores,
HasInfiniteStability = infiniteStability
});
}
public static HdbscanResult Run(HdbscanParameters parameters)
{
var numPoints = parameters.DataSet != null
? parameters.DataSet.Length
: parameters.Distances.Length;
if (parameters.Distances == null)
{
// Precompute distances.
var distances = new double[numPoints][];
for (var i = 0; i < distances.Length; i++)
{
distances[i] = new double[numPoints];
}
if (parameters.UseMultipleThread)
{
var size = numPoints * numPoints;
var maxDegreeOfParallelism = parameters.MaxDegreeOfParallelism;
if (maxDegreeOfParallelism == 0)
{
// Not specified. Use all threads.
maxDegreeOfParallelism = Environment.ProcessorCount;
}
var option = new ParallelOptions {
MaxDegreeOfParallelism = Math.Max(1, maxDegreeOfParallelism)
};
Parallel.For(0, size, option, index =>
{
var i = index % numPoints;
var j = index / numPoints;
if (i < j)
{
var distance = parameters.DistanceFunction.ComputeDistance(
parameters.DataSet[i],
parameters.DataSet[j]);
distances[i][j] = distance;
distances[j][i] = distance;
}
});
}
else
{
for (var i = 0; i < numPoints; i++)
{
for (var j = 0; j < i; j++)
{
var distance = parameters.DistanceFunction.ComputeDistance(
parameters.DataSet[i],
parameters.DataSet[j]);
distances[i][j] = distance;
distances[j][i] = distance;
}
}
}
parameters.Distances = distances;
}
// Compute core distances
var coreDistances = HdbscanAlgorithm.CalculateCoreDistances(
parameters.Distances,
parameters.MinPoints);
// Calculate minimum spanning tree
var mst = HdbscanAlgorithm.ConstructMst(
parameters.Distances,
coreDistances,
true);
mst.QuicksortByEdgeWeight();
var pointNoiseLevels = new double[numPoints];
var pointLastClusters = new int[numPoints];
var hierarchy = new List <int[]>();
// Compute hierarchy and cluster tree
var clusters = HdbscanAlgorithm.ComputeHierarchyAndClusterTree(
mst,
parameters.MinClusterSize,
parameters.Constraints,
hierarchy,
pointNoiseLevels,
pointLastClusters);
// Propagate clusters
var infiniteStability = HdbscanAlgorithm.PropagateTree(clusters);
// Compute final flat partitioning
var prominentClusters = HdbscanAlgorithm.FindProminentClusters(
clusters,
hierarchy,
numPoints);
// Compute outlier scores for each point
var scores = HdbscanAlgorithm.CalculateOutlierScores(
clusters,
pointNoiseLevels,
pointLastClusters,
coreDistances);
return(new HdbscanResult
{
Labels = prominentClusters,
OutliersScore = scores,
HasInfiniteStability = infiniteStability
});
}
private static Dictionary <int, double> PrecomputeSparseMatrixDistancesIfApplicable <T>(
HdbscanParameters <T> parameters, int numPoints)
{
Dictionary <int, double> sparseDistance = null;
if (parameters.Distances == null && parameters.CacheDistance &&
parameters.DataSet is Dictionary <int, int>[])
{
sparseDistance = new Dictionary <int, double>();
if (parameters.DistanceFunction is not ISparseMatrixSupport sparseMatrixSupport)
{
throw new NotSupportedException("The distance function used does not support sparse matrix.");
}
var mostCommonDistanceValueForSparseMatrix = sparseMatrixSupport.GetMostCommonDistanceValueForSparseMatrix();
if (parameters.MaxDegreeOfParallelism is 0 or > 1)
{
var size = numPoints * numPoints;
var maxDegreeOfParallelism = parameters.MaxDegreeOfParallelism;
if (maxDegreeOfParallelism == 0)
{
// Not specified. Use all threads.
maxDegreeOfParallelism = Environment.ProcessorCount;
}
var option = new ParallelOptions
{
MaxDegreeOfParallelism = Math.Max(1, maxDegreeOfParallelism)
};
Parallel.For(0, option.MaxDegreeOfParallelism, option, indexThread =>
{
var distanceThread = new Dictionary <int, double>();
for (var index = 0; index < size; index++)
{
if (index % option.MaxDegreeOfParallelism != indexThread)
{
continue;
}
var i = index % numPoints;
var j = index / numPoints;
if (i >= j)
{
continue;
}
var distance = parameters.DistanceFunction.ComputeDistance(
i,
j,
parameters.DataSet[i],
parameters.DataSet[j]);
// ReSharper disable once CompareOfFloatsByEqualityOperator
if (distance != mostCommonDistanceValueForSparseMatrix)
{
distanceThread.Add(i * numPoints + j, distance);
}
}
lock (sparseDistance)
foreach (var d in distanceThread)
{
sparseDistance.Add(d.Key, d.Value);
}
});
}
else
{
for (var i = 0; i < numPoints; i++)
{
for (var j = 0; j < i; j++)
{
var distance = parameters.DistanceFunction.ComputeDistance(
i,
j,
parameters.DataSet[i],
parameters.DataSet[j]);
// ReSharper disable once CompareOfFloatsByEqualityOperator
if (distance != mostCommonDistanceValueForSparseMatrix)
{
sparseDistance.Add(i * numPoints + j, distance);
}
}
}
}
}
