/// <summary>
/// Construct clusters using different update methods
/// </summary>
/// <param name="distanceMatrix">IDistanceMatrix</param>
/// <param name="updateDistanceMethodName">enum EUpdateDistanceMethods</param>
public HierarchicalClustering(IDistanceMatrix distanceMatrix, UpdateDistanceMethodsTypes updateDistanceMethodName)
{
if (distanceMatrix.Dimension <= 0)
{
throw new Exception("Invalid distance matrix dimension");
}
try
{
// The number of nodes in the final tree is 2N-2:
// N sequence nodes (leaves) and N-2 internal nodes
// where N is the number of input sequences
_nodes = new List <BinaryGuideTreeNode>(distanceMatrix.Dimension * 2 - 1);
_edges = new List <BinaryGuideTreeEdge>(distanceMatrix.Dimension * 2 - 2);
// The number of clusters is the number of leaves at the beginning
// As the algorithm merges clusters, only one cluster remains.
_clusters = new List <int>(distanceMatrix.Dimension);
// Construct _indexToCluster
_indexToCluster = new int[distanceMatrix.Dimension];
for (int i = 0; i < distanceMatrix.Dimension; ++i)
{
_indexToCluster[i] = i;
}
}
catch (OutOfMemoryException ex)
{
throw new Exception("Out of memory", ex.InnerException);
}
// Choose a update-distance method
switch (updateDistanceMethodName)
{
case (UpdateDistanceMethodsTypes.Average):
_updateDistanceMethod = new UpdateDistanceMethodSelector(UpdateAverage);
break;
case (UpdateDistanceMethodsTypes.Single):
_updateDistanceMethod = new UpdateDistanceMethodSelector(UpdateSingle);
break;
case (UpdateDistanceMethodsTypes.Complete):
_updateDistanceMethod = new UpdateDistanceMethodSelector(UpdateComplete);
break;
case (UpdateDistanceMethodsTypes.WeightedMAFFT):
_updateDistanceMethod = new UpdateDistanceMethodSelector(UpdateWeightedMAFFT);
break;
default:
throw new Exception("invalid update method");
}
}
/// <summary>
/// Construct clusters using different update methods
/// </summary>
/// <param name="distanceMatrix">IDistanceMatrix</param>
/// <param name="updateDistanceMethodName">enum EUpdateDistanceMethods</param>
public HierarchicalClusteringParallel(IDistanceMatrix distanceMatrix, UpdateDistanceMethodsTypes updateDistanceMethodName)
{
if (distanceMatrix.Dimension <= 0)
{
throw new Exception("Invalid distance matrix dimension");
}
// The number of nodes in the final tree is 2N-2:
// N sequence nodes (leaves) and N-2 internal nodes
// where N is the number of input sequences
_nodes = new List <BinaryGuideTreeNode>(distanceMatrix.Dimension * 2 - 2);
_edges = new List <BinaryGuideTreeEdge>();
// The number of clusters is the number of leaves at the beginning
// As the algorithm merges clusters, only one cluster remains.
_clusters = new List <int>(distanceMatrix.Dimension);
// Choose a update-distance method
switch (updateDistanceMethodName)
{
case (UpdateDistanceMethodsTypes.Aaverage):
_updateDistanceMethod = new UpdateDistanceMethodSelector(UpdateAverage);
break;
case (UpdateDistanceMethodsTypes.Single):
_updateDistanceMethod = new UpdateDistanceMethodSelector(UpdateSingle);
break;
case (UpdateDistanceMethodsTypes.Complete):
_updateDistanceMethod = new UpdateDistanceMethodSelector(UpdateComplete);
break;
case (UpdateDistanceMethodsTypes.WeightedMAFFT):
_updateDistanceMethod = new UpdateDistanceMethodSelector(UpdateWeightedMAFFT);
break;
default:
throw new Exception("invalid update method");
}
// Initialize the clusters
Initialize(distanceMatrix);
// Clustering...
while (_numberOfClusters > 1)
{
GetNextPairOfCluster(distanceMatrix);
CreateCluster();
UpdateDistance(distanceMatrix);
UpdateClusters();
}
}
/// <summary>
/// Construct clusters using different update methods
/// </summary>
/// <param name="distanceMatrix">IDistanceMatrix</param>
/// <param name="updateDistanceMethodName">enum EUpdateDistanceMethods</param>
public HierarchicalClustering(IDistanceMatrix distanceMatrix, UpdateDistanceMethodsTypes updateDistanceMethodName)
{
if (distanceMatrix.Dimension <= 0)
{
throw new Exception("Invalid distance matrix dimension");
}
try
{
// The number of nodes in the final tree is 2N-2:
// N sequence nodes (leaves) and N-2 internal nodes
// where N is the number of input sequences
_nodes = new List<BinaryGuideTreeNode>(distanceMatrix.Dimension * 2 - 1);
_edges = new List<BinaryGuideTreeEdge>(distanceMatrix.Dimension * 2 - 2);
// The number of clusters is the number of leaves at the beginning
// As the algorithm merges clusters, only one cluster remains.
_clusters = new List<int>(distanceMatrix.Dimension);
// Construct _indexToCluster
_indexToCluster = new int[distanceMatrix.Dimension];
for (int i = 0; i < distanceMatrix.Dimension; ++i)
{
_indexToCluster[i] = i;
}
}
catch (OutOfMemoryException ex)
{
throw new Exception("Out of memory", ex.InnerException);
}
// Choose a update-distance method
switch(updateDistanceMethodName)
{
case(UpdateDistanceMethodsTypes.Average):
_updateDistanceMethod = new UpdateDistanceMethodSelector(UpdateAverage);
break;
case(UpdateDistanceMethodsTypes.Single):
_updateDistanceMethod = new UpdateDistanceMethodSelector(UpdateSingle);
break;
case(UpdateDistanceMethodsTypes.Complete):
_updateDistanceMethod = new UpdateDistanceMethodSelector(UpdateComplete);
break;
case(UpdateDistanceMethodsTypes.WeightedMAFFT):
_updateDistanceMethod = new UpdateDistanceMethodSelector(UpdateWeightedMAFFT);
break;
default:
throw new Exception("invalid update method");
}
}
