public void ClusterNoDataObjects()
{
var nj = new NeighborJoiningClusterer <char>((o1, o2) => 0);
IUndirectedGraph <Cluster <char>, ClusterEdge <char> > tree = nj.GenerateClusters(Enumerable.Empty <char>());
Assert.That(tree.IsEdgesEmpty);
}
public void ClusterOneDataObject()
{
var nj = new NeighborJoiningClusterer<char>((o1, o2) => 0);
IUndirectedGraph<Cluster<char>, ClusterEdge<char>> tree = nj.GenerateClusters(new[] {'A'});
Assert.That(tree.VertexCount, Is.EqualTo(1));
Assert.That(tree.IsEdgesEmpty);
}
public void Cluster()
{
var matrix = new double[, ]
{
{ 0, 1, 2, 3, 3 },
{ 1, 0, 2, 3, 3 },
{ 2, 2, 0, 3, 3 },
{ 3, 3, 3, 0, 1 },
{ 3, 3, 3, 1, 0 }
};
var nj = new NeighborJoiningClusterer <char>((o1, o2) => matrix[o1 - 'A', o2 - 'A']);
IUndirectedGraph <Cluster <char>, ClusterEdge <char> > tree = nj.GenerateClusters(new[] { 'A', 'B', 'C', 'D', 'E' });
var vertices = new Dictionary <string, Cluster <char> >
{
{ "root", new Cluster <char> {
Description = "root"
} },
{ "A", new Cluster <char>('A')
{
Description = "A"
} },
{ "B", new Cluster <char>('B')
{
Description = "B"
} },
{ "C", new Cluster <char>('C')
{
Description = "C"
} },
{ "D", new Cluster <char>('D')
{
Description = "D"
} },
{ "E", new Cluster <char>('E')
{
Description = "E"
} },
{ "DE", new Cluster <char> {
Description = "DE"
} },
{ "AB", new Cluster <char> {
Description = "AB"
} }
};
var edges = new[]
{
new ClusterEdge <char>(vertices["root"], vertices["C"], 1.0),
new ClusterEdge <char>(vertices["root"], vertices["DE"], 1.5),
new ClusterEdge <char>(vertices["root"], vertices["AB"], 0.5),
new ClusterEdge <char>(vertices["DE"], vertices["D"], 0.5),
new ClusterEdge <char>(vertices["DE"], vertices["E"], 0.5),
new ClusterEdge <char>(vertices["AB"], vertices["A"], 0.5),
new ClusterEdge <char>(vertices["AB"], vertices["B"], 0.5)
};
AssertTreeEqual(tree, edges.ToUndirectedGraph <Cluster <char>, ClusterEdge <char> >(false));
}
public void ClusterOneDataObject()
{
var nj = new NeighborJoiningClusterer <char>((o1, o2) => 0);
IUndirectedGraph <Cluster <char>, ClusterEdge <char> > tree = nj.GenerateClusters(new[] { 'A' });
Assert.That(tree.VertexCount, Is.EqualTo(1));
Assert.That(tree.IsEdgesEmpty);
}
public void ClusterTwoDataObjects()
{
var nj = new NeighborJoiningClusterer <char>((o1, o2) => 1);
IUndirectedGraph <Cluster <char>, ClusterEdge <char> > tree = nj.GenerateClusters(new[] { 'A', 'B' });
var edges = new[]
{
new ClusterEdge <char>(new Cluster <char>('A'), new Cluster <char>('B'), 1.0)
};
AssertTreeEqual(tree, edges.ToUndirectedGraph <Cluster <char>, ClusterEdge <char> >());
}
public void ClusterTwoDataObjects()
{
var nj = new NeighborJoiningClusterer<char>((o1, o2) => 1);
IUndirectedGraph<Cluster<char>, ClusterEdge<char>> tree = nj.GenerateClusters(new[] {'A', 'B'});
var edges = new[]
{
new ClusterEdge<char>(new Cluster<char>('A'), new Cluster<char>('B'), 1.0)
};
AssertTreeEqual(tree, edges.ToUndirectedGraph<Cluster<char>, ClusterEdge<char>>());
}
public void Cluster()
{
var matrix = new double[,]
{
{0, 1, 2, 3, 3},
{1, 0, 2, 3, 3},
{2, 2, 0, 3, 3},
{3, 3, 3, 0, 1},
{3, 3, 3, 1, 0}
};
var nj = new NeighborJoiningClusterer<char>((o1, o2) => matrix[o1 - 'A', o2 - 'A']);
IUndirectedGraph<Cluster<char>, ClusterEdge<char>> tree = nj.GenerateClusters(new[] {'A', 'B', 'C', 'D', 'E'});
var vertices = new Dictionary<string, Cluster<char>>
{
{"root", new Cluster<char> {Description = "root"}},
{"A", new Cluster<char>('A') {Description = "A"}},
{"B", new Cluster<char>('B') {Description = "B"}},
{"C", new Cluster<char>('C') {Description = "C"}},
{"D", new Cluster<char>('D') {Description = "D"}},
{"E", new Cluster<char>('E') {Description = "E"}},
{"DE", new Cluster<char> {Description = "DE"}},
{"AB", new Cluster<char> {Description = "AB"}}
};
var edges = new[]
{
new ClusterEdge<char>(vertices["root"], vertices["C"], 1.0),
new ClusterEdge<char>(vertices["root"], vertices["DE"], 1.5),
new ClusterEdge<char>(vertices["root"], vertices["AB"], 0.5),
new ClusterEdge<char>(vertices["DE"], vertices["D"], 0.5),
new ClusterEdge<char>(vertices["DE"], vertices["E"], 0.5),
new ClusterEdge<char>(vertices["AB"], vertices["A"], 0.5),
new ClusterEdge<char>(vertices["AB"], vertices["B"], 0.5)
};
AssertTreeEqual(tree, edges.ToUndirectedGraph<Cluster<char>, ClusterEdge<char>>(false));
}
public void Compute()
{
var clusterer = new NeighborJoiningClusterer <TSeq>((seq1, seq2) =>
{
var pairwiseAligner = new PairwiseAlignmentAlgorithm <TSeq, TItem>(_scorer, seq1, seq2, _itemsSelector);
pairwiseAligner.Compute();
return(1.0 - pairwiseAligner.GetAlignments().First().NormalizedScore);
});
IUndirectedGraph <Cluster <TSeq>, ClusterEdge <TSeq> > unrootedTree = clusterer.GenerateClusters(_sequences);
IBidirectionalGraph <Cluster <TSeq>, ClusterEdge <TSeq> > rootedTree = unrootedTree.ToRootedTree();
var profiles = new Dictionary <Cluster <TSeq>, Profile <TSeq, TItem> >();
var nodeStack = new Stack <Cluster <TSeq> >();
Cluster <TSeq> root = rootedTree.Roots().First();
double maxWeight = double.MinValue;
if (root.DataObjects.Count == 1)
{
profiles[root] = CreateProfile(root.DataObjects.First(), 0);
maxWeight = 0;
}
nodeStack.Push(root);
foreach (ClusterEdge <TSeq> edge in rootedTree.OutEdges(root))
{
maxWeight = Math.Max(maxWeight, CalcSequenceWeights(rootedTree, edge, 0, nodeStack, profiles));
}
foreach (Profile <TSeq, TItem> profile in profiles.Values)
{
profile.Weights[0] += 1.0 - maxWeight;
}
var scorer = new ProfileScorer <TSeq, TItem>(_scorer);
while (nodeStack.Count > 0)
{
Cluster <TSeq> node = nodeStack.Pop();
var curProfiles = new Stack <Profile <TSeq, TItem> >();
foreach (ClusterEdge <TSeq> childEdge in rootedTree.OutEdges(node))
{
curProfiles.Push(profiles[childEdge.Target]);
profiles.Remove(childEdge.Target);
}
if (node.DataObjects.Count == 1)
{
curProfiles.Push(profiles[node]);
profiles.Remove(node);
}
while (curProfiles.Count > 1)
{
Profile <TSeq, TItem> profile1 = curProfiles.Pop();
Profile <TSeq, TItem> profile2 = curProfiles.Pop();
var profileAligner = new PairwiseAlignmentAlgorithm <Profile <TSeq, TItem>, AlignmentCell <TItem>[]>(scorer, profile1, profile2, GetProfileItems);
profileAligner.Compute();
Alignment <Profile <TSeq, TItem>, AlignmentCell <TItem>[]> profileAlignment = profileAligner.GetAlignments().First();
var sequences = new List <Tuple <TSeq, AlignmentCell <TItem>, IEnumerable <AlignmentCell <TItem> >, AlignmentCell <TItem> > >();
for (int i = 0; i < profile1.Alignment.SequenceCount; i++)
{
int seq = i;
sequences.Add(Tuple.Create(profile1.Alignment.Sequences[i], profile1.Alignment.Prefixes[i], Enumerable.Range(0, profileAlignment.ColumnCount)
.Select(col => profileAlignment[0, col].IsNull ? new AlignmentCell <TItem>() : profileAlignment[0, col][0][seq]), profile1.Alignment.Suffixes[i]));
}
for (int j = 0; j < profile2.Alignment.SequenceCount; j++)
{
int seq = j;
sequences.Add(Tuple.Create(profile2.Alignment.Sequences[j], profile2.Alignment.Prefixes[j], Enumerable.Range(0, profileAlignment.ColumnCount)
.Select(col => profileAlignment[1, col].IsNull ? new AlignmentCell <TItem>() : profileAlignment[1, col][0][seq]), profile2.Alignment.Suffixes[j]));
}
var newAlignment = new Alignment <TSeq, TItem>(profileAlignment.RawScore, profileAlignment.NormalizedScore, sequences);
curProfiles.Push(new Profile <TSeq, TItem>(newAlignment, profile1.Weights.Concat(profile2.Weights)));
}
profiles[node] = curProfiles.Pop();
}
Alignment <TSeq, TItem> alignment = profiles[root].Alignment;
if (UseInputOrder)
{
var reorderedSequences = new List <Tuple <TSeq, AlignmentCell <TItem>, IEnumerable <AlignmentCell <TItem> >, AlignmentCell <TItem> > >();
foreach (TSeq sequence in _sequences)
{
for (int i = 0; i < alignment.SequenceCount; i++)
{
int seq = i;
if (sequence.Equals(alignment.Sequences[seq]))
{
reorderedSequences.Add(Tuple.Create(sequence, alignment.Prefixes[seq], Enumerable.Range(0, alignment.ColumnCount).Select(col => alignment[seq, col]), alignment.Suffixes[seq]));
break;
}
}
}
_result = new Alignment <TSeq, TItem>(alignment.RawScore, alignment.NormalizedScore, reorderedSequences);
}
else
{
_result = alignment;
}
}
public IBidirectionalGraph<HierarchicalGraphVertex, HierarchicalGraphEdge> GenerateHierarchicalGraph(HierarchicalGraphType graphType,
ClusteringMethod clusteringMethod, SimilarityMetric similarityMetric)
{
switch (clusteringMethod)
{
case ClusteringMethod.Upgma:
Func<Variety, Variety, double> upgmaGetDistance = null;
switch (similarityMetric)
{
case SimilarityMetric.Lexical:
upgmaGetDistance = (v1, v2) => 1.0 - v1.VarietyPairs[v2].LexicalSimilarityScore;
break;
case SimilarityMetric.Phonetic:
upgmaGetDistance = (v1, v2) => 1.0 - v1.VarietyPairs[v2].PhoneticSimilarityScore;
break;
}
var upgma = new UpgmaClusterer<Variety>(upgmaGetDistance);
IBidirectionalGraph<Cluster<Variety>, ClusterEdge<Variety>> upgmaTree = upgma.GenerateClusters(_projectService.Project.Varieties);
return BuildHierarchicalGraph(upgmaTree);
case ClusteringMethod.NeighborJoining:
Func<Variety, Variety, double> njGetDistance = null;
switch (similarityMetric)
{
case SimilarityMetric.Lexical:
njGetDistance = (v1, v2) => 1.0 - v1.VarietyPairs[v2].LexicalSimilarityScore;
break;
case SimilarityMetric.Phonetic:
njGetDistance = (v1, v2) => 1.0 - v1.VarietyPairs[v2].PhoneticSimilarityScore;
break;
}
var nj = new NeighborJoiningClusterer<Variety>(njGetDistance);
IUndirectedGraph<Cluster<Variety>, ClusterEdge<Variety>> njTree = nj.GenerateClusters(_projectService.Project.Varieties);
switch (graphType)
{
case HierarchicalGraphType.Dendrogram:
IBidirectionalGraph<Cluster<Variety>, ClusterEdge<Variety>> rootedTree = njTree.ToRootedTree();
return BuildHierarchicalGraph(rootedTree);
case HierarchicalGraphType.Tree:
return BuildHierarchicalGraph(njTree);
}
break;
}
return null;
}
public IBidirectionalGraph <HierarchicalGraphVertex, HierarchicalGraphEdge> GenerateHierarchicalGraph(HierarchicalGraphType graphType,
ClusteringMethod clusteringMethod, SimilarityMetric similarityMetric)
{
switch (clusteringMethod)
{
case ClusteringMethod.Upgma:
Func <Variety, Variety, double> upgmaGetDistance = null;
switch (similarityMetric)
{
case SimilarityMetric.Lexical:
upgmaGetDistance = (v1, v2) => 1.0 - v1.VarietyPairs[v2].LexicalSimilarityScore;
break;
case SimilarityMetric.Phonetic:
upgmaGetDistance = (v1, v2) => 1.0 - v1.VarietyPairs[v2].PhoneticSimilarityScore;
break;
}
var upgma = new UpgmaClusterer <Variety>(upgmaGetDistance);
IBidirectionalGraph <Cluster <Variety>, ClusterEdge <Variety> > upgmaTree = upgma.GenerateClusters(_projectService.Project.Varieties);
return(BuildHierarchicalGraph(upgmaTree));
case ClusteringMethod.NeighborJoining:
Func <Variety, Variety, double> njGetDistance = null;
switch (similarityMetric)
{
case SimilarityMetric.Lexical:
njGetDistance = (v1, v2) => 1.0 - v1.VarietyPairs[v2].LexicalSimilarityScore;
break;
case SimilarityMetric.Phonetic:
njGetDistance = (v1, v2) => 1.0 - v1.VarietyPairs[v2].PhoneticSimilarityScore;
break;
}
var nj = new NeighborJoiningClusterer <Variety>(njGetDistance);
IUndirectedGraph <Cluster <Variety>, ClusterEdge <Variety> > njTree = nj.GenerateClusters(_projectService.Project.Varieties);
switch (graphType)
{
case HierarchicalGraphType.Dendrogram:
IBidirectionalGraph <Cluster <Variety>, ClusterEdge <Variety> > rootedTree = njTree.ToRootedTree();
return(BuildHierarchicalGraph(rootedTree));
case HierarchicalGraphType.Tree:
return(BuildHierarchicalGraph(njTree));
}
break;
}
return(null);
}
public void ClusterNoDataObjects()
{
var nj = new NeighborJoiningClusterer<char>((o1, o2) => 0);
IUndirectedGraph<Cluster<char>, ClusterEdge<char>> tree = nj.GenerateClusters(Enumerable.Empty<char>());
Assert.That(tree.IsEdgesEmpty);
}
