Package | Description |
---|---|
de.jstacs.clustering.distances | |
de.jstacs.clustering.hierachical |
Modifier and Type | Method and Description |
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static Pair<double[][],int[][]> |
DeBruijnMotifComparison.getClusterRepresentative(ClusterTree<StatisticalModel> tree,
int n)
Returns a position weight matrix (PWM) representation of the root node of the given cluster tree and
also computed the relative shifts of the motifs such that they align best with the consensus motif at the root.
|
Modifier and Type | Method and Description |
---|---|
ClusterTree<Integer> |
Hclust.cluster(double[][] distMat,
LinkedList<ClusterTree<Integer>> list,
int indexOff)
Further clusters the supplied cluster trees using the given distance matrix
and creating original indexes for the inner node starting at -indexOff-1 in descending order
|
ClusterTree<T> |
Hclust.cluster(double[][] distMat,
T... objects)
Clusters the given objects using the supplied distance matrix, which must be in the same
order as the elements provided in
objects . |
ClusterTree<T> |
Hclust.cluster(int indexOff,
double[][] distMat,
ClusterTree<T>[] leaves)
Clusters the given leaf trees using the supplied distance matrix
|
ClusterTree<T> |
Hclust.cluster(T... objects)
Clusters the supplied objects and return the resulting cluster tree.
|
ClusterTree<T> |
Hclust.createTree(ClusterTree<Integer> intTree,
T... objects)
Creates a cluster tree given an index tree using the original indexes refering to the indexes
of elements in
objects . |
static <T> ClusterTree<T>[] |
Hclust.cutTree(double distance,
ClusterTree<T> tree)
Cuts the cluster tree at the given distance and returns the sub-trees below the cut.
|
<S> ClusterTree<S> |
ClusterTree.dropBelow(IntList rootOriginalIndexes,
S[] newElements)
Removes all sub-trees below the inner nodes identified by the original indexes supplied
and creates new leaf nodes instead, which obtain the supplied leaf elements.
|
ClusterTree<Integer> |
ClusterTree.getIndexTree()
Returns a cluster tree with identical structure as this cluster tree but with all leaves replaced by
integer leaves holding the corresponding original indices.
|
ClusterTree<T>[] |
ClusterTree.getLeaves()
Returns all leaves of this cluster tree as
ClusterTree objects comprising just the corresponding
leaf element |
ClusterTree<T>[] |
ClusterTree.getSubTrees()
Returns the sub-trees of this cluster tree root node
|
Modifier and Type | Method and Description |
---|---|
ClusterTree<T> |
Hclust.cluster(int indexOff,
double[][] distMat,
ClusterTree<T>[] leaves)
Clusters the given leaf trees using the supplied distance matrix
|
ClusterTree<T> |
Hclust.createTree(ClusterTree<Integer> intTree,
T... objects)
Creates a cluster tree given an index tree using the original indexes refering to the indexes
of elements in
objects . |
static <T> T[][] |
Hclust.cutTree(ClusterTree<T> tree,
double distance)
Cuts the cluster tree at the specified distance and returns the leaf elements
grouped by their origin in the sub-trees below the cut
|
static <T> ClusterTree<T>[] |
Hclust.cutTree(double distance,
ClusterTree<T> tree)
Cuts the cluster tree at the given distance and returns the sub-trees below the cut.
|
double |
Hclust.getDistance(double[][] distMat,
ClusterTree<Integer> tree,
ClusterTree<Integer> tree2)
Returns the distance between the two supplied trees using the linkage method of this
Hclust object
and the given distance matrix. |
double |
Hclust.getDistance(double[][] distMat,
ClusterTree<Integer> tree,
ClusterTree<Integer> tree2)
Returns the distance between the two supplied trees using the linkage method of this
Hclust object
and the given distance matrix. |
Modifier and Type | Method and Description |
---|---|
ClusterTree<Integer> |
Hclust.cluster(double[][] distMat,
LinkedList<ClusterTree<Integer>> list,
int indexOff)
Further clusters the supplied cluster trees using the given distance matrix
and creating original indexes for the inner node starting at -indexOff-1 in descending order
|
Constructor and Description |
---|
ClusterTree(double distance,
int originalIndex,
ClusterTree<T>... subTrees)
Creates a new cluster tree with supplied sub-trees and given distance.
|