Interface | Description |
---|---|
IterativeContext |
An interface for algorithms that proceed iteratively.
|
SettableTransformer<I,O> |
An interface for classes that can set the value to be returned (from
transform() )
when invoked on a given input. |
Class | Description |
---|---|
BasicMapEntry<K,V> |
An simple minimal implementation of
Map.Entry . |
DiscreteDistribution |
A utility class for calculating properties of discrete distributions.
|
Indexer |
A class providing static methods useful for improving the
performance of graph algorithms.
|
IterativeProcess |
Provides basic infrastructure for iterative algorithms.
|
KMeansClusterer<T> |
Groups items into a specified number of clusters, based on their proximity in
d-dimensional space, using the k-means algorithm.
|
MapBinaryHeap<T> |
An array-based binary heap implementation of a priority queue,
which also provides
efficient
update() and contains operations. |
MapSettableTransformer<I,O> |
A
SettableTransformer that operates on an underlying Map instance. |
SelfLoopEdgePredicate<V,E> |
A
Predicate that returns true if the input edge's
endpoints in the input graph are identical. |
WeightedChoice<T> |
Selects items according to their probability in an arbitrary probability
distribution.
|
Exception | Description |
---|---|
KMeansClusterer.NotEnoughClustersException |
An exception that indicates that the specified data points cannot be
clustered into the number of clusters requested by the user.
|
DiscreteDistribution
: calculates statistical measures on
discrete probability distributions represented as double
arrays
KMeansClusterer
: uses the k-means algorithm to cluster
points in d-dimensional space into k clusters
MapBinaryHeap
: a binary heap implementation that permits
efficient element access and update operations
RandomLocationTransformer
: a class that randomly assigns
2D coordinates to items (default initializer for iterative Layouts)
SettableTransformer
: an extension of Transformer
that allows mutation of the transformation
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