Package edu.uci.ics.jung.algorithms.util


package edu.uci.ics.jung.algorithms.util
Provides general algorithmic utilities. These include:
  • 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
  • Class
    Description
    An simple minimal implementation of Map.Entry.
    A utility class for calculating properties of discrete distributions.
    A class providing static methods useful for improving the performance of graph algorithms.
    An interface for algorithms that proceed iteratively.
    Provides basic infrastructure for iterative algorithms.
    Groups items into a specified number of clusters, based on their proximity in d-dimensional space, using the k-means algorithm.
    An exception that indicates that the specified data points cannot be clustered into the number of clusters requested by the user.
    An array-based binary heap implementation of a priority queue, which also provides efficient update() and contains operations.
    A SettableTransformer that operates on an underlying Map instance.
    A Predicate that returns true if the input edge's endpoints in the input graph are identical.
    An interface for classes that can set the value to be returned (from transform()) when invoked on a given input.
    Selects items according to their probability in an arbitrary probability distribution.