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K

KEY - Static variable in class edu.uci.ics.jung.algorithms.importance.BaryCenter
 
KEY - Static variable in class edu.uci.ics.jung.algorithms.importance.DegreeDistributionRanker
 
KEY - Static variable in class edu.uci.ics.jung.algorithms.importance.PageRank
 
key - Variable in class edu.uci.ics.jung.graph.decorators.UserDatumNumberEdgeValue
 
key - Variable in class edu.uci.ics.jung.graph.decorators.UserDatumNumberVertexValue
 
key - Variable in class edu.uci.ics.jung.graph.predicates.ContainsUserDataKeyVertexPredicate
 
key - Variable in class edu.uci.ics.jung.visualization.PersistentLayoutImpl
a key for this class
key_meta_map - Static variable in class edu.uci.ics.jung.utils.UnifiedUserData
 
keySet() - Method in class scratch.joshua.jung_2_0.decoration.TestMap
 
KKLayout - Class in edu.uci.ics.jung.visualization.contrib
Implements the Kamada-Kawai algorithm for node layout.
KKLayout(Graph) - Constructor for class edu.uci.ics.jung.visualization.contrib.KKLayout
 
KKLayout(Graph, Distance) - Constructor for class edu.uci.ics.jung.visualization.contrib.KKLayout
 
KKLayout - Class in samples.preview_new_graphdraw.iterablelayouts
Implements the Kamada-Kawai algorithm for node layout.
KKLayout() - Constructor for class samples.preview_new_graphdraw.iterablelayouts.KKLayout
 
KKLayout(double) - Constructor for class samples.preview_new_graphdraw.iterablelayouts.KKLayout
 
KKLayoutInt - Class in edu.uci.ics.jung.visualization.contrib
Implements the Kamada-Kawai algorithm for node layout, tweaked to store vertex distances as integers.
KKLayoutInt(Graph) - Constructor for class edu.uci.ics.jung.visualization.contrib.KKLayoutInt
 
KleinbergSmallWorldGenerator - Class in edu.uci.ics.jung.random.generators
Graph generator that produces a random graph with small world properties.
KleinbergSmallWorldGenerator(int, double) - Constructor for class edu.uci.ics.jung.random.generators.KleinbergSmallWorldGenerator
Constructs the small world graph generator.
kmc - Variable in class edu.uci.ics.jung.algorithms.cluster.VoltageClusterer
 
kmc - Variable in class test.edu.uci.ics.jung.algorithms.cluster.KMeansTest
 
KMeansClusterer - Class in edu.uci.ics.jung.algorithms.cluster
Groups Objects into a specified number of clusters, based on their proximity in d-dimensional space, using the k-means algorithm.
KMeansClusterer(int, double) - Constructor for class edu.uci.ics.jung.algorithms.cluster.KMeansClusterer
Creates an instance for which calls to cluster will terminate when either of the two following conditions is true:
  • the number of iterations is > max_iterations
  • none of the centroids has moved as much as convergence_threshold since the previous iteration
    KMeansClusterer.NotEnoughClustersException - Exception in edu.uci.ics.jung.algorithms.cluster
    An exception that indicates that the specified data points cannot be clustered into the number of clusters requested by the user.
    KMeansClusterer.NotEnoughClustersException() - Constructor for exception edu.uci.ics.jung.algorithms.cluster.KMeansClusterer.NotEnoughClustersException
     
    KMeansTest - Class in test.edu.uci.ics.jung.algorithms.cluster
     
    KMeansTest() - Constructor for class test.edu.uci.ics.jung.algorithms.cluster.KMeansTest
     
    KNeighborhoodExtractor - Class in edu.uci.ics.jung.algorithms.connectivity
    Extracts the subgraph (neighborhood) from a graph whose nodes are no more than distance k away from at least one of the root nodes (starting vertices).
    KNeighborhoodExtractor() - Constructor for class edu.uci.ics.jung.algorithms.connectivity.KNeighborhoodExtractor
     
    KNeighborhoodFilter - Class in edu.uci.ics.jung.graph.filters.impl
    A filter used to extract the k-neighborhood around one or more root node(s)
    KNeighborhoodFilter(Set, int, int) - Constructor for class edu.uci.ics.jung.graph.filters.impl.KNeighborhoodFilter
    Constructs a new instance of the filter
    KNeighborhoodFilter(Vertex, int, int) - Constructor for class edu.uci.ics.jung.graph.filters.impl.KNeighborhoodFilter
    Constructs a new instance of the filter
    KPartiteEdgePredicate - Class in edu.uci.ics.jung.graph.predicates
    An edge predicate that passes Edges whose endpoints satisfy distinct elements of the Predicate collection passed in as a parameter to the constructor.
    KPartiteEdgePredicate(Collection) - Constructor for class edu.uci.ics.jung.graph.predicates.KPartiteEdgePredicate
     
    KPartiteGraph - Interface in edu.uci.ics.jung.graph
    An interface for k-partite graphs.
    KPartiteSparseGraph - Class in edu.uci.ics.jung.graph.impl
    An implementation of KPartiteGraph based on SparseGraph.
    KPartiteSparseGraph(Collection, boolean) - Constructor for class edu.uci.ics.jung.graph.impl.KPartiteSparseGraph
    Creates a KPartiteSparseGraph whose partitions are specified by the predicates in the partitions array.
    KPartiteSparseGraph(Graph, Collection, boolean) - Constructor for class edu.uci.ics.jung.graph.impl.KPartiteSparseGraph
    Creates a new KPartiteSparseGraph which contains all the vertices and edges in g.
    KPartiteTaggerTest - Class in test.edu.uci.ics.jung.algorithms.transformations
     
    KPartiteTaggerTest() - Constructor for class test.edu.uci.ics.jung.algorithms.transformations.KPartiteTaggerTest
     
    KPartiteTest - Class in test.edu.uci.ics.jung.graph.impl
     
    KPartiteTest() - Constructor for class test.edu.uci.ics.jung.graph.impl.KPartiteTest
     
    KStepMarkov - Class in edu.uci.ics.jung.algorithms.importance
    Algorithm variant of PageRankWithPriors that computes the importance of a node based upon taking fixed-length random walks out from the root set and then computing the stationary probability of being at each node.
    KStepMarkov(DirectedGraph, Set, int, String) - Constructor for class edu.uci.ics.jung.algorithms.importance.KStepMarkov
    Construct the algorihm instance and initializes the algorithm.
    KullbackLeibler(double[], double[]) - Static method in class edu.uci.ics.jung.statistics.DiscreteDistribution
    Returns the Kullback-Leibler divergence between the two specified distributions, which must have the same number of elements.
    kurtosis() - Method in class edu.uci.ics.jung.statistics.Histogram
    Returns the kurtosis of the values accumulated in the histogram bins.
    kurtosis() - Method in class edu.uci.ics.jung.statistics.StatisticalMoments
    The kurtosis measures the sharpness of the distribution near the maximum.
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