- 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 Edge
s 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.