Uses of Class
netkit.classifiers.active.PickLabelStrategy.LabelNode

Packages that use PickLabelStrategy.LabelNode

netkit.classifiers.active
netkit.classifiers.active.graphfunctions

Uses of PickLabelStrategy.LabelNode in netkit.classifiers.active

Methods in netkit.classifiers.active that return PickLabelStrategy.LabelNode

PickLabelStrategy.LabelNode[]	PickLabelStrategyImp.getNodesToLabel(DataSplit currentSplit, Estimate currentPredictions, int maxPicks) Get the list of nodes to get labels for.
PickLabelStrategy.LabelNode[]	PickLabelStrategy.getNodesToLabel(DataSplit currentSplit, Estimate currentPredictions, int maxPicks) Get the list of nodes to get labels for.
PickLabelStrategy.LabelNode[]	UncertaintyLabeling.peek(DataSplit split, Estimate predictions, int maxPicks)
PickLabelStrategy.LabelNode[]	PickLabelStrategyImp.peek(DataSplit currentSplit, Estimate currentPredictions, int numPeek)
PickLabelStrategy.LabelNode[]	PickLabelStrategy.peek(DataSplit currentSplit, Estimate currentPredictions, int numPeek) Get the list of nodes to get labels for...
PickLabelStrategy.LabelNode[]	GreedyTruth.peek(DataSplit split, Estimate predictions, int maxPicks)
PickLabelStrategy.LabelNode[]	GraphCentralityLabeling.peek(DataSplit split, Estimate predictions, int maxPicks)
PickLabelStrategy.LabelNode[]	EmpiricalRiskMinimizationHarmonic.peek(DataSplit split, Estimate predictions, int maxPicks)
protected PickLabelStrategy.LabelNode[]	UncertaintyLabeling.pickNodes(Estimate predictions, int maxPicks)
protected PickLabelStrategy.LabelNode[]	RandomLabeling.pickNodes(Estimate predictions, int maxPicks)
protected abstract PickLabelStrategy.LabelNode[]	PickLabelStrategyImp.pickNodes(Estimate predictions, int maxPicks) Get the list of nodes to get labels for.
protected PickLabelStrategy.LabelNode[]	GreedyTruth.pickNodes(Estimate predictions, int maxPicks) Get the next nodes to label based on the empirical risk minimization principle.
protected PickLabelStrategy.LabelNode[]	GraphCentralityLabeling.pickNodes(Estimate predictions, int maxPicks) Picks the next nodes as the ones with the highest closeness centrality (normalized by cluster size) in a cluster that has no known labels.
protected PickLabelStrategy.LabelNode[]	ERMHybrid.pickNodes(Estimate predictions, int maxPicks) Maxpicks are ignored.
protected PickLabelStrategy.LabelNode[]	EmpiricalRiskMinimizationHarmonic.pickNodes(Estimate predictions, int maxPicks) Get the next nodes to label based on the empirical risk minimization principle.
protected PickLabelStrategy.LabelNode[]	EmpiricalRiskMinimization.pickNodes(Estimate predictions, int maxPicks) Get the next nodes to label based on the empirical risk minimization principle.
protected PickLabelStrategy.LabelNode[]	ComparatorLabeler.pickNodes(Estimate predictions, int maxPicks) Maxpicks are ignored.

Methods in netkit.classifiers.active with parameters of type PickLabelStrategy.LabelNode

int	PickLabelStrategy.LabelNode.compareTo(PickLabelStrategy.LabelNode ln)
protected double	PickLabelStrategyImp.getAverageRank(java.util.List<? extends PickLabelStrategy.LabelNode> list, PickLabelStrategy.LabelNode target)

Method parameters in netkit.classifiers.active with type arguments of type PickLabelStrategy.LabelNode

protected double	PickLabelStrategyImp.getAverageRank(java.util.List<? extends PickLabelStrategy.LabelNode> sortedlist, int targetIndex)
protected double	PickLabelStrategyImp.getAverageRank(java.util.List<? extends PickLabelStrategy.LabelNode> list, PickLabelStrategy.LabelNode target)

Uses of PickLabelStrategy.LabelNode in netkit.classifiers.active.graphfunctions

Methods in netkit.classifiers.active.graphfunctions with parameters of type PickLabelStrategy.LabelNode

int	ScoringFunction.compare(PickLabelStrategy.LabelNode n1, PickLabelStrategy.LabelNode n2) Standard comparator function.