Uses of Class
netkit.classifiers.Estimate

Packages that use Estimate

netkit.classifiers
netkit.classifiers.active
netkit.classifiers.aggregators
netkit.classifiers.io
netkit.classifiers.relational
netkit.graph.io
netkit.inference
netkit.util

Uses of Estimate in netkit.classifiers

Methods in netkit.classifiers that return Estimate

Estimate	NetworkLearner.runActiveLearner(PickLabelStrategy ps, DataSplit split) See fully parameterized method for details.
Estimate	NetworkLearner.runActiveLearner(PickLabelStrategy ps, DataSplit split, int picksPerIteration, int maxPicks, boolean learnWithTruth, int depth) Run active learning using the given parameters.
Estimate	NetworkLearning.runInference(DataSplit split)
Estimate	NetworkLearner.runInference(DataSplit split)
Estimate	NetworkLearner.runInference(DataSplit split, boolean showItAcc, boolean learnWithTruth, int depth)
Estimate	NetworkLearner.runLeaveOneOut(DataSplit split)
Estimate	NetworkLearner.runLeaveOneOut(DataSplit split, boolean learnWithTruth, int depth)

Methods in netkit.classifiers with parameters of type Estimate

void	Estimate.copyInto(Estimate result)
boolean	ClassifierImp.estimate(Node node, Estimate result)
boolean	Classifier.estimate(Node node, Estimate result)

Constructors in netkit.classifiers with parameters of type Estimate

Classification(Estimate e)

Estimate(Estimate e)

Uses of Estimate in netkit.classifiers.active

Methods in netkit.classifiers.active with parameters of type Estimate

static double	EmpiricalRiskMinimization.computeEmpiricalRisk(Estimate predictions) Compute the empirical risk for a specific set of predictions using the standard empirical risk formulation: risk(predictions) = sum over x in testset: argmin_i [ 1-f(x,i) ], where f(x,i) is the probability that x belongs to class i.
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.
double	UncertaintyLabeling.getRank(DataSplit split, Estimate predictions, Node node)
double	PickLabelStrategyImp.getRank(DataSplit currentSplit, Estimate currentPredictions, Node node)
double	PickLabelStrategy.getRank(DataSplit currentSplit, Estimate currentPredictions, Node node) Get the rank of the given node if the strategy were to pick the node.
double	GreedyTruth.getRank(DataSplit split, Estimate predictions, Node node)
double	GraphCentralityLabeling.getRank(DataSplit split, Estimate predictions, Node node)
double	EmpiricalRiskMinimizationHarmonic.getRank(DataSplit split, Estimate predictions, Node node)
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.

Uses of Estimate in netkit.classifiers.aggregators

Methods in netkit.classifiers.aggregators with parameters of type Estimate

double[]	SharedNodeInfo.countNeighbors(Node n, Estimate prior) Count, for all relevant neighbors, how many of the neighboring attributes took on each of the possible values (weighted by the edge weight).
double	SharedNodeInfo.getSum(Node n, Estimate prior) Get the (weighted) sum of all relevant neighbors.
double	Ratio.getValue(Node n, Estimate prior)
double	Mode.getValue(Node n, Estimate prior)
double	Min.getValue(Node n, Estimate prior)
double	Mean.getValue(Node n, Estimate prior)
double	Max.getValue(Node n, Estimate prior)
double	Exist.getValue(Node n, Estimate prior)
double	Count.getValue(Node n, Estimate prior)
double	Aggregator.getValue(Node n, Estimate prior) Gets the value stored in this object for the supplied Node.

Uses of Estimate in netkit.classifiers.io

Methods in netkit.classifiers.io that return Estimate

Estimate	ReadEstimateRainbow.readEstimate(Graph graph, java.lang.String nodeType, AttributeCategorical attribute, java.io.File input) Create a new Estimate object and call the more general readEstimate method.
Estimate	ReadEstimate.readEstimate(Graph graph, java.lang.String nodeType, AttributeCategorical attribute, java.io.File input) Read from a given file an estimate for nodes in the given graph.

Methods in netkit.classifiers.io with parameters of type Estimate

void	PrintEstimateWriter.print(Node node, Estimate e) Print an estimate of the given node using the given output format and the given current estimates.
void	PrintEstimateWriter.print(Node node, Estimate e, Classification known) Print an estimate of the given node using the given output format and the given current estimates and true labels.
void	PrintEstimateWriter.println(Node node, Estimate e) Print an estimate line of the given node using the given output format and the given current estimates.
void	PrintEstimateWriter.println(Node node, Estimate e, Classification known) Print an estimate line of the given node using the given output format and the given current estimates and true labels.
void	ReadEstimateRainbow.readEstimate(Graph graph, Estimate estimates, java.io.File input) Reads in a set of estimates from the given file, assuming that each line is of the form: nodeID trueclass class:score ...
void	ReadEstimate.readEstimate(Graph graph, Estimate estimates, java.io.File input) Read from a given file estimates for nodes in the given graph.
java.lang.String	PrintEstimateWriter.toString(Node node, Estimate e) The equivalent of a print, where the output has been set to a string to be returned.
java.lang.String	PrintEstimateWriter.toString(Node node, Estimate e, Classification known) The equivalent of a print, where the output has been set to a string to be returned.

Uses of Estimate in netkit.classifiers.relational

Fields in netkit.classifiers.relational declared as Estimate

protected Estimate

NetworkClassifierImp.prior This keeps track of the priors for the unknown nodes.

Methods in netkit.classifiers.relational with parameters of type Estimate

int	NetworkClassifierImp.classify(Node node, Estimate prior, boolean updatePrior) Classify a given node into one of the given classes.
int	NetworkClassifier.classify(Node node, Estimate prior, boolean updatePrior) Classify a given node into one of the given classes.
double[]	NetworkClassifierImp.estimate(Node node, Estimate prior, boolean updatePrior) Estimate the probabilities that a given node into belongs to any given class It may use the class estimations of other nodes and may update the prior of the given node.
double[]	NetworkClassifier.estimate(Node node, Estimate prior, boolean updatePrior) Estimate the probabilities that a given node into belongs to any given class It may use the class estimations of other nodes and may update the prior of the given node.
boolean	NetworkClassifierImp.estimate(Node node, Estimate prior, double[] result, boolean updatePrior) Estimate the probabilities that a given node into belongs to any given class It may use the class estimations of other nodes and may update the prior of the given node.
boolean	NetworkClassifier.estimate(Node node, Estimate prior, double[] result, boolean updatePrior) Estimate the probabilities that a given node into belongs to any given class It may use the class estimations of other nodes and may update the prior of the given node.
boolean	NetworkClassifierImp.estimate(Node node, Estimate prior, Estimate result, boolean updatePrior) Estimate the probabilities that a given node into belongs to any given class It may use the class estimations of other nodes and may update the prior of the given node.
boolean	NetworkClassifier.estimate(Node node, Estimate prior, Estimate result, boolean updatePrior) Estimate the probabilities that a given node into belongs to any given class It may use the class estimations of other nodes and may update the prior of the given node.
void	WeightedVoteRelationalNeighbor.initializeRun(Estimate currPrior, Node[] unknowns) This initializes wvRN for the next collective inference iteration by setting up the laplace correction, if needed, for the current iteration.
void	NetworkClassifierImp.initializeRun(Estimate currPrior, Node[] unknowns) This is called prior to predicting labels for the unknown labels in the graph, in case the classifier needs to initialize itself.
void	NetworkClassifier.initializeRun(Estimate currPrior, Node[] unknowns) This is called prior to predicting labels for the unknown labels in the graph, in case the classifier needs to initialize itself.
void	Harmonic.initializeRun(Estimate currPrior, Node[] unknowns) This initializes Harmonic function for the next collective inference iteration by doing absolutely nothing.

Uses of Estimate in netkit.graph.io

Methods in netkit.graph.io with parameters of type Estimate

static void	PajekGraph.saveGraph(Graph graph, java.io.PrintWriter pw, Classification truth, Estimate pred, java.lang.String labelAttribute)
static void	PajekGraph.saveGraph(Graph graph, java.lang.String file, Classification truth, Estimate pred, java.lang.String labelAttribute) Save the given graph as a pajek graph, to the given file, using the classification and estimate and label.

Uses of Estimate in netkit.inference

Fields in netkit.inference declared as Estimate

protected Estimate	InferenceMethod.currPrior
protected Estimate	InferenceMethod.initialPrior

Methods in netkit.inference that return Estimate

Estimate	InferenceMethod.estimate(NetworkClassifier networkClassifier, java.util.Iterator<Node> unknowns)
Estimate	InferenceMethod.getCurrentEstimate()
Estimate	GibbsSampling.getCurrentEstimate()
Estimate	InferenceMethod.getInitialPrior()

Methods in netkit.inference with parameters of type Estimate

void	InferenceMethodListener.estimate(Estimate e, int[] unknown)
void	InferenceMethod.estimate(NetworkClassifier networkClassifier, java.util.Iterator<Node> unknowns, Estimate result)
void	InferenceMethod.notifyListeners(Estimate e, int[] unknown)
void	InferenceMethod.setInitialPrior(Estimate prior)

Uses of Estimate in netkit.util

Constructors in netkit.util with parameters of type Estimate

ConfusionMatrix(Estimate predictions, Classification truth)

ROC(Estimate predictions, Classification truth, int posClass)