netkit.classifiers.relational
Class Harmonic

java.lang.Object
  netkit.classifiers.ClassifierImp
      netkit.classifiers.relational.NetworkClassifierImp
          netkit.classifiers.relational.Harmonic

All Implemented Interfaces:

Classifier, NetworkClassifier, Configurable

public class Harmonic
extends NetworkClassifierImp

The Harmonic Function classifier from Zhu (2003) Reference:

• Zhu, X., Ghahramani, Z., & Lafferty, J. (2003).
  Semi-supervised learning using Gaussian fields and harmonic functions.
  The 20th International Conference on Machine Learning (ICML).

This takes no parameters and does not use collective inference. It uses only known labels and the graph.

Author:

Sofus A. Macskassy

Nested Class Summary

Nested classes/interfaces inherited from class netkit.classifiers.relational.NetworkClassifierImp

NetworkClassifierImp.Aggregation

Field Summary

Fields inherited from class netkit.classifiers.relational.NetworkClassifierImp

aggFactory, aggregation, aggregators, aggTypes, dynamicAggregators, prior

Fields inherited from class netkit.classifiers.ClassifierImp

attribute, classPrior, clsIdx, graph, keyIndex, logger, nodeType, right, tmpVector, useIntrinsic, vectorClsIdx

Constructor Summary

Harmonic()

Method Summary

boolean	doEstimate(Node node, double[] estimation) Returns the prediction computer in the induceModel call.
java.lang.String	getDescription()
double	getERM(Node n)
double[]	getERM(Node n, Classification truth)
java.lang.String	getName()
java.lang.String	getShortName()
void	induceModel(Graph graph, DataSplit split) Harmonic has no model per se as its learning consists of computing the harmonic function which results in the predictions.
void	initializeRun(Estimate currPrior, Node[] unknowns) This initializes Harmonic function for the next collective inference iteration by doing absolutely nothing.
java.lang.String	toString()

Methods inherited from class netkit.classifiers.relational.NetworkClassifierImp

classify, configure, estimate, estimate, estimate, estimate, generateAggregators, getAttributeNames, getDefaultConfiguration, includeClassAttribute, makeVector

Methods inherited from class netkit.classifiers.ClassifierImp

addListener, classify, classify, clearListeners, estimate, estimate, getLogger, getNofifyListeners, notifyListeners, notifyListeners, removeListener, reset, setNofityListeners

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface netkit.classifiers.Classifier

addListener, classify, classify, clearListeners, estimate, estimate, getLogger, getNofifyListeners, notifyListeners, notifyListeners, removeListener, reset, setNofityListeners

Constructor Detail

Harmonic

public Harmonic()

Method Detail

getShortName

public java.lang.String getShortName()

getName

public java.lang.String getName()

getDescription

public java.lang.String getDescription()

initializeRun

public void initializeRun(Estimate currPrior,
                          Node[] unknowns)

This initializes Harmonic function for the next collective inference iteration by doing absolutely nothing.

Specified by:

initializeRun in interface NetworkClassifier

Overrides:

initializeRun in class NetworkClassifierImp

Parameters:

currPrior - The current priors for all nodes in the graph

unknowns - The list of nodes whose labels are unknown

doEstimate

public boolean doEstimate(Node node,
                          double[] estimation)

Returns the prediction computer in the induceModel call.

Specified by:

doEstimate in class NetworkClassifierImp

Parameters:

node -

estimation -

Returns:

true, if the node was part of the original test set, false otherwise.

getERM

public double getERM(Node n)

getERM

public double[] getERM(Node n,
                       Classification truth)

induceModel

public void induceModel(Graph graph,
                        DataSplit split)

Harmonic has no model per se as its learning consists of computing the harmonic function which results in the predictions. Thus, this call creates the predictions for all the instances in the split test set.

Specified by:

induceModel in interface Classifier

Overrides:

induceModel in class NetworkClassifierImp

Parameters:

graph - The graph to induce a model over

split - The data split identifying which nodes have known and unknown labels

See Also:

NetworkClassifierImp.induceModel(netkit.graph.Graph, netkit.classifiers.DataSplit)

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object