netkit.inference
Class InferenceMethod

java.lang.Object
  netkit.inference.InferenceMethod

All Implemented Interfaces:

Configurable

Direct Known Subclasses:

GibbsSampling, IterativeClassification, NullInference, RelaxationLabeling

public abstract class InferenceMethod
extends java.lang.Object
implements Configurable

Field Summary

protected Estimate	currPrior
protected double[][]	idMatrix
protected Estimate	initialPrior
java.util.logging.Logger	logger
protected int	numIterations
protected double[]	tmpPredict
protected Node[]	unknown

Constructor Summary

InferenceMethod()

Method Summary

void	addListener(InferenceMethodListener cl)
Classification	classify(NetworkClassifier networkClassifier, java.util.Iterator<Node> unknowns)
void	classify(NetworkClassifier networkClassifier, java.util.Iterator<Node> unknowns, Classification result)
void	clearListeners()
void	configure(Configuration config)
Estimate	estimate(NetworkClassifier networkClassifier, java.util.Iterator<Node> unknowns)
void	estimate(NetworkClassifier networkClassifier, java.util.Iterator<Node> unknowns, Estimate result)
double	getCurrentAccuracy()
Estimate	getCurrentEstimate()
double	getCurrentTrainingLOOAccuracy(NetworkClassifier nc) What is the accuracy on the training data, if we do a leave-one-out estimation, keeping current predictions for the test set.
Configuration	getDefaultConfiguration()
abstract java.lang.String	getDescription()
Estimate	getInitialPrior()
abstract java.lang.String	getName()
boolean	getNofifyListeners()
int	getNumIterations()
abstract java.lang.String	getShortName()
protected abstract boolean	iterate(NetworkClassifier networkClassifier)
void	notifyListeners(Classification c, int[] unknown)
void	notifyListeners(Estimate e, int[] unknown)
void	notifyListeners(Graph g, int[] unknown)
void	removeListener(InferenceMethodListener cl)
void	reset(java.util.Iterator<Node> unknowns)
void	savePredictions(java.lang.String outPredict, PrintEstimateWriter pe, boolean append, Node[] eval, java.lang.String header)
void	savePredictionsInPajek(java.lang.String pajekFile)
void	setInitialPrior(Estimate prior)
void	setNofityListeners(boolean notify)
void	setNumIterations(int numIterations)
void	setShowIterationAccuracies(boolean showItAcc)
void	setTruth(Classification truth)

Methods inherited from class java.lang.Object

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

Field Detail

logger

public final java.util.logging.Logger logger

unknown

protected Node[] unknown

tmpPredict

protected double[] tmpPredict

initialPrior

protected Estimate initialPrior

currPrior

protected Estimate currPrior

idMatrix

protected double[][] idMatrix

numIterations

protected int numIterations

Constructor Detail

InferenceMethod

public InferenceMethod()

Method Detail

getShortName

public abstract java.lang.String getShortName()

getName

public abstract java.lang.String getName()

getDescription

public abstract java.lang.String getDescription()

iterate

protected abstract boolean iterate(NetworkClassifier networkClassifier)

getDefaultConfiguration

public Configuration getDefaultConfiguration()

Specified by:

getDefaultConfiguration in interface Configurable

configure

public void configure(Configuration config)

Specified by:

configure in interface Configurable

reset

public void reset(java.util.Iterator<Node> unknowns)

setShowIterationAccuracies

public void setShowIterationAccuracies(boolean showItAcc)

getCurrentTrainingLOOAccuracy

public double getCurrentTrainingLOOAccuracy(NetworkClassifier nc)

What is the accuracy on the training data, if we do a leave-one-out estimation, keeping current predictions for the test set. This is a pseudo-estimation of how well we are doing in the current iteration.

Returns:

getCurrentAccuracy

public double getCurrentAccuracy()

getCurrentEstimate

public Estimate getCurrentEstimate()

setTruth

public final void setTruth(Classification truth)

savePredictions

public final void savePredictions(java.lang.String outPredict,
                                  PrintEstimateWriter pe,
                                  boolean append,
                                  Node[] eval,
                                  java.lang.String header)

savePredictionsInPajek

public final void savePredictionsInPajek(java.lang.String pajekFile)

estimate

public final Estimate estimate(NetworkClassifier networkClassifier,
                               java.util.Iterator<Node> unknowns)

estimate

public final void estimate(NetworkClassifier networkClassifier,
                           java.util.Iterator<Node> unknowns,
                           Estimate result)

classify

public final Classification classify(NetworkClassifier networkClassifier,
                                     java.util.Iterator<Node> unknowns)

classify

public final void classify(NetworkClassifier networkClassifier,
                           java.util.Iterator<Node> unknowns,
                           Classification result)

setInitialPrior

public final void setInitialPrior(Estimate prior)

getInitialPrior

public final Estimate getInitialPrior()

setNumIterations

public final void setNumIterations(int numIterations)

getNumIterations

public final int getNumIterations()

addListener

public final void addListener(InferenceMethodListener cl)

removeListener

public final void removeListener(InferenceMethodListener cl)

clearListeners

public final void clearListeners()

getNofifyListeners

public final boolean getNofifyListeners()

setNofityListeners

public final void setNofityListeners(boolean notify)

notifyListeners

public final void notifyListeners(Estimate e,
                                  int[] unknown)

notifyListeners

public final void notifyListeners(Classification c,
                                  int[] unknown)

notifyListeners

public final void notifyListeners(Graph g,
                                  int[] unknown)