edu.udo.cs.yale.operator.learner.igss.utility
Interface Utility

All Known Implementing Classes:

AbstractUtility, Accuracy, Binomial, Coverage, InstanceAveraging, Linear, Squared, WRAcc

public interface Utility

Interface for all utility functions.

Version:

$Id: Utility.java,v 1.1 2006/10/02 16:07:49 ingomierswa Exp $

Author:

Dirk Dach

Field Summary

static int	FIRST_TYPE_INDEX
static int	LAST_TYPE_INDEX
static int	TYPE_ACCURACY
static int	TYPE_BINOMIAL
static int	TYPE_LINEAR
static int	TYPE_SQUARED
static int	TYPE_WRACC
static java.lang.String[]	UTILITY_TYPES

Method Summary

double	calculateM(double delta, double epsilon) Calculates the M-value needed for the GSS algorithm.
double	confidenceIntervall(double totalWeight, double delta) Calculates the the unspecific confidence intervall.
double	confidenceIntervall(double totalWeight, double totalPositiveWeight, Hypothesis hypo, double delta) Calculates the the confidence intervall for a specific hypothesis.
double	getUpperBound(double totalWeight, double totalPositiveWeight, Hypothesis hypo, double delta) Returns an upper bound for the utility of refinements for the given hypothesis.
double	utility(double totalWeight, double totalPositiveWeight, Hypothesis hypo) Calculates the utility for the given number of examples,positive examples and hypothesis

Field Detail

UTILITY_TYPES

static final java.lang.String[] UTILITY_TYPES

FIRST_TYPE_INDEX

static final int FIRST_TYPE_INDEX

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Constant Field Values

TYPE_ACCURACY

static final int TYPE_ACCURACY

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Constant Field Values

TYPE_LINEAR

static final int TYPE_LINEAR

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Constant Field Values

TYPE_SQUARED

static final int TYPE_SQUARED

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Constant Field Values

TYPE_BINOMIAL

static final int TYPE_BINOMIAL

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Constant Field Values

TYPE_WRACC

static final int TYPE_WRACC

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Constant Field Values

LAST_TYPE_INDEX

static final int LAST_TYPE_INDEX

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Constant Field Values

Method Detail

utility

double utility(double totalWeight,
               double totalPositiveWeight,
               Hypothesis hypo)

Calculates the utility for the given number of examples,positive examples and hypothesis

calculateM

double calculateM(double delta,
                  double epsilon)

Calculates the M-value needed for the GSS algorithm.

confidenceIntervall

double confidenceIntervall(double totalWeight,
                           double delta)

Calculates the the unspecific confidence intervall. Uses Chernoff bounds if the number of random experiments is too small and normal approximatione otherwise.

confidenceIntervall

double confidenceIntervall(double totalWeight,
                           double totalPositiveWeight,
                           Hypothesis hypo,
                           double delta)

Calculates the the confidence intervall for a specific hypothesis. Uses Chernoff bounds if the number of random experiments is too small and normal approximation otherwise.

getUpperBound

double getUpperBound(double totalWeight,
                     double totalPositiveWeight,
                     Hypothesis hypo,
                     double delta)

Returns an upper bound for the utility of refinements for the given hypothesis.