weka.estimators

Class UnivariateMixtureEstimator

java.lang.Object

weka.estimators.UnivariateMixtureEstimator

All Implemented Interfaces:

java.io.Serializable, OptionHandler, RevisionHandler, UnivariateDensityEstimator, UnivariateIntervalEstimator, UnivariateQuantileEstimator

public class UnivariateMixtureEstimator
extends java.lang.Object
implements UnivariateDensityEstimator, UnivariateIntervalEstimator, UnivariateQuantileEstimator, OptionHandler, java.io.Serializable

Simple weighted mixture density estimator. Uses a mixture of Gaussians and applies the leave-one-out bootstrap for model selection. Can alternatively use normalized entropy.

Version:

$Revision: 8034 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
class	UnivariateMixtureEstimator.MM Fast univariate mixture model implementation.

Constructor Summary

Constructors

Constructor and Description
UnivariateMixtureEstimator()

Method Summary

Modifier and Type	Method and Description
void	addValue(double value, double weight) Adds a value to the density estimator.
UnivariateMixtureEstimator.MM	buildModel(int K, double[] values, double[] weights) Build mixture model.
int	getMaxNumComponents() Returns the number of components to use.
int	getNumBootstrapRuns() Returns the number of Bootstrap runs.
int	getNumComponents() Returns the number of components to use.
java.lang.String[]	getOptions() Returns the current set of options.
java.lang.String	getRevision() Returns the revision string.
int	getSeed() Gets the seed for the random number generations
boolean	getUseNormalizedEntropy()
java.lang.String	globalInfo() Returns a string describing the estimator.
java.util.Enumeration<Option>	listOptions() Returns an enumeration that lists the command-line options that are available
double	logDensity(double value) Returns the natural logarithm of the density estimate at the given point.
static void	main(java.lang.String[] args) Main method, used for testing this class.
java.lang.String	maxNumComponentsToolTipText() The tool tip for this property.
java.lang.String	numBootstrapRunsToolTipText() The tool tip for this property.
java.lang.String	numComponentsToolTipText() The tool tip for this property.
double[][]	predictIntervals(double conf) Returns the interval for the given confidence value.
double	predictQuantile(double percentage) Returns the quantile for the given percentage.
double[][]	resampleWithWeights(java.util.Random random, boolean[] sampled) Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
java.lang.String	seedTipText() Returns the tip text for this property
void	setMaxNumComponents(int maxNumComponents) Sets the number of components to use.
void	setNumBootstrapRuns(int numBootstrapRuns) Sets the number of Bootstrap runs.
void	setNumComponents(int numComponents) Sets the number of components to use.
void	setOptions(java.lang.String[] options) Sets options based on the given array of strings.
void	setSeed(int seed) Set the seed for random number generation.
void	setUseNormalizedEntropy(boolean useNormalizedEntropy)
java.lang.String	toString() Returns textual description of this estimator.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler

makeCopy

Constructor Detail

UnivariateMixtureEstimator

public UnivariateMixtureEstimator()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing the estimator.

getUseNormalizedEntropy

public boolean getUseNormalizedEntropy()

Returns:

whether normalized entropy is used

setUseNormalizedEntropy

public void setUseNormalizedEntropy(boolean useNormalizedEntropy)

Parameters:

useNormalizedEntropy - whether to use normalized entropy

numBootstrapRunsToolTipText

public java.lang.String numBootstrapRunsToolTipText()

The tool tip for this property.

getNumBootstrapRuns

public int getNumBootstrapRuns()

Returns the number of Bootstrap runs.

Returns:

the number of Bootstrap runs

setNumBootstrapRuns

public void setNumBootstrapRuns(int numBootstrapRuns)

Sets the number of Bootstrap runs.

Parameters:

mnumBootstrapRuns - the number of Bootstrap runs

numComponentsToolTipText

public java.lang.String numComponentsToolTipText()

The tool tip for this property.

getNumComponents

public int getNumComponents()

Returns the number of components to use.

Returns:

the m_NumComponents

setNumComponents

public void setNumComponents(int numComponents)

Sets the number of components to use.

Parameters:

m_NumComponents - the m_NumComponents to set

seedTipText

public java.lang.String seedTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setSeed

public void setSeed(int seed)

Set the seed for random number generation.

Parameters:

seed - the seed

getSeed

public int getSeed()

Gets the seed for the random number generations

Returns:

the seed for the random number generation

maxNumComponentsToolTipText

public java.lang.String maxNumComponentsToolTipText()

The tool tip for this property.

getMaxNumComponents

public int getMaxNumComponents()

Returns the number of components to use.

Returns:

the maximum number of components to use

setMaxNumComponents

public void setMaxNumComponents(int maxNumComponents)

Sets the number of components to use.

Parameters:

maxNumComponents - the maximum number of components to evaluate

addValue

public void addValue(double value,
                     double weight)

Adds a value to the density estimator.

Specified by:

addValue in interface UnivariateDensityEstimator

Specified by:

addValue in interface UnivariateIntervalEstimator

Specified by:

addValue in interface UnivariateQuantileEstimator

Parameters:

value - the value to add

weight - the weight of the value

buildModel

public UnivariateMixtureEstimator.MM buildModel(int K,
                                                double[] values,
                                                double[] weights)

Build mixture model. Assumes K >= 1, values.length >= 1, and values.length = weights.length.

resampleWithWeights

public double[][] resampleWithWeights(java.util.Random random,
                                      boolean[] sampled)

Creates a new dataset of the same size using random sampling with replacement according to the given weight vector. The weights of the instances in the new dataset are set to one.

predictIntervals

public double[][] predictIntervals(double conf)

Returns the interval for the given confidence value.

Specified by:

predictIntervals in interface UnivariateIntervalEstimator

Parameters:

conf - the confidence value in the interval [0, 1]

Returns:

the interval

predictQuantile

public double predictQuantile(double percentage)

Returns the quantile for the given percentage.

Specified by:

predictQuantile in interface UnivariateQuantileEstimator

Parameters:

percentage - the percentage

Returns:

the quantile

logDensity

public double logDensity(double value)

Returns the natural logarithm of the density estimate at the given point.

Specified by:

logDensity in interface UnivariateDensityEstimator

Parameters:

value - the value at which to evaluate

Returns:

the natural logarithm of the density estimate at the given value

toString

public java.lang.String toString()

Returns textual description of this estimator.

Overrides:

toString in class java.lang.Object

listOptions

public java.util.Enumeration<Option> listOptions()

Returns an enumeration that lists the command-line options that are available

Specified by:

listOptions in interface OptionHandler

Returns:

the list of options as an enumeration

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Sets options based on the given array of strings.

Specified by:

setOptions in interface OptionHandler

Parameters:

options - the list of options to parse

Throws:

java.lang.Exception - if an option is not supported

getOptions

public java.lang.String[] getOptions()

Returns the current set of options.

Specified by:

getOptions in interface OptionHandler

Returns:

the current set of options as a string

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Returns:

the revision

main

public static void main(java.lang.String[] args)
                 throws java.lang.Exception

Main method, used for testing this class.

Throws:

java.lang.Exception