UnivariateMixtureEstimator (interruptible-weka 0.1.2 API)

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.

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

class UnivariateMixtureEstimator.MM
Fast univariate mixture model implementation.

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

Field Summary

Fields
Modifier and Type	Field and Description
`protected boolean`	`m_Debug` Whether to output debug info.
`protected int`	`m_MaxNumComponents` The maximum number of components to use (default is 5)
`protected UnivariateMixtureEstimator.MM`	`m_MixtureModel` The current mixture model
`protected int`	`m_NumBootstrapRuns` The number of Bootstrap runs to use to select the number of components (default is 10)
`protected int`	`m_NumComponents` The number of components to use (default is -1)
`protected int`	`m_NumIntervals` The number of intervals used to approximate prediction interval.
`protected int`	`m_NumValues` The number of values that have been seen
`protected java.util.Random`	`m_Random` The random number generator.
`protected int`	`m_Seed` The random number seed to use (default is 1
`protected boolean`	`m_UseNormalizedEntropy` Whether to use normalized entropy instance of bootstrap.
`protected double[]`	`m_Values` The values used for this estimator
`protected double[]`	`m_Weights` The weights used for this estimator

Constructor Summary

Constructors
Constructor and Description

UnivariateMixtureEstimator()

Constructors
Constructor and Description
`UnivariateMixtureEstimator()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
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.
`protected double`	`entropy(UnivariateMixtureEstimator.MM mixtureModel)` Calculates entrpy for given model and data.
`protected UnivariateMixtureEstimator.MM`	`findModelUsingNormalizedEntropy()` Selects the number of components using normalized entropy.
`protected int`	`findNumComponentsUsingBootStrap()` Selects the number of components using leave-one-out Bootstrap, estimating loglikelihood.
`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.
`protected void`	`updateModel()` Updates the model based on the current data.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - m_Values
```
protected double[] m_Values
```
    The values used for this estimator
  - m_Weights
```
protected double[] m_Weights
```
    The weights used for this estimator
  - m_NumValues
```
protected int m_NumValues
```
    The number of values that have been seen
  - m_MixtureModel
```
protected UnivariateMixtureEstimator.MM m_MixtureModel
```
    The current mixture model
  - m_NumComponents
```
protected int m_NumComponents
```
    The number of components to use (default is -1)
  - m_MaxNumComponents
```
protected int m_MaxNumComponents
```
    The maximum number of components to use (default is 5)
  - m_Seed
```
protected int m_Seed
```
    The random number seed to use (default is 1
  - m_NumBootstrapRuns
```
protected int m_NumBootstrapRuns
```
    The number of Bootstrap runs to use to select the number of components (default is 10)
  - m_NumIntervals
```
protected int m_NumIntervals
```
    The number of intervals used to approximate prediction interval.
  - m_UseNormalizedEntropy
```
protected boolean m_UseNormalizedEntropy
```
    Whether to use normalized entropy instance of bootstrap.
  - m_Debug
```
protected boolean m_Debug
```
    Whether to output debug info.
  - m_Random
```
protected java.util.Random m_Random
```
    The random number generator.
- 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.
  - findNumComponentsUsingBootStrap
```
protected int findNumComponentsUsingBootStrap()
```
    Selects the number of components using leave-one-out Bootstrap, estimating loglikelihood.
    
    Returns:
    
    the number of components to use
  - entropy
```
protected double entropy(UnivariateMixtureEstimator.MM mixtureModel)
```
    Calculates entrpy for given model and data.
  - findModelUsingNormalizedEntropy
```
protected UnivariateMixtureEstimator.MM findModelUsingNormalizedEntropy()
```
    Selects the number of components using normalized entropy.
    
    Returns:
    
    the model to use
  - updateModel
```
protected void updateModel()
```
    Updates the model based on the current data. Uses the leave-one-out Bootstrap to choose the number of components.
  - 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

Class UnivariateMixtureEstimator

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

m_Values

m_Weights

m_NumValues

m_MixtureModel

m_NumComponents

m_MaxNumComponents

m_Seed

m_NumBootstrapRuns

m_NumIntervals

m_UseNormalizedEntropy

m_Debug

m_Random

Constructor Detail

UnivariateMixtureEstimator

Method Detail

globalInfo

getUseNormalizedEntropy

setUseNormalizedEntropy

numBootstrapRunsToolTipText

getNumBootstrapRuns

setNumBootstrapRuns

numComponentsToolTipText

getNumComponents

setNumComponents

seedTipText

setSeed

getSeed

maxNumComponentsToolTipText

getMaxNumComponents

setMaxNumComponents

addValue

buildModel

resampleWithWeights

findNumComponentsUsingBootStrap

entropy

findModelUsingNormalizedEntropy

updateModel

predictIntervals

predictQuantile

logDensity

toString

listOptions

setOptions

getOptions

getRevision

main