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## weka.classifiers.functions
Class PaceRegression

    java.lang.Object
      weka.classifiers.AbstractClassifier
          weka.classifiers.functions.PaceRegression

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable, Classifier, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

    public class PaceRegression

extends AbstractClassifier
implements OptionHandler, WeightedInstancesHandler, TechnicalInformationHandler

Class for building pace regression linear models and using them for prediction.
Under regularity conditions, pace regression is provably optimal when the number of coefficients tends to infinity. It consists of a group of estimators that are either overall optimal or optimal under certain conditions.
The current work of the pace regression theory, and therefore also this implementation, do not handle:
- missing values
- non-binary nominal attributes
- the case that n - k is small where n is the number of instances and k is the number of coefficients (the threshold used in this implmentation is 20)
For more information see:
Wang, Y (2000). A new approach to fitting linear models in high dimensional spaces. Hamilton, New Zealand.
Wang, Y., Witten, I. H.: Modeling for optimal probability prediction. In: Proceedings of the Nineteenth International Conference in Machine Learning, Sydney, Australia, 650-657, 2002.

BibTeX:

     @phdthesis{Wang2000,
        address = {Hamilton, New Zealand},
        author = {Wang, Y},
        school = {Department of Computer Science, University of Waikato},
        title = {A new approach to fitting linear models in high dimensional spaces},
        year = {2000}
     }

     @inproceedings{Wang2002,
        address = {Sydney, Australia},
        author = {Wang, Y. and Witten, I. H.},
        booktitle = {Proceedings of the Nineteenth International Conference in Machine Learning},
        pages = {650-657},
        title = {Modeling for optimal probability prediction},
        year = {2002}
     }

Valid options are:

     -D
      Produce debugging output.
      (default no debugging output)

     -E <estimator>
      The estimator can be one of the following:
       eb -- Empirical Bayes estimator for noraml mixture (default)
       nested -- Optimal nested model selector for normal mixture
       subset -- Optimal subset selector for normal mixture
       pace2 -- PACE2 for Chi-square mixture
       pace4 -- PACE4 for Chi-square mixture
       pace6 -- PACE6 for Chi-square mixture

       ols -- Ordinary least squares estimator
       aic -- AIC estimator
       bic -- BIC estimator
       ric -- RIC estimator
       olsc -- Ordinary least squares subset selector with a threshold

     -S <threshold value>
      Threshold value for the OLSC estimator

Version:
  $Revision: 5928 $
Author:
  Yong Wang (yongwang@cs.waikato.ac.nz), Gabi Schmidberger (gabi@cs.waikato.ac.nz)
See Also:
  Serialized Form

Field Summary
`static Tag[]` | `TAGS_ESTIMATOR` estimator types

Constructor Summary
`PaceRegression()`

Method Summary
`void` | `buildClassifier(Instances data)` Builds a pace regression model for the given data.
`boolean` | `checkForMissing(Instance instance, Instances model)` Checks if an instance has a missing value.
`double` | `classifyInstance(Instance instance)` Classifies the given instance using the linear regression function.
`double[]` | `coefficients()` Returns the coefficients for this linear model.
`java.lang.String` | `debugTipText()` Returns the tip text for this property
`java.lang.String` | `estimatorTipText()` Returns the tip text for this property
`Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
`boolean` | `getDebug()` Controls whether debugging output will be printed
`SelectedTag` | `getEstimator()` Gets the estimator
`java.lang.String[]` | `getOptions()` Gets the current settings of the classifier.
`java.lang.String` | `getRevision()` Returns the revision string.
`TechnicalInformation` | `getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`double` | `getThreshold()` Gets the threshold for olsc estimator
`java.lang.String` | `globalInfo()` Returns a string describing this classifier
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`static void` | `main(java.lang.String[] argv)` Generates a linear regression function predictor.
`int` | `numParameters()` Get the number of coefficients used in the model
`void` | `setDebug(boolean debug)` Controls whether debugging output will be printed
`void` | `setEstimator(SelectedTag estimator)` Sets the estimator.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setThreshold(double newThreshold)` Set threshold for the olsc estimator
`java.lang.String` | `thresholdTipText()` Returns the tip text for this property
`java.lang.String` | `toString()` Outputs the linear regression model as a string.

Methods inherited from class weka.classifiers.AbstractClassifier
`distributionForInstance, forName, makeCopies, makeCopy`

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

Field Detail

### TAGS_ESTIMATOR

    public static final Tag[] TAGS_ESTIMATOR

  estimator types

Constructor Detail

### PaceRegression

    public PaceRegression()

Method Detail

### globalInfo

    public java.lang.String globalInfo()

  Returns a string describing this classifier

  Returns:
    a description of the classifier suitable for displaying in the explorer/experimenter gui

### getTechnicalInformation

    public TechnicalInformation getTechnicalInformation()

  Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

  Specified by:
    `getTechnicalInformation` in interface `TechnicalInformationHandler`
  Returns:
    the technical information about this class

### getCapabilities

    public Capabilities getCapabilities()

  Returns default capabilities of the classifier.

  Specified by:
    `getCapabilities` in interface `Classifier`
  Specified by:
    `getCapabilities` in interface `CapabilitiesHandler`
  Overrides:
    `getCapabilities` in class `AbstractClassifier`
  Returns:
    the capabilities of this classifier
  See Also:
    `Capabilities`

### buildClassifier

    public void buildClassifier(Instances data)
                         throws java.lang.Exception

  Builds a pace regression model for the given data.

  Specified by:
    `buildClassifier` in interface `Classifier`
  Parameters:
    `data` - the training data to be used for generating the linear regression function
  Throws:
    `java.lang.Exception` - if the classifier could not be built successfully

### checkForMissing

    public boolean checkForMissing(Instance instance,
                                   Instances model)

  Checks if an instance has a missing value.

  Parameters:
    `instance` - the instance
    `model` - the data
  Returns:
    true if missing value is present

### classifyInstance

    public double classifyInstance(Instance instance)
                            throws java.lang.Exception

  Classifies the given instance using the linear regression function.

  Specified by:
    `classifyInstance` in interface `Classifier`
  Overrides:
    `classifyInstance` in class `AbstractClassifier`
  Parameters:
    `instance` - the test instance
  Returns:
    the classification
  Throws:
    `java.lang.Exception` - if classification can't be done successfully

### toString

    public java.lang.String toString()

  Outputs the linear regression model as a string.

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    the model as string

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options.

  Specified by:
    `listOptions` in interface `OptionHandler`
  Overrides:
    `listOptions` in class `AbstractClassifier`
  Returns:
    an enumeration of all the available options.

### setOptions

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

  Parses a given list of options.

  Valid options are:

       -D
        Produce debugging output.
        (default no debugging output)

       -E <estimator>
        The estimator can be one of the following:
         eb -- Empirical Bayes estimator for noraml mixture (default)
         nested -- Optimal nested model selector for normal mixture
         subset -- Optimal subset selector for normal mixture
         pace2 -- PACE2 for Chi-square mixture
         pace4 -- PACE4 for Chi-square mixture
         pace6 -- PACE6 for Chi-square mixture

         ols -- Ordinary least squares estimator
         aic -- AIC estimator
         bic -- BIC estimator
         ric -- RIC estimator
         olsc -- Ordinary least squares subset selector with a threshold

       -S <threshold value>
        Threshold value for the OLSC estimator

  Specified by:
    `setOptions` in interface `OptionHandler`
  Overrides:
    `setOptions` in class `AbstractClassifier`
  Parameters:
    `options` - the list of options as an array of strings
  Throws:
    `java.lang.Exception` - if an option is not supported

### coefficients

    public double[] coefficients()

  Returns the coefficients for this linear model.

  Returns:
    the coefficients for this linear model

### getOptions

    public java.lang.String[] getOptions()

  Gets the current settings of the classifier.

  Specified by:
    `getOptions` in interface `OptionHandler`
  Overrides:
    `getOptions` in class `AbstractClassifier`
  Returns:
    an array of strings suitable for passing to setOptions

### numParameters

    public int numParameters()

  Get the number of coefficients used in the model

  Returns:
    the number of coefficients

### debugTipText

    public java.lang.String debugTipText()

  Returns the tip text for this property

  Overrides:
    `debugTipText` in class `AbstractClassifier`
  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### setDebug

    public void setDebug(boolean debug)

  Controls whether debugging output will be printed

  Overrides:
    `setDebug` in class `AbstractClassifier`
  Parameters:
    `debug` - true if debugging output should be printed

### getDebug

    public boolean getDebug()

  Controls whether debugging output will be printed

  Overrides:
    `getDebug` in class `AbstractClassifier`
  Returns:
    true if debugging output should be printed

### estimatorTipText

    public java.lang.String estimatorTipText()

  Returns the tip text for this property

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

### getEstimator

    public SelectedTag getEstimator()

  Gets the estimator

  Returns:
    the estimator

### setEstimator

    public void setEstimator(SelectedTag estimator)

  Sets the estimator.

  Parameters:
    `estimator` - the new estimator

### thresholdTipText

    public java.lang.String thresholdTipText()

  Returns the tip text for this property

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

### setThreshold

    public void setThreshold(double newThreshold)

  Set threshold for the olsc estimator

  Parameters:
    `newThreshold` - the threshold for the olsc estimator

### getThreshold

    public double getThreshold()

  Gets the threshold for olsc estimator

  Returns:
    the threshold

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Overrides:
    `getRevision` in class `AbstractClassifier`
  Returns:
    the revision

### main

    public static void main(java.lang.String[] argv)

  Generates a linear regression function predictor.

  Parameters:
    `argv` - the options

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