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## weka.classifiers.rules
Class Ridor

    java.lang.Object
      weka.classifiers.AbstractClassifier
          weka.classifiers.rules.Ridor

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

    public class Ridor

extends AbstractClassifier
implements AdditionalMeasureProducer, WeightedInstancesHandler

An implementation of a RIpple-DOwn Rule learner.
It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate. Then it generates the "best" exceptions for each exception and iterates until pure. Thus it performs a tree-like expansion of exceptions.The exceptions are a set of rules that predict classes other than the default. IREP is used to generate the exceptions.
For more information about Ripple-Down Rules, see:
Brian R. Gaines, Paul Compton (1995). Induction of Ripple-Down Rules Applied to Modeling Large Databases. J. Intell. Inf. Syst.. 5(3):211-228.

There are five inner classes defined in this class.
The first is Ridor_node, which implements one node in the Ridor tree. It's basically composed of a default class and a set of exception rules to the default class.
The second inner class is RidorRule, which implements a single exception rule using REP.
The last three inner classes are only used in RidorRule. They are Antd, NumericAntd and NominalAntd, which all implement a single antecedent in the RidorRule.
The Antd class is an abstract class, which has two subclasses, NumericAntd and NominalAntd, to implement the corresponding abstract functions. These two subclasses implement the functions related to a antecedent with a nominal attribute and a numeric attribute respectively.

Valid options are:

     -F <number of folds>
      Set number of folds for IREP
      One fold is used as pruning set.
      (default 3)

     -S <number of shuffles>
      Set number of shuffles to randomize
      the data in order to get better rule.
      (default 10)

     -A
      Set flag of whether use the error rate
      of all the data to select the default class
      in each step. If not set, the learner will only use the error rate in the pruning data

     -M
       Set flag of whether use the majority class as
      the default class in each step instead of
      choosing default class based on the error rate
      (if the flag is not set)

     -N <min. weights>
      Set the minimal weights of instances
      within a split.
      (default 2.0)

Version:
  $Revision: 5928 $
Author:
  Xin XU (xx5@cs.waikato.ac.nz)
See Also:
  Serialized Form

Constructor Summary
`Ridor()`

Method Summary
`void` | `buildClassifier(Instances instances)` Builds a ripple-down manner rule learner.
`double` | `classifyInstance(Instance datum)` Classify the test instance with the rule learner
`java.util.Enumeration` | `enumerateMeasures()` Returns an enumeration of the additional measure names
`java.lang.String` | `foldsTipText()` Returns the tip text for this property
`Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
`int` | `getFolds()`
`boolean` | `getMajorityClass()`
`double` | `getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure
`double` | `getMinNo()`
`java.lang.String[]` | `getOptions()` Gets the current settings of the Classifier.
`java.lang.String` | `getRevision()` Returns the revision string.
`int` | `getSeed()`
`int` | `getShuffle()`
`boolean` | `getWholeDataErr()`
`java.lang.String` | `globalInfo()` Returns a string describing classifier
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options Valid options are:
`static void` | `main(java.lang.String[] args)` Main method.
`java.lang.String` | `majorityClassTipText()` Returns the tip text for this property
`java.lang.String` | `minNoTipText()` Returns the tip text for this property
`java.lang.String` | `seedTipText()` Returns the tip text for this property
`void` | `setFolds(int fold)`
`void` | `setMajorityClass(boolean m)`
`void` | `setMinNo(double m)`
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setSeed(int s)`
`void` | `setShuffle(int sh)`
`void` | `setWholeDataErr(boolean a)`
`java.lang.String` | `shuffleTipText()` Returns the tip text for this property
`java.lang.String` | `toString()` Prints the all the rules of the rule learner.
`java.lang.String` | `wholeDataErrTipText()` Returns the tip text for this property

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

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

Constructor Detail

### Ridor

    public Ridor()

Method Detail

### globalInfo

    public java.lang.String globalInfo()

  Returns a string describing classifier

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

### 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 instances)
                         throws java.lang.Exception

  Builds a ripple-down manner rule learner.

  Specified by:
    `buildClassifier` in interface `Classifier`
  Parameters:
    `instances` - the training data
  Throws:
    `java.lang.Exception` - if classifier can't be built successfully

### classifyInstance

    public double classifyInstance(Instance datum)

  Classify the test instance with the rule learner

  Specified by:
    `classifyInstance` in interface `Classifier`
  Overrides:
    `classifyInstance` in class `AbstractClassifier`
  Parameters:
    `datum` - the instance to be classified
  Returns:
    the classification

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options Valid options are:

  -F number
  Set number of folds for reduced error pruning. One fold is used as the pruning set. (Default: 3)

  -S number
  Set number of shuffles for randomization. (Default: 10)

  -A
  Set flag of whether use the error rate of all the data to select the default class in each step. If not set, the learner will only use the error rate in the pruning data

  -M
  Set flag of whether use the majority class as the default class in each step instead of choosing default class based on the error rate (if the flag is not set)

  -N number
  Set the minimal weights of instances within a split. (Default: 2)

  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:

       -F <number of folds>
        Set number of folds for IREP
        One fold is used as pruning set.
        (default 3)

       -S <number of shuffles>
        Set number of shuffles to randomize
        the data in order to get better rule.
        (default 10)

       -A
        Set flag of whether use the error rate
        of all the data to select the default class
        in each step. If not set, the learner will only use the error rate in the pruning data

       -M
         Set flag of whether use the majority class as
        the default class in each step instead of
        choosing default class based on the error rate
        (if the flag is not set)

       -N <min. weights>
        Set the minimal weights of instances
        within a split.
        (default 2.0)

  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

### 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

### foldsTipText

    public java.lang.String foldsTipText()

  Returns the tip text for this property

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

### setFolds

    public void setFolds(int fold)

### getFolds

    public int getFolds()

### shuffleTipText

    public java.lang.String shuffleTipText()

  Returns the tip text for this property

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

### setShuffle

    public void setShuffle(int sh)

### getShuffle

    public int getShuffle()

### 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 s)

### getSeed

    public int getSeed()

### wholeDataErrTipText

    public java.lang.String wholeDataErrTipText()

  Returns the tip text for this property

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

### setWholeDataErr

    public void setWholeDataErr(boolean a)

### getWholeDataErr

    public boolean getWholeDataErr()

### majorityClassTipText

    public java.lang.String majorityClassTipText()

  Returns the tip text for this property

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

### setMajorityClass

    public void setMajorityClass(boolean m)

### getMajorityClass

    public boolean getMajorityClass()

### minNoTipText

    public java.lang.String minNoTipText()

  Returns the tip text for this property

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

### setMinNo

    public void setMinNo(double m)

### getMinNo

    public double getMinNo()

### enumerateMeasures

    public java.util.Enumeration enumerateMeasures()

  Returns an enumeration of the additional measure names

  Specified by:
    `enumerateMeasures` in interface `AdditionalMeasureProducer`
  Returns:
    an enumeration of the measure names

### getMeasure

    public double getMeasure(java.lang.String additionalMeasureName)

  Returns the value of the named measure

  Specified by:
    `getMeasure` in interface `AdditionalMeasureProducer`
  Parameters:
    `additionalMeasureName` - the name of the measure to query for its value
  Returns:
    the value of the named measure
  Throws:
    `java.lang.IllegalArgumentException` - if the named measure is not supported

### toString

    public java.lang.String toString()

  Prints the all the rules of the rule learner.

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    a textual description of the classifier

### 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[] args)

  Main method.

  Parameters:
    `args` - the options for the classifier

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