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## weka.classifiers.rules
Class ConjunctiveRule

    java.lang.Object
      weka.classifiers.Classifier
          weka.classifiers.rules.ConjunctiveRule

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

    public class ConjunctiveRule

extends Classifier
implements OptionHandler, WeightedInstancesHandler

This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.
A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. In this case, the consequent is the distribution of the available classes (or mean for a numeric value) in the dataset. If the test instance is not covered by this rule, then it's predicted using the default class distributions/value of the data not covered by the rule in the training data.This learner selects an antecedent by computing the Information Gain of each antecendent and prunes the generated rule using Reduced Error Prunning (REP) or simple pre-pruning based on the number of antecedents.
For classification, the Information of one antecedent is the weighted average of the entropies of both the data covered and not covered by the rule.
For regression, the Information is the weighted average of the mean-squared errors of both the data covered and not covered by the rule.
In pruning, weighted average of the accuracy rates on the pruning data is used for classification while the weighted average of the mean-squared errors on the pruning data is used for regression.

Valid options are:

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

     -R
      Set if NOT uses randomization
      (default:use randomization)

     -E
      Set whether consider the exclusive
      expressions for nominal attributes
      (default false)

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

     -P <number of antecedents>
      Set number of antecedents for pre-pruning
      if -1, then REP is used
      (default -1)

     -S <seed>
      Set the seed of randomization
      (default 1)

Version:
  $Revision: 5529 $
Author:
  Xin XU (xx5@cs.waikato.ac.nz)
See Also:
  Serialized Form

Constructor Summary
`ConjunctiveRule()`

Method Summary
`void` | `buildClassifier(Instances instances)` Builds a single rule learner with REP dealing with nominal classes or numeric classes.
`double[]` | `distributionForInstance(Instance instance)` Computes class distribution for the given instance.
`java.lang.String` | `exclusiveTipText()` Returns the tip text for this property
`java.lang.String` | `foldsTipText()` Returns the tip text for this property
`Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
`boolean` | `getExclusive()` Returns whether exclusive expressions for nominal attributes splits are considered
`int` | `getFolds()` returns the current number of folds
`double` | `getMinNo()` Gets the minimum total weight of the instances in a rule
`int` | `getNumAntds()` Gets the number of antecedants
`java.lang.String[]` | `getOptions()` Gets the current settings of the Classifier.
`java.lang.String` | `getRevision()` Returns the revision string.
`long` | `getSeed()` returns the current seed value for randomizing the data
`java.lang.String` | `globalInfo()` Returns a string describing classifier
`boolean` | `hasAntds()` Whether this rule has antecedents, i.e.
`boolean` | `isCover(Instance datum)` Whether the instance covered by this rule
`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` | `minNoTipText()` Returns the tip text for this property
`java.lang.String` | `numAntdsTipText()` Returns the tip text for this property
`java.lang.String` | `seedTipText()` Returns the tip text for this property
`void` | `setExclusive(boolean e)` Sets whether exclusive expressions for nominal attributes splits are considered
`void` | `setFolds(int folds)` the number of folds to use
`void` | `setMinNo(double m)` Sets the minimum total weight of the instances in a rule
`void` | `setNumAntds(int n)` Sets the number of antecedants
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setSeed(long s)` sets the seed for randomizing the data
`java.lang.String` | `toString()` Prints this rule
`java.lang.String` | `toString(java.lang.String att, java.lang.String cl)` Prints this rule with the specified class label

Methods inherited from class weka.classifiers.Classifier
`classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug`

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

Constructor Detail

### ConjunctiveRule

    public ConjunctiveRule()

Method Detail

### globalInfo

    public java.lang.String globalInfo()

  Returns a string describing classifier

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

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options Valid options are:

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

  -R
  Set if NOT randomize the data before split to growing and pruning data. If NOT set, the seed of randomization is specified by the -S option. (Default: randomize)

  -S
  Seed of randomization. (Default: 1)

  -E
  Set whether consider the exclusive expressions for nominal attribute split. (Default: false)

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

  -P number
  Set the number of antecedents allowed in the rule if pre-pruning is used. If this value is other than -1, then pre-pruning will be used, otherwise the rule uses REP. (Default: -1)

  Specified by:
    `listOptions` in interface `OptionHandler`
  Overrides:
    `listOptions` in class `Classifier`
  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:

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

       -R
        Set if NOT uses randomization
        (default:use randomization)

       -E
        Set whether consider the exclusive
        expressions for nominal attributes
        (default false)

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

       -P <number of antecedents>
        Set number of antecedents for pre-pruning
        if -1, then REP is used
        (default -1)

       -S <seed>
        Set the seed of randomization
        (default 1)

  Specified by:
    `setOptions` in interface `OptionHandler`
  Overrides:
    `setOptions` in class `Classifier`
  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 `Classifier`
  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 folds)

  the number of folds to use

  Parameters:
    `folds` - the number of folds to use

### getFolds

    public int getFolds()

  returns the current number of folds

  Returns:
    the number of folds

### 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(long s)

  sets the seed for randomizing the data

  Parameters:
    `s` - the seed value

### getSeed

    public long getSeed()

  returns the current seed value for randomizing the data

  Returns:
    the seed value

### exclusiveTipText

    public java.lang.String exclusiveTipText()

  Returns the tip text for this property

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

### getExclusive

    public boolean getExclusive()

  Returns whether exclusive expressions for nominal attributes splits are considered

  Returns:
    true if exclusive expressions for nominal attributes splits are considered

### setExclusive

    public void setExclusive(boolean e)

  Sets whether exclusive expressions for nominal attributes splits are considered

  Parameters:
    `e` - whether to consider exclusive expressions for nominal attribute splits

### 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)

  Sets the minimum total weight of the instances in a rule

  Parameters:
    `m` - the minimum total weight of the instances in a rule

### getMinNo

    public double getMinNo()

  Gets the minimum total weight of the instances in a rule

  Returns:
    the minimum total weight of the instances in a rule

### numAntdsTipText

    public java.lang.String numAntdsTipText()

  Returns the tip text for this property

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

### setNumAntds

    public void setNumAntds(int n)

  Sets the number of antecedants

  Parameters:
    `n` - the number of antecedants

### getNumAntds

    public int getNumAntds()

  Gets the number of antecedants

  Returns:
    the number of antecedants

### getCapabilities

    public Capabilities getCapabilities()

  Returns default capabilities of the classifier.

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

### buildClassifier

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

  Builds a single rule learner with REP dealing with nominal classes or numeric classes. For nominal classes, this rule learner predicts a distribution on the classes. For numeric classes, this learner predicts a single value.

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

### distributionForInstance

    public double[] distributionForInstance(Instance instance)
                                     throws java.lang.Exception

  Computes class distribution for the given instance.

  Overrides:
    `distributionForInstance` in class `Classifier`
  Parameters:
    `instance` - the instance for which distribution is to be computed
  Returns:
    the class distribution for the given instance
  Throws:
    `java.lang.Exception` - if given instance is null

### isCover

    public boolean isCover(Instance datum)

  Whether the instance covered by this rule

  Parameters:
    `datum` - the instance in question
  Returns:
    the boolean value indicating whether the instance is covered by this rule

### hasAntds

    public boolean hasAntds()

  Whether this rule has antecedents, i.e. whether it is a default rule

  Returns:
    the boolean value indicating whether the rule has antecedents

### toString

    public java.lang.String toString(java.lang.String att,
                                     java.lang.String cl)

  Prints this rule with the specified class label

  Parameters:
    `att` - the string standing for attribute in the consequent of this rule
    `cl` - the string standing for value in the consequent of this rule
  Returns:
    a textual description of this rule with the specified class label

### toString

    public java.lang.String toString()

  Prints this rule

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    a textual description of this rule

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Overrides:
    `getRevision` in class `Classifier`
  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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