Class RepeatedHillClimber

All Implemented Interfaces:
Serializable, OptionHandler, RevisionHandler

public class RepeatedHillClimber extends HillClimber
This Bayes Network learning algorithm repeatedly uses hill climbing starting with a randomly generated network structure and return the best structure of the various runs.

Valid options are:

 -U <integer>
  Number of runs
 
 -A <seed>
  Random number seed
 
 -P <nr of parents>
  Maximum number of parents
 
 -R
  Use arc reversal operation.
  (default false)
 
 -N
  Initial structure is empty (instead of Naive Bayes)
 
 -mbc
  Applies a Markov Blanket correction to the network structure, 
  after a network structure is learned. This ensures that all 
  nodes in the network are part of the Markov blanket of the 
  classifier node.
 
 -S [LOO-CV|k-Fold-CV|Cumulative-CV]
  Score type (LOO-CV,k-Fold-CV,Cumulative-CV)
 
 -Q
  Use probabilistic or 0/1 scoring.
  (default probabilistic scoring)
 
Version:
$Revision: 10154 $
Author:
Remco Bouckaert (rrb@xm.co.nz)
See Also:
  • Constructor Details

    • RepeatedHillClimber

      public RepeatedHillClimber()
  • Method Details

    • getRuns

      public int getRuns()
      Returns the number of runs
      Returns:
      number of runs
    • setRuns

      public void setRuns(int nRuns)
      Sets the number of runs
      Parameters:
      nRuns - The number of runs to set
    • getSeed

      public int getSeed()
      Returns the random seed
      Returns:
      random number seed
    • setSeed

      public void setSeed(int nSeed)
      Sets the random number seed
      Parameters:
      nSeed - The number of the seed to set
    • listOptions

      public Enumeration<Option> listOptions()
      Returns an enumeration describing the available options.
      Specified by:
      listOptions in interface OptionHandler
      Overrides:
      listOptions in class HillClimber
      Returns:
      an enumeration of all the available options.
    • setOptions

      public void setOptions(String[] options) throws Exception
      Parses a given list of options.

      Valid options are:

       -U <integer>
        Number of runs
       
       -A <seed>
        Random number seed
       
       -P <nr of parents>
        Maximum number of parents
       
       -R
        Use arc reversal operation.
        (default false)
       
       -N
        Initial structure is empty (instead of Naive Bayes)
       
       -mbc
        Applies a Markov Blanket correction to the network structure, 
        after a network structure is learned. This ensures that all 
        nodes in the network are part of the Markov blanket of the 
        classifier node.
       
       -S [LOO-CV|k-Fold-CV|Cumulative-CV]
        Score type (LOO-CV,k-Fold-CV,Cumulative-CV)
       
       -Q
        Use probabilistic or 0/1 scoring.
        (default probabilistic scoring)
       
      Specified by:
      setOptions in interface OptionHandler
      Overrides:
      setOptions in class HillClimber
      Parameters:
      options - the list of options as an array of strings
      Throws:
      Exception - if an option is not supported
    • getOptions

      public String[] getOptions()
      Gets the current settings of the search algorithm.
      Specified by:
      getOptions in interface OptionHandler
      Overrides:
      getOptions in class HillClimber
      Returns:
      an array of strings suitable for passing to setOptions
    • globalInfo

      public String globalInfo()
      This will return a string describing the classifier.
      Overrides:
      globalInfo in class HillClimber
      Returns:
      The string.
    • runsTipText

      public String runsTipText()
      Returns:
      a string to describe the Runs option.
    • seedTipText

      public String seedTipText()
      Returns:
      a string to describe the Seed option.
    • getRevision

      public String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Overrides:
      getRevision in class HillClimber
      Returns:
      the revision