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Class weka.attributeSelection.ExhaustiveSearch

java.lang.Object
   |
   +----weka.attributeSelection.ASSearch
           |
           +----weka.attributeSelection.ExhaustiveSearch

public class ExhaustiveSearch
extends ASSearch
implements StartSetHandler, OptionHandler
Class for performing an exhaustive search.

Valid options are:

-P
Specify a starting set of attributes. Eg 1,4,7-9.

-V
Verbose output. Output new best subsets as the search progresses.

Author:
Mark Hall (mhall@cs.waikato.ac.nz)

Constructor Index

 o ExhaustiveSearch()
Constructor

Method Index

 o getOptions()
Gets the current settings of RandomSearch.
 o getStartSet()
Returns a list of attributes (and or attribute ranges) as a String
 o getVerbose()
get whether or not output is verbose
 o globalInfo()
Returns a string describing this search method
 o listOptions()
Returns an enumeration describing the available options
 o search(ASEvaluation, Instances)
Searches the attribute subset space using an exhaustive search.
 o setOptions(String[])
Parses a given list of options.
 o setStartSet(String)
Sets a starting set of attributes for the search.
 o setVerbose(boolean)
set whether or not to output new best subsets as the search proceeds
 o startSetTipText()
Returns the tip text for this property
 o toString()
prints a description of the search
 o verboseTipText()
Returns the tip text for this property

Constructors

 o ExhaustiveSearch
 public ExhaustiveSearch()
Constructor

Methods

 o globalInfo
 public String globalInfo()
Returns a string describing this search method

Returns:
a description of the search suitable for displaying in the explorer/experimenter gui
 o listOptions
 public Enumeration listOptions()
Returns an enumeration describing the available options

Returns:
an enumeration of all the available options
 o setOptions
 public void setOptions(String options[]) throws Exception
Parses a given list of options. Valid options are:

-P
Specify a starting set of attributes. Eg 1,4,7-9.

-V
Verbose output. Output new best subsets as the search progresses.

Parameters:
options - the list of options as an array of strings
Throws: Exception
if an option is not supported
 o startSetTipText
 public String startSetTipText()
Returns the tip text for this property

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui
 o setStartSet
 public void setStartSet(String startSet) throws Exception
Sets a starting set of attributes for the search. It is the search method's responsibility to report this start set (if any) in its toString() method.

Parameters:
startSet - a string containing a list of attributes (and or ranges), eg. 1,2,6,10-15. "" indicates no start set. If a start point is supplied, Exhaustive search stops after finding the smallest possible subset with merit as good as or better than the start set. Otherwise, the search space is explored FULLY, and the best subset returned.
Throws: Exception
if start set can't be set.
 o getStartSet
 public String getStartSet()
Returns a list of attributes (and or attribute ranges) as a String

Returns:
a list of attributes (and or attribute ranges)
 o verboseTipText
 public String verboseTipText()
Returns the tip text for this property

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui
 o setVerbose
 public void setVerbose(boolean v)
set whether or not to output new best subsets as the search proceeds

Parameters:
v - true if output is to be verbose
 o getVerbose
 public boolean getVerbose()
get whether or not output is verbose

Returns:
true if output is set to verbose
 o getOptions
 public String[] getOptions()
Gets the current settings of RandomSearch.

Returns:
an array of strings suitable for passing to setOptions()
 o toString
 public String toString()
prints a description of the search

Returns:
a description of the search as a string
Overrides:
toString in class Object
 o search
 public int[] search(ASEvaluation ASEval,
                     Instances data) throws Exception
Searches the attribute subset space using an exhaustive search.

Parameters:
ASEvaluator - the attribute evaluator to guide the search
data - the training instances.
Returns:
an array (not necessarily ordered) of selected attribute indexes
Throws: Exception
if the search can't be completed
Overrides:
search in class ASSearch

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