Package weka.datagenerators

Class ClusterGenerator

java.lang.Object
weka.datagenerators.DataGenerator
weka.datagenerators.ClusterGenerator
All Implemented Interfaces:
Serializable, OptionHandler, Randomizable, RevisionHandler
Direct Known Subclasses:
BIRCHCluster, SubspaceCluster
public abstract class ClusterGenerator extends DataGenerator
Abstract class for cluster data generators.

Example usage as the main of a datagenerator called RandomGenerator: 

 public static void main(String[] args) {
   try {
     DataGenerator.makeData(new RandomGenerator(), args);
   } catch (Exception e) {
     e.printStackTrace();
     System.err.println(e.getMessage());
   }
 }

Version:
$Revision: 15705 $
Author:
Gabi Schmidberger (gabi@cs.waikato.ac.nz), FracPete (fracpete at waikato dot ac dot nz)
See Also:

  • Constructor Details

    • ClusterGenerator

      public ClusterGenerator()
      initializes the generator

  • Method Details

    • listOptions

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

    • setOptions

      public void setOptions(String[] options) throws Exception
      Sets the options.
      Specified by:
      setOptions in interface OptionHandler
      Overrides:
      setOptions in class DataGenerator
      Parameters:
      options - the options
      Throws:
      Exception - if invalid option

    • getOptions

      public String[] getOptions()
      Gets the current settings of the classifier.
      Specified by:
      getOptions in interface OptionHandler
      Overrides:
      getOptions in class DataGenerator
      Returns:
      an array of strings suitable for passing to setOptions
      See Also:
      • DataGenerator.removeBlacklist(String[])

    • setNumAttributes

      public void setNumAttributes(int numAttributes)
      Sets the number of attributes the dataset should have.
      Parameters:
      numAttributes - the new number of attributes

    • getNumAttributes

      public int getNumAttributes()
      Gets the number of attributes that should be produced.
      Returns:
      the number of attributes that should be produced

    • numAttributesTipText

      public String numAttributesTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • setClassFlag

      public void setClassFlag(boolean classFlag)
      Sets the class flag, if class flag is set, the cluster is listed as class atrribute in an extra attribute.
      Parameters:
      classFlag - the new class flag

    • getClassFlag

      public boolean getClassFlag()
      Gets the class flag.
      Returns:
      the class flag

    • classFlagTipText

      public String classFlagTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui