content: /*
   *    This program is free software; you can redistribute it and/or modify
   *    it under the terms of the GNU General Public License as published by
   *    the Free Software Foundation; either version 2 of the License, or
   *    (at your option) any later version.
   *
   *    This program is distributed in the hope that it will be useful,
   *    but WITHOUT ANY WARRANTY; without even the implied warranty of
   *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
   *    GNU General Public License for more details.
   *
   *    You should have received a copy of the GNU General Public License
   *    along with this program; if not, write to the Free Software
   *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
   */
  
  /*
   * Agrawal.java
   * Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.datagenerators.classifiers.classification;
  
  import weka.core.Attribute;
  import weka.core.FastVector;
  import weka.core.Instance; 
  import weka.core.DenseInstance;
  import weka.core.Instances;
  import weka.core.Option;
  import weka.core.RevisionUtils;
  import weka.core.SelectedTag;
  import weka.core.Tag;
  import weka.core.TechnicalInformation;
  import weka.core.TechnicalInformationHandler;
  import weka.core.Utils;
  import weka.core.TechnicalInformation.Field;
  import weka.core.TechnicalInformation.Type;
  import weka.datagenerators.ClassificationGenerator;
  
  import java.util.Enumeration;
  import java.util.Random;
  import java.util.Vector;
  
  /**
   <!-- globalinfo-start -->
   * Generates a people database and is based on the paper by Agrawal et al.:<br/>
   * R. Agrawal, T. Imielinski, A. Swami (1993). Database Mining: A Performance Perspective. IEEE Transactions on Knowledge and Data Engineering. 5(6):914-925. URL http://www.almaden.ibm.com/software/quest/Publications/ByDate.html.
   * <p/>
   <!-- globalinfo-end -->
   *
   <!-- technical-bibtex-start -->
   * BibTeX:
   * <pre>
   * &#64;article{Agrawal1993,
   *    author = {R. Agrawal and T. Imielinski and A. Swami},
   *    journal = {IEEE Transactions on Knowledge and Data Engineering},
   *    note = {Special issue on Learning and Discovery in Knowledge-Based Databases},
   *    number = {6},
   *    pages = {914-925},
   *    title = {Database Mining: A Performance Perspective},
   *    volume = {5},
   *    year = {1993},
   *    URL = {http://www.almaden.ibm.com/software/quest/Publications/ByDate.html},
   *    PDF = {http://www.almaden.ibm.com/software/quest/Publications/papers/tkde93.pdf}
   * }
   * </pre>
   * <p/>
   <!-- technical-bibtex-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -h
   *  Prints this help.</pre>
   * 
   * <pre> -o &lt;file&gt;
   *  The name of the output file, otherwise the generated data is
   *  printed to stdout.</pre>
   * 
   * <pre> -r &lt;name&gt;
   *  The name of the relation.</pre>
   * 
   * <pre> -d
   *  Whether to print debug informations.</pre>
   * 
   * <pre> -S
   *  The seed for random function (default 1)</pre>
   * 
   * <pre> -n &lt;num&gt;
   *  The number of examples to generate (default 100)</pre>
   * 
   * <pre> -F &lt;num&gt;
   *  The function to use for generating the data. (default 1)</pre>
   * 
   * <pre> -B
   *  Whether to balance the class.</pre>
   * 
   * <pre> -P &lt;num&gt;
   *  The perturbation factor. (default 0.05)</pre>
   * 
   <!-- options-end -->
   *
   * @author Richard Kirkby (rkirkby at cs dot waikato dot ac dot nz)
   * @author FracPete (fracpete at waikato dot ac dot nz)
   * @version $Revision: 5987 $
   */
  
  public class Agrawal
    extends ClassificationGenerator
    implements TechnicalInformationHandler {
    
    /** for serialization */
    static final long serialVersionUID = 2254651939636143025L;
    
    /**
     * the interface for the class functions
     */
    protected interface ClassFunction {
      /**
       * returns a class value based on the given inputs
       * @param salary the salary
       * @param commission the commission
       * @param age the age
       * @param elevel the education level
       * @param car 
       * @param zipcode the zip code
       * @param hvalue
       * @param hyears
       * @param loan
       */
      public long determineClass(double salary, double commission, int age,
          int elevel, int car, int zipcode, double hvalue, int hyears,
          double loan);
    }
  
    /** 
     * built in functions are based on the paper (page 924),
     * which turn out to be functions pred20 thru pred29 in the public c code
     */
    protected static ClassFunction[] builtInFunctions = {
      // function 1
      new ClassFunction() {
        public long determineClass(double salary, double commission,
            int age, int elevel, int car, int zipcode,
            double hvalue, int hyears, double loan) {
          if (age < 40 || 60 <= age)
            return 0;
          else
            return 1;
        }
      },
      // function 2
      new ClassFunction() {
        public long determineClass(double salary, double commission,
            int age, int elevel, int car, int zipcode,
            double hvalue, int hyears, double loan) {
          if (age < 40)
            if (50000 <= salary && salary <= 100000)
              return 0;
            else
              return 1;
          else if (age < 60) // && age >= 40
            if (75000 <= salary && salary <= 125000)
              return 0;
            else
              return 1;
          else // age >= 60
            if (25000 <= salary && salary <= 75000)
              return 0;
            else
              return 1;
        }
      },
      // function 3
      new ClassFunction() {
        public long determineClass(double salary, double commission,
            int age, int elevel, int car, int zipcode,
            double hvalue, int hyears, double loan) {
          if (age < 40)
            if (elevel == 0 || elevel == 1)
              return 0;
            else
              return 1;
          else if (age < 60) // && age >= 40
            if (elevel == 1 || elevel == 2 || elevel == 3)
              return 0;
            else
              return 1;
          else // age >= 60
            if (elevel == 2 || elevel == 3 || elevel == 4)
              return 0;
            else
              return 1;
        }
      },
      // function 4
      new ClassFunction() {
        public long determineClass(double salary, double commission,
            int age, int elevel, int car, int zipcode,
            double hvalue, int hyears, double loan) {
          if (age < 40)
            if (elevel == 0 || elevel == 1)
              if (25000 <= salary && salary <= 75000)
                return 0;
              else
                return 1;
            else if (50000 <= salary && salary <= 100000)
              return 0;
            else
              return 1;
          else if (age < 60) // && age >= 40
            if (elevel == 1 || elevel == 2 || elevel == 3)
              if (50000 <= salary && salary <= 100000)
                return 0;
              else
                return 1;
            else if (75000 <= salary && salary <= 125000)
              return 0;
            else
              return 1;
          else // age >= 60
            if (elevel == 2 || elevel == 3 || elevel == 4)
              if (50000 <= salary && salary <= 100000)
                return 0;
              else
                return 1;
            else if (25000 <= salary && salary <= 75000)
              return 0;
            else
              return 1;
        }
      },
      // function 5
      new ClassFunction() {
        public long determineClass(double salary, double commission,
            int age, int elevel, int car, int zipcode,
            double hvalue, int hyears, double loan) {
          if (age < 40)
            if (50000 <= salary && salary <= 100000)
              if (100000 <= loan && loan <= 300000)
                return 0;
              else
                return 1;
            else if (200000 <= loan && loan <= 400000)
              return 0;
            else
              return 1;
          else if (age < 60) // && age >= 40
            if (75000 <= salary && salary <= 125000)
              if (200000 <= loan && loan <= 400000)
                return 0;
              else
                return 1;
            else if (300000 <= loan && loan <= 500000)
              return 0;
            else
              return 1;
          else // age >= 60
            if (25000 <= salary && salary <= 75000)
              if (300000 <= loan && loan <= 500000)
                return 0;
              else
                return 1;
            else if (100000 <= loan && loan <= 300000)
              return 0;
            else
              return 1;
        }
      },
      // function 6
      new ClassFunction() {
        public long determineClass(double salary, double commission,
            int age, int elevel, int car, int zipcode,
            double hvalue, int hyears, double loan) {
          double totalSalary = salary + commission;
          if (age < 40)
            if (50000 <= totalSalary && totalSalary <= 100000)
              return 0;
            else
              return 1;
          else if (age < 60) // && age >= 40
            if (75000 <= totalSalary && totalSalary <= 125000)
              return 0;
            else
              return 1;
          else // age >= 60
            if (25000 <= totalSalary && totalSalary <= 75000)
              return 0;
            else
              return 1;
        }
      },
      // function 7
      new ClassFunction() {
        public long determineClass(double salary, double commission,
            int age, int elevel, int car, int zipcode,
            double hvalue, int hyears, double loan) {
          double disposable = (2.0 * (salary + commission) / 3.0
              - loan / 5.0 - 20000.0);
          return disposable > 0 ? 0 : 1;
        }
      },
      // function 8
      new ClassFunction() {
        public long determineClass(double salary, double commission,
            int age, int elevel, int car, int zipcode,
            double hvalue, int hyears, double loan) {
          double disposable = (2.0 * (salary + commission) / 3.0
              - 5000.0 * (double) elevel - 20000.0);
          return disposable > 0 ? 0 : 1;
        }
      },
      // function 9
      new ClassFunction() {
        public long determineClass(double salary, double commission,
            int age, int elevel, int car, int zipcode,
            double hvalue, int hyears, double loan) {
          double disposable = (2.0 * (salary + commission) / 3.0
              - 5000.0 * (double) elevel - loan / 5.0 - 10000.0);
          return disposable > 0 ? 0 : 1;
        }
      },
      // function 10
      new ClassFunction() {
        public long determineClass(double salary, double commission,
            int age, int elevel, int car, int zipcode,
            double hvalue, int hyears, double loan) {
          double equity = 0.0;
          if (hyears >= 20)
            equity = hvalue * ((double) hyears - 20.0) / 10.0;
          double disposable = (2.0 * (salary + commission) / 3.0
              - 5000.0 * (double) elevel + equity / 5.0 - 10000.0);
          return disposable > 0 ? 0 : 1;
        }
      } 
    };
  
    /** function 1 */
    public final static int FUNCTION_1 = 1;
    /** function 2 */
    public final static int FUNCTION_2 = 2;
    /** function 3 */
    public final static int FUNCTION_3 = 3;
    /** function 4 */
    public final static int FUNCTION_4 = 4;
    /** function 5 */
    public final static int FUNCTION_5 = 5;
    /** function 6 */
    public final static int FUNCTION_6 = 6;
    /** function 7 */
    public final static int FUNCTION_7 = 7;
    /** function 8 */
    public final static int FUNCTION_8 = 8;
    /** function 9 */
    public final static int FUNCTION_9 = 9;
    /** function 10 */
    public final static int FUNCTION_10 = 10;
    /** the funtion tags */
    public static final Tag[] FUNCTION_TAGS = {
      new Tag(FUNCTION_1,  "Function 1"),
      new Tag(FUNCTION_2,  "Function 2"),
      new Tag(FUNCTION_3,  "Function 3"),
      new Tag(FUNCTION_4,  "Function 4"),
      new Tag(FUNCTION_5,  "Function 5"),
      new Tag(FUNCTION_6,  "Function 6"),
      new Tag(FUNCTION_7,  "Function 7"),
      new Tag(FUNCTION_8,  "Function 8"),
      new Tag(FUNCTION_9,  "Function 9"),
      new Tag(FUNCTION_10, "Function 10"),
    };
    
    /** the function to use for generating the data */
    protected int m_Function;
  
    /** whether to balance the class */
    protected boolean m_BalanceClass;
  
    /** the perturabation fraction */
    protected double m_PerturbationFraction;
    
    /** used for balancing the class */
    protected boolean m_nextClassShouldBeZero;
  
    /** the last class label that was generated */
    protected double m_lastLabel;
    
    /**
     * initializes the generator with default values
     */
    public Agrawal() {
      super();
  
      setFunction(defaultFunction());
      setBalanceClass(defaultBalanceClass());
      setPerturbationFraction(defaultPerturbationFraction());
    }
  
    /**
     * Returns a string describing this data generator.
     *
     * @return a description of the data generator suitable for
     * displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
      return 
           "Generates a people database and is based on the paper by Agrawal "
         + "et al.:\n"
         + getTechnicalInformation().toString();
    }
  
    /**
     * Returns an instance of a TechnicalInformation object, containing 
     * detailed information about the technical background of this class,
     * e.g., paper reference or book this class is based on.
     * 
     * @return the technical information about this class
     */
    public TechnicalInformation getTechnicalInformation() {
      TechnicalInformation 	result;
      
      result = new TechnicalInformation(Type.ARTICLE);
      result.setValue(Field.AUTHOR, "R. Agrawal and T. Imielinski and A. Swami");
      result.setValue(Field.YEAR, "1993");
      result.setValue(Field.TITLE, "Database Mining: A Performance Perspective");
      result.setValue(Field.JOURNAL, "IEEE Transactions on Knowledge and Data Engineering");
      result.setValue(Field.VOLUME, "5");
      result.setValue(Field.NUMBER, "6");
      result.setValue(Field.PAGES, "914-925");
      result.setValue(Field.NOTE, "Special issue on Learning and Discovery in Knowledge-Based Databases");
      result.setValue(Field.URL, "http://www.almaden.ibm.com/software/quest/Publications/ByDate.html");
      result.setValue(Field.PDF, "http://www.almaden.ibm.com/software/quest/Publications/papers/tkde93.pdf");
      
      return result;
    }
  
   /**
     * Returns an enumeration describing the available options.
     *
     * @return an enumeration of all the available options
     */
    public Enumeration listOptions() {
      Vector result = enumToVector(super.listOptions());
  
      result.add(new Option(
                "\tThe function to use for generating the data. (default " 
                + defaultFunction().getSelectedTag().getID() + ")",
                "F", 1, "-F <num>"));
  
      result.add(new Option(
                "\tWhether to balance the class.",
                "B", 0, "-B"));
  
      result.add(new Option(
                "\tThe perturbation factor. (default " 
                + defaultPerturbationFraction() + ")",
                "P", 1, "-P <num>"));
  
      return result.elements();
    }
  
    /**
     * Parses a list of options for this object. <p/>
     *
     <!-- options-start -->
     * Valid options are: <p/>
     * 
     * <pre> -h
     *  Prints this help.</pre>
     * 
     * <pre> -o &lt;file&gt;
     *  The name of the output file, otherwise the generated data is
     *  printed to stdout.</pre>
     * 
     * <pre> -r &lt;name&gt;
     *  The name of the relation.</pre>
     * 
     * <pre> -d
     *  Whether to print debug informations.</pre>
     * 
     * <pre> -S
     *  The seed for random function (default 1)</pre>
     * 
     * <pre> -n &lt;num&gt;
     *  The number of examples to generate (default 100)</pre>
     * 
     * <pre> -F &lt;num&gt;
     *  The function to use for generating the data. (default 1)</pre>
     * 
     * <pre> -B
     *  Whether to balance the class.</pre>
     * 
     * <pre> -P &lt;num&gt;
     *  The perturbation factor. (default 0.05)</pre>
     * 
     <!-- options-end -->
     *
     * @param options the list of options as an array of strings
     * @throws Exception if an option is not supported
     */
    public void setOptions(String[] options) throws Exception {
      String        tmpStr;
  
      super.setOptions(options);
  
      tmpStr = Utils.getOption('F', options);
      if (tmpStr.length() != 0)
        setFunction(new SelectedTag(Integer.parseInt(tmpStr), FUNCTION_TAGS));
      else
        setFunction(defaultFunction());
  
      setBalanceClass(Utils.getFlag('B', options));
  
      tmpStr = Utils.getOption('P', options);
      if (tmpStr.length() != 0)
        setPerturbationFraction(Double.parseDouble(tmpStr));
      else
        setPerturbationFraction(defaultPerturbationFraction());
    }
  
    /**
     * Gets the current settings of the datagenerator.
     *
     * @return an array of strings suitable for passing to setOptions
     */
    public String[] getOptions() {
      Vector        result;
      String[]      options;
      int           i;
      
      result  = new Vector();
      options = super.getOptions();
      for (i = 0; i < options.length; i++)
        result.add(options[i]);
      
      result.add("-F");
      result.add("" + m_Function);
      
      if (getBalanceClass())
        result.add("-B");
      
      result.add("-P");
      result.add("" + getPerturbationFraction());
      
      return (String[]) result.toArray(new String[result.size()]);
    }
  
    /**
     * returns the default function
     * 
     * @return the default function
     */
    protected SelectedTag defaultFunction() {
      return new SelectedTag(FUNCTION_1, FUNCTION_TAGS);
    }
    
    /**
     * Gets the function for generating the data.
     *
     * @return the function.
     * @see #FUNCTION_TAGS
     */
    public SelectedTag getFunction() {
      return new SelectedTag(m_Function, FUNCTION_TAGS);
    }
    
    /**
     * Sets the function for generating the data.
     *
     * @param value the function.
     * @see #FUNCTION_TAGS
     */
    public void setFunction(SelectedTag value) {
      if (value.getTags() == FUNCTION_TAGS)
        m_Function = value.getSelectedTag().getID();
    }
    
    /**
     * Returns the tip text for this property
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String functionTipText() {
      return "The function to use for generating the data.";
    }
  
    /**
     * returns the default for balancing the class
     * 
     * @return the default for balancing the class
     */
    protected boolean defaultBalanceClass() {
      return false;
    }
    
    /**
     * Gets whether the class is balanced.
     *
     * @return whether the class is balanced.
     */
    public boolean getBalanceClass() { 
      return m_BalanceClass; 
    }
    
    /**
     * Sets whether the class is balanced.
     *
     * @param value whether to balance the class.
     */
    public void setBalanceClass(boolean value) { 
      m_BalanceClass = value;
    }  
    
    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for
     *         displaying in the explorer/experimenter gui
     */
    public String balanceClassTipText() {
      return "Whether to balance the class.";
    }
  
    /**
     * returns the default perturbation fraction
     * 
     * @return the default perturbation fraction
     */
    protected double defaultPerturbationFraction() {
      return 0.05;
    }
    
    /**
     * Gets the perturbation fraction.
     *
     * @return the perturbation fraction.
     */
    public double getPerturbationFraction() { 
      return m_PerturbationFraction; 
    }
    
    /**
     * Sets the perturbation fraction.
     *
     * @param value the perturbation fraction.
     */
    public void setPerturbationFraction(double value) { 
      if ( (value >= 0.0) && (value <= 1.0) )
        m_PerturbationFraction = value;
      else
        throw new IllegalArgumentException(
            "Perturbation fraction must be in [0,1] (provided: " + value + ")!");
    }  
    
    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for
     *         displaying in the explorer/experimenter gui
     */
    public String perturbationFractionTipText() {
      return "The perturbation fraction: 0 <= fraction <= 1.";
    }
  
    /**
     * Return if single mode is set for the given data generator
     * mode depends on option setting and or generator type.
     * 
     * @return single mode flag
     * @throws Exception if mode is not set yet
     */
    public boolean getSingleModeFlag() throws Exception {
      return true;
    }
  
    /**
     * Initializes the format for the dataset produced. 
     * Must be called before the generateExample or generateExamples
     * methods are used.
     * Re-initializes the random number generator with the given seed.
     *
     * @return the format for the dataset 
     * @throws Exception if the generating of the format failed
     * @see  #getSeed()
     */
    public Instances defineDataFormat() throws Exception {
      FastVector      atts;
      FastVector      attValues;
      int             i;
  
      m_Random = new Random(getSeed());
      m_nextClassShouldBeZero = true;
      m_lastLabel             = Double.NaN;
  
      // number of examples is the same as given per option
      setNumExamplesAct(getNumExamples());
  
      // set up attributes
      atts = new FastVector();
      
      atts.addElement(new Attribute("salary"));
      
      atts.addElement(new Attribute("commission"));
      
      attValues = new FastVector();
      atts.addElement(new Attribute("age"));
  
      attValues = new FastVector();
      for (i = 0; i < 5; i++)
        attValues.addElement("" + i);
      atts.addElement(new Attribute("elevel", attValues));
      
      attValues = new FastVector();
      for (i = 1; i <= 20; i++)
        attValues.addElement("" + i);
      atts.addElement(new Attribute("car", attValues));
      
      attValues = new FastVector();
      for (i = 0; i < 9; i++)
        attValues.addElement("" + i);
      atts.addElement(new Attribute("zipcode", attValues));
      
      atts.addElement(new Attribute("hvalue"));
      
      atts.addElement(new Attribute("hyears"));
      
      atts.addElement(new Attribute("loan"));
      
      attValues = new FastVector();
      for (i = 0; i < 2; i++)
        attValues.addElement("" + i);
      atts.addElement(new Attribute("group", attValues));
      
      // dataset
      m_DatasetFormat = new Instances(getRelationNameToUse(), atts, 0);
      
      return m_DatasetFormat;
    }
  
    /**
     * perturbs the given value
     * 
     * @param val the value to perturb
     * @param min the minimum
     * @param max the maximum
     * @return the perturbed value
     */
    protected double perturbValue(double val, double min, double max) {
      return perturbValue(val, max - min, min, max);
    }
  
    /**
     * perturbs the given value
     * 
     * @param val the value to perturb
     * @param range the range for the perturbation
     * @param min the minimum
     * @param max the maximum
     * @return the perturbed value
     */
    protected double perturbValue(double val, double range, 
        double min, double max) {
      
      val += range * (2.0 * (getRandom().nextDouble() - 0.5)) 
                   * getPerturbationFraction();
  
      if (val < min)
        val = min;
      else if (val > max)
        val = max;
      
      return val;
    }
  
    /**
     * Generates one example of the dataset. 
     *
     * @return the generated example
     * @throws Exception if the format of the dataset is not yet defined
     * @throws Exception if the generator only works with generateExamples
     * which means in non single mode
     */
    public Instance generateExample() throws Exception {
      Instance      result;
      double        salary;
      double        commission;
      double        hvalue;
      double        loan;
      int           age;
      int           elevel;
      int           car;
      int           zipcode;
      int           hyears;
      boolean       desiredClassFound;
      double[]      atts;
      Random        random;
      ClassFunction classFunction;
  
      result = null;
      random = getRandom();
  
      if (m_DatasetFormat == null)
        throw new Exception("Dataset format not defined.");
  
      salary            = 0;
      commission        = 0;
      hvalue            = 0;
      loan              = 0;
      age               = 0;
      elevel            = 0;
      car               = 0;
      zipcode           = 0;
      hyears            = 0;
      desiredClassFound = false;
      classFunction     = builtInFunctions[m_Function - 1];
  
      while (!desiredClassFound) {
        // generate attributes
        salary     = 20000.0 + 130000.0 * random.nextDouble();
        commission = (salary >= 75000.0) ? 
                     0 : (10000.0 + 65000.0 * random.nextDouble());
        age        = 20 + random.nextInt(61);
        elevel     = random.nextInt(5);
        car        = 1 + random.nextInt(20);
        zipcode    = random.nextInt(9);
        hvalue     = (9.0 - (double) zipcode) * 100000.0
                     * (0.5 + random.nextDouble());
        hyears     = 1 + random.nextInt(30);
        loan       = random.nextDouble() * 500000.0;
        
        // determine class
        m_lastLabel = classFunction.determineClass(salary, commission, age,
            elevel, car, zipcode, hvalue, hyears, loan);
        if (!getBalanceClass()) {
          desiredClassFound = true;
        }
        else {
          // balance the classes
          if (    ( m_nextClassShouldBeZero && (m_lastLabel == 0))
               || (!m_nextClassShouldBeZero && (m_lastLabel == 1)) ) {
            desiredClassFound = true;
            m_nextClassShouldBeZero = !m_nextClassShouldBeZero;
          } // else keep searching
        }
      }
      
      // perturb values
      if (getPerturbationFraction() > 0.0) {
        salary = perturbValue(salary, 20000, 150000);
        if (commission > 0)
          commission = perturbValue(commission, 10000, 75000);
        age    = (int) Math.round(perturbValue(age, 20, 80));
        hvalue = perturbValue(
                    hvalue, (9.0 - (double) zipcode) * 100000.0, 0, 135000);
        hyears = (int) Math.round(perturbValue(hyears, 1, 30));
        loan   = perturbValue(loan, 0, 500000);
      }
  
      // create instance
      atts    = new double[m_DatasetFormat.numAttributes()];
      atts[0] = salary;
      atts[1] = commission;
      atts[2] = age;
      atts[3] = elevel;
      atts[4] = car - 1;
      atts[5] = zipcode;
      atts[6] = hvalue;
      atts[7] = hyears;
      atts[8] = loan;
      atts[9] = m_lastLabel;
      result  = new DenseInstance(1.0, atts);
      result.setDataset(m_DatasetFormat);
  
      return result;
    }
  
    /**
     * Generates all examples of the dataset. Re-initializes the random number
     * generator with the given seed, before generating instances.
     *
     * @return the generated dataset
     * @throws Exception if the format of the dataset is not yet defined
     * @throws Exception if the generator only works with generateExample,
     * which means in single mode
     * @see   #getSeed()
     */
    public Instances generateExamples() throws Exception {
      Instances       result;
      int             i;
  
      result   = new Instances(m_DatasetFormat, 0);
      m_Random = new Random(getSeed());
  
      for (i = 0; i < getNumExamplesAct(); i++)
        result.add(generateExample());
      
      return result;
    }
  
    /**
     * Generates a comment string that documentates the data generator.
     * By default this string is added at the beginning of the produced output
     * as ARFF file type, next after the options.
     * 
     * @return string contains info about the generated rules
     */
    public String generateStart () {
      return "";
    }
  
    /**
     * Generates a comment string that documentats the data generator.
     * By default this string is added at the end of theproduces output
     * as ARFF file type.
     * 
     * @return string contains info about the generated rules
     * @throws Exception if the generating of the documentaion fails
     */
    public String generateFinished() throws Exception {
      return "";
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 5987 $");
    }
  
    /**
     * Main method for executing this class.
     *
     * @param args should contain arguments for the data producer: 
     */
    public static void main(String[] args) {
      runDataGenerator(new Agrawal(), args);
    }
  }
  